Argument Mapping for Scientific Articles Introduction What are they? Argument maps are visual tools that allow us to see the steps that take an argument from the premises or evidence to the conclusions they support. There are many ways to create argument maps and they have been implemented in many different ways for different purposes. Here we use them to understand the structure of a scientific argument. Therefore, in this document I use the term “argument map” to mean “argument map for empirical, scientific, or academic arguments” . The following diagram shows the general structure of an argument map the way we use them. The most important aspect of the tree structure above is that its branches end in “data” and “evidence” . This is what differentiates a scientific (empirical) argument from other types of arguments. A scientific argument relies on data and empirical evidence for its validity. Data is the ultimate arbiter. Sometimes we rely on others who have provided empirical evidence to support a claim. In such cases a claim is supported by a “reference” to other scholars. It is important to remember that a scientific argument is not valid just because a scientist has said so. What are they useful for? For our purposes, argument maps help us understand the reasoning of researchers and figure out areas where they may be wrong. By discovering error in other researchers arguments, we have a chance to create new arguments that lead to new conclusions and discoveries. This process of detecting errors and improving on previous work is an essential core of scientific research and scientific progress. Science is a collaborative and constructive endeavor, where previous discoveries and arguments build the path for future ones. It is important to make sure that we build scientific knowledge that is true, accurate, and as error-free as possible. Argument maps can help us achieve this goal. Argument mapping is also extremely helpful in structuring our own thoughts and making our own arguments more clear for other researchers. If you are interested in writing a scholarly piece, you can use an argument map to structure your thoughts and create a blueprint of what you need to write. Then you can expand on that map to create the actual sections and subsections of your paper. Finally you add the paragraphs that create the smooth transitions from each claim to the next, connecting your data/evidence to your main claim/conclusion. Why should I care? Not every argument made by a scientist or researcher is equally valid. Some arguments are more erroneous than others. Errors can seep into our reasoning from various sources. It might be the way we collected our evidence. It might be the way we defined our theoretical concepts. Or it might be that we did not think of alternative explanations. Sometimes such errors are harmless, and we or others can catch and correct them if need be. However, sometimes such errors are extremely harmful. An example of a terribly harmful case is the research that claimed a link between the MMR vaccine and autism. The study was erroneous in many many ways and has caused damage at an extremely large and international scale. If you would like to know more, this article provides a relatively good summary of its problems. You can read the original report on all the errors in that study here. The bottom line is that we need to make sure we have a way to spot errors in our arguments and reasoning if we want to avoid causing harm to ourselves and others. Making sure we don’t have errors in our reasoning and arguments is in some ways similar to housecleaning and maintenance. We need to know the structure of the house and what needs repair or cleaning. This is what argument maps can do for our arguments and thoughts. But we should also know that it is impossible to have everything 100% clean and repaired all the time. Some things are more important to be clean and functioning than others. It depends on how they affect us and what our limitations are. So it is important to do our knowledge maintenance in a way that satisfies our goals. Finally, similar to an unclean house, the cause of reasoning full of errors is often laziness. If we put the time and care into how we think, we can make sure that we remove the harmful errors and make life easier for ourselves and others. Research on the Role of Argument Maps in Improving Analytical Reasoning There is growing research showing argument maps help the development of analytical and critical thinking. Here is a recent study: Cullen, Fan, van der Brugge & Elga (2018): Improving analytical reasoning and argument understanding: a quasi-experimental field study of argument visualization. npj Science of Learning. 3: 21. Elements of an Argument Map What elements you use to construct your argument map depends on the type of argument and what your goal is for argument mapping. Here I present a list of common and useful elements for mapping arguments in scientific studies. Data Every scientific paper has a way of presenting you with the data that they collected. This is often in the form. of a graph or summary statistics such as percentages or means. A lot can happen at this stage to result in erroneous inference. Interpretation of Data The same graph or pattern of data may receive different interpretations. It is important to understand how the authors of a paper interpret their data and what are possible alternative interpretations of what they found. It is common in scientific studies to miss alternative interpretations at this stage and reach conclusions that do not necessarily follow. (Supporting) Claims Claims are propositions that the authors are committed to, so that they can conclude the main point or the main claim. Claims can provide support for each other and clarify the chain of reasoning in an argument. Main Claim There is often a main claim or a few main claims in a scientific paper. The main claim of a paper is the conclusion or culmination of its arguments. For it to be valid, all the prior steps that lead to it must be valid. The process of making a scientific argument is difficult precisely because inferential errors can appear at any step that leads to the main conclusion. An argument map helps us understand the steps that lead to a main conclusion and makes tracking the sources of errors easier. References Sometimes authors do not provide data and evidence to support a particular claim but rather refer to other researchers who have done so. We can include the references in our argument map underneath the claim that they support. Definitions As we create argument maps, we often notice that whether an argument is valid or not crucially depends on precise definitions of some concepts or theoretical constructs. We can include the definitions that the authors provide, perhaps as a footnote to our argument map. Assumptions Sometimes we notice that the authors have implicit or explicit assumptions that are critical for the validity of the argument. We can include these assumptions as well and keep track of them. If we believe that these assumptions do not hold, then the argument is not going to be valid and we need to find a way to address the issue and improve the argument. Objections Sometimes we can find problems with the reasoning that the authors provide. We can also add those problems as “objections” to the argument map so that we remember where the errors were and where improvements need to happen. A worked-out example Take a look at the following scientific article: Suzuki, Wheatcroft, & Griesser (2016). Experimental evidence for compositional syntax in bird calls. Nature Communications The diagram is a small argument map I made for the scientific paper above: At the lowest level we have the graphs (presentation of data) that the paper provides for its arguments. Higher up I have summarized how the authors interpreted the data they collected. Higher up I have shown how those interpretations connect to two theoretical constructs: “compositional” interpretation and “sequential” interpretation. The authors have argued for compositional calls in Parus Minor by showing that: 1) it is compositional and 2) it is not sequential. Finally at the top of the map we have their main claims: that compositionality is not unique to humans. At this point, the argument map makes one issue clear. The theoretical constructs “sequential” and “compositional” interpretation are central to the arguments of the paper. Therefore, the paper needs to provide a clear definition of these concepts. I have included the definition that the paper implicitly alludes to below. I have also included an objection to this definition provided by a linguist, Mark Liberman. ● DEFINITION: combination of symbols like A+B can be interpreted compositionally or sequentially. In sequential interpretation, every ordering of symbols is interpretable. This is not the case in rule-based interpretation. Only symbol combinations that have corresponding rules are interpretable. ○ OBJECTION: Even in sequential interpretation, a sequence might not be interpretable due to pragmatic reasons. (Mark Liberman, language log post http://languagelog.ldc.upenn.edu/nll/?p=24561) Take a look at the argument map above again. What types of errors can we expect at each level of the argument? How important are they for the purpose of the argument? How can we address these errors? Research in the News Take a look at the following news articles reporting on the original study you just read. 1. Syntax is not unique to humans! (phys.org) 2. Japanese great tits use syntax to communicate – just like humans (IBT) 3. Birds have syntax just like humans do 4. Great Tits Use Linguistic Traits Including Phrases Thought To Be Unique To Humans 5. Good Grammar Is a Matter of Life or Death for Japanese Tits Discuss how accurately these news articles reflect the true content of the research article? What are the consequences of representing scientific research inaccurately? How can we make sure we are not contributing to the spread of misinformation?
Tutorial and Laboratories 11464 - 11524G AR/VR for Data Analysis and Communication Basics of Data Visualisation - Week 1 Introduction In this tutorial will continue practising basic operations in R. In particular, you will learn about visualising data in R; how to create plots, choosing a plot type and basics of data visualisation. This is a basic introduction to some of the basic plotting commands. It is assumed that you are now familiar with different data types, how to subset data from vectors and data frames, and how to enter data or read data files which was covered in our previous tutorial. It is recommended to complete the tutorial from week 3 before attempting this tutorial. Skills Covered in this tutorial include: • Plotting in Rstudio • Basics of data visualisation • Using different plot types Note: Do not copy-paste the commands. As you type each line, you will make mistakes and correct them, which make you think as you go along. Remember, that the objective is that you understand the commands and master the concepts, so you can reproduce their principles on your own later. 1. Plotting in RStudio One of the most powerful features of R is its capability to produce a different range/style of graphics to visualise data quickly and easily. When a plot is generated in Rstudio, the plots are displayed in the built-in plotting window in the bottom-right pane (please refer to the tutorial in Week 2). This window has different options, and it is presented in Figure 1. Figure 1. The plotting window in RStudio. 1) Navigate through all the plotting history. 2) The plot is displayed in a larger window. 3) The image is exported as image of FPF format. 4) remove the current plot from the plotting history. 5) clear all plotting history. In this tutorial, we will use two different datasets (w1 and tree), which can be found in Canvas in the Week 4 module. You can read the data as follows: # read data w1
Tutorial EG501V Computational Fluid Dynamics (AY 2023/24) Tutorial 6. Navier-Stokes, pressure correction – With solutions Velocity, pressure, distance etc. are all dimensionless in this tutorial. The discrete version of the pressure-correction equation reads (LN07, Eq. 7.8) Consider the situation sketched in the figure which represents two-dimensional flow in a channel with a solid wall as the lower boundary, constant (and known) pressure at the left boundary, a symmetry condition at the upper boundary, and zero-gradient at the right boundary. In terms of the pressure correction π :∂ ∂ = π n 0 on all boundaries except for the left boundary which has π = 0. Further given the preliminary velocity values as in the figure, determine the matrix-vector system [A] = that needs to be solved in order to determine (which is the vector containing the 9 pressure correction values in the same order as numbered in the figure). Further given: hx = 1, hy = 0.5, ρ/∆t = 1.
EC223 A1 Statistical analysis - Spring 2025 - Department of Economics Course description: This is an introductory mathematical statistics course, covering probability theory, statistical inference, and an introduction to regression analysis. The course aims at providing students with the necessary background to progress to higher level econometrics and applied economics courses. Effective Fall 2023, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning I, Critical Thinking. Prerequisite courses: CAS EC101, CAS EC102, CAS MA225 The required software: Stata is a statistical software product popular with economists and financial experts. I recommend purchasing your own Stata license through the BU information technology department (check their website at https://www.bu.edu/casit/information/purchasing-software/). It will re-direct you to the Stata website, where you will see different options. I recommend the $145 Stata/BE for one year (especially if you plan to take ec224 next semester). Instead of purchasing your own Stata software, students can utilize the computers in the BU library and in CAS 327 at 685 Commonwealth Avenue. Student ID card access can be requested (students can set up the card for access in CAS 331 between 9 am – 5 pm. The cards will give access between 8 am – 10 pm, 7 days a week, and we ask students to be aware of the room availability as it is also used for lectures and lab sections. The schedule of classes in CAS 327 is posted on their door as well as online at http://www.bu.edu/casit/computer-labs/. The building doors are typically open until 11 pm most weekday evenings. The required textbook: “ Mathematical Statistics With Applications,” by Jay Devore, Kenneth Berk, Matthew Carlton. Springer Publisher, 3rd edition, 2021. It is available as a free file in a .pdf format online: https://link.springer.com/content/pdf/10.1007/978-3-030-55156-8.pdf The reference textbook for the course is Statistics for Business and Economics by Newbold, Carlson, Thorne. “ Mathematical Statistics With Applications,” Pearson Publisher, 9th edition, 2023. NOT required. Why is it useful? It provides an exceptionally clear explanation of the major concepts in probability and mathematical statistics without using calculus. Great for those who would like to understand mathematics not only at the formal level, but with their heart. Various versions are available (unfortunately, not for free) via the Barnes & Noble bookstore on campus. Blackboard: All the materials from the course will be posted on the blackboard course site. The announcements will be sent via blackboard email – so please check it regularly. It is the students’ responsibility to keep up with the course requirements (i.e., you will need to go through all the course materials on blackboard, as well as keep pace with online quizzes and assignments). Please note that I do not plan to record lectures on the regular basis. If you cannot keep pace with the course material, please contact me immediately so that we can resolve any potential issues. Grading: I’ll base the course grade on students’ scores on: 1. Two midterm exams (20% of the final grade each) 2. Final exam (20% of the final grade) 3. Homework & Stata Assignments (15% of the final grade; online, approximately each week) 4. Short Quizzes (10% of the final grade; online, approximately each other week; the lowest- score quiz will be dropped) 5. Empirical Team Project (15% of the final grade) Built-in grade flexibility: Active participation in class is encouraged and rewarded, such as asking interesting questions related to the material and answering my questions in class. If a student exhibits a very active participation in class, the participation score will be weighed higher in the final grade. Note on missed midterm exams: There will be no makeup exam for the midterm exams. If you miss a midterm, then the points for the missed exam will be automatically added to your final exam. If a student misses the final exam, I must be contacted on the day of the exam and every effort must be made to take the makeup exam as soon as possible, to avoid an incomplete grade in the course. Exams will be given in class in person unless otherwise indicated. Preparing for the Exams: The structure of knowledge in mathematical statistics is strongly hierarchic in that each successive lecture tends to build on prior material in a rather systematic fashion. As such it is very easy to fall behind if you miss a class and do not study the missed material before the subsequent lecture. All exams will be based on questions drawn from the material covered in the textbook, lectures, and problem sets (including the assigned homework problems and in-class Stata assignments). In other words, all material associated with the course may appear on exams, including lecture material that is not in the textbook (please note that all supplemental materials will be posted on blackboard). Quizzes (online format): Short online quizzes are 20-minute tests based on the recently covered material only and formatted as multiple choice and true/false questions. The links to quizzes are under the respective week learning module (folder) on blackboard course site. Read the instructions carefully before taking the quiz. Quizzes are to be submitted via blackboard link online. Late submissions will not be graded (resulting in a zero score for the late quiz). The due date will be clearly indicated for each assignment; the deadline is firm. Late submissions will not be graded. Once again: absolutely no make-ups for the missed quizzes. However, the lowest-score quiz will be dropped. So do not worry if you happened to miss one quiz – your quiz grade will not be affected in case if you get sick. Homework Assignments (problem sets; online format): It is encouraged that students work together on the homework assignments because better learning of the material usually occurs through student discussion and interaction. Homework assignments will be posted on the course site and will require the on-line submission by the end of a due date (indicated in the assignment link). The online format of the homework assignments will be like that of the quizzes. The only difference is that the homework assignment will not have a time limit (i.e., you do not need to complete the assignment in one setting within 20 minutes) and there will be the unlimited number of attempts (only two in quizzes, with the second attempts only for technical issues during the first attempt). Homework assignments are to be submitted via blackboard assignment online. Late assignments will not be graded (due date will be clearly indicated for each assignment). Once more: the deadline is firm. Late assignments will not be graded. NO extra projects for the missed homework will be given. Empirical homework: Stata Assignments (print-out format): Stata assignments will be posted on the course site, following Stata session in class (please feel free to seek TF’s help on all your Stata assignments; cooperation with classmates is also encouraged). Just like with the theoretical homework assignments, late Stata assignments will not be graded (due date will be clearly indicated for each assignment). Once more: the deadline is firm. Late Stata assignments will not be graded. Final Project: The individual empirical projects will be an important part of the course, that will be built on the empirical assignments in Stata and will allow the students to fill the gap between the statistical theory and applied data analysis. The details will be explained in the class. During one of the Stata sessions, I will provide the students with the exemplary questions they will need to address in their final project. Students with Documented Disabilities: If you have a disability that necessitates extra time for exams, or any other accommodations, you will need to give me a note from the BU office of Disabilities Services at least one week before exam so that I can make necessary arrangements. Academic conduct. The Boston University academic conduct policies are available at http://www.bu.edu/academics/policies/academic-conduct-code/ Tentative Course Outline I. Basic Concepts of Probability Theory. II. Discrete random variables and their probability distributions. III. Continuous random variables and their probability distributions. IV. Foundations of Bayesian analysis. V. Sampling and sampling distributions. Central limit theorem and law of large numbers. VI. Point versus interval estimation. Construction of confidence intervals. VII. Methods of estimation (least squares, method of moments, maximum likelihood) VIII. Estimators and their properties. IX. Parametric hypotheses testing. X. Bivariate regression analysis (time permitting). XI. Empirical data analysis and presentation – concepts and implementation in Stata. XII. Tentatively (subject to change): Midterm #1 is on February 18, in class and midterm #2 is on March 18th, in class XIII. Final exam is at 3pm-5pm, on Tuesday, May 6, in class.
BUSINESS ANALYSIS REPORT Key Insights and Recommendations BSc Data Science and Business Analytics (Standard Entry) Business analytics, applied modelling and prediction (ST2187) 1. Executive Summary The business report outlines the insights from the visualizations created using Tableau and provides recommendations and strategies to acton those findings. There are primarily 7 insights: i. Tables are the least profitable product among all other products sold. ii. Tables are significantly more profitable in some countries compared to others. iii. Majority of customers who purchased Tables are associated with low volume of sales and high losses. iv. Tables have a substantially greater percentage of discount compared to other products. v. Significantly greater percentage of discount on Tables in unprofitable countries over profitable countries. vi. Forecast of sales and profits of Tables indicate that Tables remain unprofitable in the future. vii. Tables experience volatile seasonality effects in its sales. The findings outlined above help set the direction of the report. The report focuses on Tables as it is the only unprofitable product among all other products sold by the firm. The difference in profitability of Tables in different countries raises the question on what makes Tables profitable in some countries and unprofitable in others. An analysis on the average discount on all products sold by the firm reveals that Tables have a significantly greater percentage of discount compared to the discount on other products. This finding exposes the underlying reason for the unprofitability of Tables which is due to excessive discounts that are reducing profits margins. The firm could address the excessive discount on Tables by balancing the discount in profitable and unprofitable countries. It could raise discounts in profitable countries to increase profits generated and reduce discounts in unprofitable countries to decrease losses generated which would improve the overall profitability of Tables. The company could take advantage of seasonality in sales of Tables by designing Tables to be locally responsive to different cultures in different timings. New marketing strategies and promotions could be tested during off-peak seasons for Tables and implemented in peak seasons to boost sales and profitability of Tables. Additional strategies such as personalized discounts for customers and loyalty programs could be implemented to attract and retain customers. Implementation of above-mentioned strategies would improve the profitability of Tables and help the firm gain a competitive edge over its competitors in the long run. 2. Introduction The business report is conducted based on data collected by the company from December 2016 to December 2020. The data includes information on the sales and profits of multiple products sold by the company along with geographical information such as country and region, shipping information such as shipping date and shipping cost, customer information such as customer names and customer segmentation, the category and sub-category of products, the discount on products sold, and etc. The aim of this report is to identify unprofitable products sold by the company and the underlying causes for the unprofitability of the products, so as to formulate strategies and provide recommendations to minimize losses and maximize profits generated by the company. 3. Insights from Story 3.1 Overview of Profits and Sales by Product Figure 1: Ranking of Products by Overall Profitability Tables are ranked 17th in overall profits amongst 16 other products sold. They are the least profitable product among all products sold from 2017 to 2020, resulting in a loss of $64,000. Figure 2: Ranking of Products by Overall Sales Tables are ranked 8th in overall sales amongst 16 other products sold. They have comparable sales with respect to sales volume of other products sold from 2017 to 2020, resulting in sales of $757,000. Thus, the losses experienced from the sale of Tables is not associated with the lack of sales, but rather with other underlying issues which will be exposed in this report. 3.2 Geographical Overview of Profitability of Tables Figure 3: Top 10 Most and Least Profitable Countries for Tables From figure 1, we have identified Tables to be the least profitable product. Figure 3 exposes that Tables are not unprofitable throughout all the countries. In some countries like India and United Kingdom, the sale of Tables is much more profitable compared to its sale in countries like United States and Indonesia. Given the discoveries made, it is crucial to understand why the sale of Tables is profitable in some countries while unprofitable in others. The next few sections will shed more light on the overall unprofitability of Tables and itsunprofitability in certain countries. 3.3 Overview of Overall Distribution of Customers Figure 4: Distribution of Customers by Profits and Sales of Tables Figure 4 displays the distribution of customers by the total sales of Tables made to each individual customer from 2017 to 2020, and the corresponding profits generated from those sale of Tables. Observe that there is a greater proportion of unprofitable customers over profitable customers in the sale of Tables which aligns with the overall unprofitability of Tables. Furthermore, observe that significant proportion of customers associated with low sales generate higher losses compared to customers associated with high sales. This indicates that the sale of Tables is extremely unprofitable under certain conditions as the company experiences large losses from individual customers despite having low sales. Thus, it is imperative that the company discovers the conditions it should avoid to ensure that Tables become profitable. Figure 5: Distribution of Customers in Top 10 Most and Least Profitable Countries for Tables From figure 5, it is notable that significant proportion of individual customers who purchase Tables tend to be associated with low profits in profitable countries and low losses in unprofitable countries. This reinforces the reason for Tables to be unprofitable in some countries while being profitable in others which is attributed to its distribution of customers in profits and sales. Given the discoveries made earlier, the next section will expose the underlying reason for Tables to be the only unprofitable product, and the reason for some countries to be profitable in the sale of Tables while others remain unprofitable. 3.4 Overview of Discount on Products Figure 6: Average Discount of Products Figure 6 shows that the average discount on Tables from 2017 to 2020 is 29% which is significantly greater than the 15% average discount on all products. The significantly greater percentage of discount provided on Tables greatly reduces the profits generated from the sale of Tables. This explains why Tables are the only unprofitable product in figure 1 despite its comparable sales with respect to other products with similar sales volume such as Machines and Accessories. Figure 7: Average Discount in Top 10 Most and Least Profitable Countries for Tables Furthermore, the average discount on Tables differs significantly from the top 10 most and least profitable countries for Tables. Figure 7 shows that the average discount on Tables is 52% in the top 10 least profitable countries for Tables while the average discount on Tables is only 7% in the top 10 most profitable countries for Tables. This explains why tables are profitable in some countries while unprofitable in others in figure 3, which is attributed to the difference in average discount on Tables from 2017 to 2020. 3.5 Overview of Seasonality and Forecast of Tables Figure 8: Forecast of Profits and Sales for Tables Based on the four years of data from 2017 to 2020, the forecast of the following two years in figure 8 indicate similar sales volume for the sale of Tables in 2021 and 2022. Furthermore, the forecast of profits in 2021 and 2022 remains to be negative as it predominantly was from 2017 to 2020. Figure 9: Seasonality of Profits and Sales for Tables Observe that there is a seasonal effect on the sale of Tables during the months of June, November, and December which experience a significantly greater volume of sales compared to the other months. Furthermore, July in particular tends to experience significantly lower volume of sales compared to the other months. It is notable that on months that experience significantly greater sales of Tables such as June, November,and December, the company generates significantly greater losses compared to the other months. Observe that the greater the volume of sales of Tables, the greater the losses suffered by the company. As previously discovered, this is directly attributed to the significantly greater discount on Tables which reduces profits greatly as the volume of sales increases. 4. Conclusion The report has exposed Tables to be the least profitable product among all other products sold despite having comparable sales. Furthermore, Tables are unprofitable in some countries while profitable in others. The distribution of customers who purchased Tables by sales and profits indicates significant proportion of individual customers associated with low volume of sales but high losses which supports the overall unprofitability of Tables. The unprofitability of Tables is mainly due to the significantly greater percentage of discount on Tables compared to the discount on other products. This finding is supported by the significantly greater percentage of discount on Tables in unprofitable countries compared to the discount on Tables in profitable countries. Given the insights derived, strategies and recommendations have been provided in the next section to minimize the losses generated from Tables and transform. Tables into a profitable product. 5. Recommendations 5.1 Reducing Average Discount for Tables Figures 6 and 7 for reference Given that the average discount on Tables is significantly greater than the average discount on other products as shown in figure 6, the intuitive approach would be to reduce the discount on Tables. However, such an approach might reduce the revenue generated due to a fall in sale of Tables. Thus, given that the average discount for Tables in the top 10 most profitable countries are significantly lower than the average discount in the top 10 least profitable countries as shown in figure 7, the company could balance the discount on Tables in different countries. Discounts on Tables could be adjusted to be greater in profitable countries and be reduced in unprofitable countries. This would reduce the losses generated due to reduced discount in unprofitable countries while increasing revenue generated from the increase in sales due to greater discount on Tables in profitable countries. Overall, this would increase the profitability of Tables. 5.2 Taking Advantage of Seasonality for Tables The company can take advantage of seasonality by designing Tables to suit the needs of customers during the various seasons. For example, a proportion of Tables manufactured could be designed to be horror themed during the autumn season when Halloween is celebrated. Thus, the Tables manufactured would be locally responsive to the different cultures in countries. Portion of Figure 9 for reference During periods of low sales of Tables such as in July, the company can test out the effectiveness of various promotions through A/B testing. It involves offering the promotion to half the customers over multiple time periods and weighing the differences in profits generated between the two groups. If a type of promotion proves to be profitable, it could be implemented during peak seasons to further boost the sales of Tables. Discounts could be increased to enhance sales further during peak season such as in November and December, and reduced during off-peak season such as in July to balance and optimize overall discount on Tables throughout the year. The company could take advantage of seasonality of other products by bundling products together to be sold with Tables. For example, if Chairs experience a period of high sales, customers could be incentivized to buy a Table along with Chairs by offering discounts when both products are purchased together. Such an approach preserves the sales volume of chairs while increasing the sale of Tables as well. 5.3 Additional Strategies Discounts could be personalized to customer purchasing patterns. Some customers may require a 10% discount incentive to purchase products while others may require a greater amount of discount. Furthermore, the firm could take note of repeat customers and offer them a loyalty program that provides them with special discounts that other customers would not have access to. This creates loyal customers for the firm who would make repeated purchases and increase sales for the firm over time. It is crucial for the firm to develop additional strategies on top of the recommendations provided above to develop a competitive advantage over its competitors.
INF20016 Big Data Management Assignment 2:Analytics challenge data set This data set is similar to the Assignment 3 data set you looked at during this teaching period.This dataset was created for this analytics challenge becauseyou are alreadyfamiliar with this type of data and have been practising with the Assignment 3 dataset.Do note,however.that there are differences between the analytics challenge and the Assignment 3 datasets. There are multiple components of costs in this dataset.Hence.you need to take care in interpreting the tasks. For example,there are three different Marketing-related fieldsin the dataset marketing campaign,variable marketing.and marketing Costs.Depending on your task.you may be asked to use specific marketing components for youranalytics and visualisations.In your exploration of this dataset.it is worth developing an understanding of allthe calculations/formula involved ie.Net Sales is calculated witha formula of Gross Sales Marketing Costs.Hence,in the case where the task specifiesthat Marketing Costs arevaried/changed,you should be able to produce an analytics and visualisation on the impact on the Net Sales for on any other components tha thetask requires). The relationships between tables can be established by understanding the data definitions.The relevant field acting as the key to relate one table to the other is clearly described inthedata definitions.
MGMT 223 Module 6: Job quality analysis: an example Individual essay You are required to apply the effort-reward imbalance(ERI)model and the job demands-resources (JDR)model to(a)analyse the quality of a job and(b)to make recommendations on how the job could be improved for the benefit of the individual and the benefit of the organisation(or without causing harm to the organisation).This is to be expressed in an essay of 1500 to 1700 words. You may pick a job(a)that you currently hold or have held or(b)a job depicted in a TV series or movie ora job described in a novel,biography or memoir you have read.Jobs must be paid forms of employment but can be in any country.Where you analyse a job depicted in a TV series,movie or novel/biography/memoir,it must be the job of a specifc character or person and the events related should be set in the current century or in the last half of the twentieth century.If you anticipate difficulty in analysing a job of type(a)or type(b),please contact Peter Boxall immediately to discuss an alternative assignment. This essay does not rely on you discussing sources beyond those in the course readings although such reading will be valuable for your understanding.It relies on you applying the theoretical models indicated(i.e.the ERI and JDR models)to the job you choose.In other words,this is an assignment in the application of the theory in the course,not an assignment about how widely you can read. Performance criteria A range 1.The theoretical models are applied in an excellent way,which shows an accurate and highly perceptive understanding of the theory and how to use it to assess the quality of a job. 2.The argument is highly compelling.It is hard to refute the logic or argue against the analysis and recommendations because they are so well thought through. 3.The work is highly readable,enhancing the power of ideas presented.Skilful,reflective editing of key points and wording has made for a high level of persuasiveness. B range 1.Shows the same faithful understanding of key concepts and theories as the Cgrade performance but applies the theoretical models with greater understanding. 2.As for the Cgrade but showing greater overall strength of argument.It is possible,however,to identify ways in which the assignment could be improved to reach the excellent(A)grade. 3.As for the C grade but likely to demonstrate a more lively style and demonstrate the benefits of better editing and reflection. C range 1.The major theoretical models associated with the analysis are understood and appropriately applied but the quality of understanding could be deepened. 2.Arguments are adequately defended.However,deeper reflection on the analysis would lead to improvements in key parts of the argument. 3.Word choice and paragraphing is appropriate but writing style can be improved to enhance persuasiveness. D/D-range 1.Major concepts and the theoretical models relevant to the assignment are overlooked, misunderstood or misapplied. 2.The assignment fails to focus appropriately on the question or the argument is seriously flawed. 3.Word choice and paragraphing is often inappropriate,compromising the effectiveness of communication or plagiarism is present in all or part of the assignment. Requirements for formatting and submission Assignments must be typed using standard A4 paper size. You must state the number of words used in the text of your essay on the frontpage(i.e.the total words used exclusive of the reference list).Marks may be deducted for failing to do this or for exceeding the word limit,which is 1700 words.Excess words often reveal poor organisation of the material included or poor editing of the essay. ·Standard Microsoft Word settings of 2.5 margins right-and left-hand side are acceptable.Fully justified paragraphs are requested(as opposed to left-or right-aligned). 1.5 line spacing is required. 12pt Times New Roman,Arial,or Calibri are the best fonts. Following APA citing/referencing style,each assignment must contain in-text citationsand a complete reference list.APA style. advice can be found here: https://www.cite.auckland.ac.nz/2_8.html Submit an electronic copy to CANVAS (see the"Assignments"tab on Canvas).No paper copy submissions are accepted.Digital submission is the only acceptable method. All submissions will be checked through the Turnitin software(embedded within Canvas "Assignments").
MATHEMATICAL METHODS ASSESSMENT 3: GRAPHING PROJECT Program Standard Foundation Program and Tertiary Preparation Program Cohort October 2024 Course Mathematical Methods Course Code MATH1102E Assessment Name Assessment 3: Graphing Project Weighting 20% Context and Task Polynomial curves are found in many locations in the world. Bridge arches, water fountains, rollercoasters, roads, telecommunications dishes and natural landscapes can all feature polynomials in their outline/structure. Additionally, projectiles and any object/material acted on by gravity when in free motion exhibit a trajectory that is modelled accurately by a parabolic curve. Photo credit: https://www.dreamstime.com/stock-photo-water-dry-fountain-park-close-up-image98389957#_ Your task is to locate one curve either in nature or in a human-made structure that can be represented effectively by a polynomial. Alongside this, you are to generate one curve in real-life using an object such as a football, tennis ball, a free hanging chain or a water hose. You are to photograph and digitise these two curves using appropriate technology. Software such as ImageJ (https://imagej.nih.gov/ij/) can be useful in the digitisation process. After this you will develop two separate polynomial functions that precisely model the shape of these two curves. In addition, you are to write a report that explains how you developed and refined your models. This report must be less than 8 pages in length (using single spacing not including the title page, table of contents, references list and appendices). You must use a software package such as Microsoft Excel or Desmos (free to use at www.desmos.com) to develop, modify and display your curve data. You must also verify the validity of your curve models by using analytical techniques (pen and paper methods). When presenting the development of your mathematical models in the report you must consider, refine and evaluate at least three polynomial functions for your two curves. For assistance on how to structure and write your report please read the following high-level exemplar provided by the Queensland Curriculum and Assessment Authority (QCAA): https://www.qcaa.qld.edu.au/downloads/senior-qce/mathematics/snr_maths_methods_19_unit1_asr_high_psmt.pdf To successfully complete this assignment, you must do the following: • Present your findings as a report based on the approach to problem-solving and mathematical modelling outlined in the exemplar/s provided by your teacher. Your teacher will discuss this with you in more detail during class time. • Respond with a wide range of understanding and skills, such as using appropriate mathematical language, calculations, and tables of data, graphs and diagrams. • Provide an authentic response that highlights this real-life application of mathematics. Do NOT use data collected by someone else. • Respond using a written report format that can be read and interpreted independently of the assignment task sheet. • Follow the requirements of the Marking Criteria Sheet (Rubric) (see the following page). • Use both analytic (pen and paper) procedures and technology (Microsoft Excel or Desmos) throughout your response. The report you produce to present your results must include these components: • Introduction: Provide a detailed outline of the task, all your assumptions and observations, and clearly define all the mathematical and computational techniques used in the report. • Results: Include and discuss tabular and graphical representations of the data collected. • Discussion: Further discuss your results by evaluating the accuracy of your model/s, detail the strengths and limitations of the modelling process that you used, and comment on the reasonableness of your model/s. • Conclusion: Include a brief summary of your findings, and the strengths and limitations of your best model. • References List: Use the APA referencing scheme.
CIS*2750 Assignment 1 Deadline: Monday, February 10, 11:59pm Weight: 16.6% Module 2: Error handling details functions Description This module provides additional details on expected error handling functionality. Most error handling will take place in the createCard function, which has the following prototype: VCardErrorCode createCard(char* fileName, Card** newCardObject); We do not need to fully validate the format (yet). For example, we do not need to verify that the N property has 5 values. However, we do need to verify that the overall grammar of the file, as specified in Section 3.3 of the vCard specification, is correct. So while an N property with 4 or 6 values would not be considered "invalid" in Assignment 1, an N property with no values would be considered invalid, since every property must have a value. If createCard successfully creates a Card object, it returns VCardErrorCode value OK. However, the parsing can fail for various reasons. In that case, the newCardObject argument must be set to NULL and the function must return the appropriate error code: - INV_FILE is returned if there is a problem with fileName argument - it is null, it is an empty string, file does not exist or cannot be opened, file does not have the correct extension, etc.. If the error is in the file contents, you must use an appropriate error code - see below. - INV_CARD is returned if the file can be opened, but vCard object itself is invalid. You would return this code if : - The file is missing the begin and/or end tags. - The file is missing any of the required properties, i.e. VERSION or FN - The version is not 4.0 If the error is in one of the properties (optional or required), return a property-specific error code instead (see below). - INV_PROP is returned a property in a vCard file is somehow invalid, and cannot be parsed correctly. There are too many possibilities to list, but some of them are: - The property has no value - The property has no : separator - Property parameter has no value - Invalid line terminator - etc. - OTHER_ERROR is returned if some other, non-vCard error happens (e.g. malloc returns null). Error guidelines: - Always return the earliest error in the file - i.e. the error with the smallest line number. For example, if the file is missing the VERSION and, later in the file, has a property with no value, you must return INV_CARD, not INV_PROP. You could always keep track of the line number that generated the error to avoid ambiguity. Since you are parsing your file in order, this should not be difficult. - On the other hand, if the problem is that the required FN property is missing its value, the error code must be INV_PROP. - Make sure the error is specific. If the error is in the header TEL property, the error code must be INV_PROP - not INV_CARD. or INV_FILE. Error Handling The error handling functionality in your code must return the correct error code. It should not try to "fix" the error in any way. In particular: - Do not print any error messages to the screen. Simply return the error code, and let the "user" of your library - i.e. whatever function called createCard() - deal with the error - Do not ask the user to re-enter any input, e.g. if file name is invalid. Again, that is not the job of the library. The library simply detects and reports the errors. You must make sure that you free all temporary resources if an error is encountered. For example, if, when you get to the end of a vCard file, you find that the end tag is missing, you will need to free all memory that you may have allocated to a Card object, close the file, and return INV_CARD. Make sure you validate all function arguments, particularly in the card parser functions. Passing an invalid argument should not crash your code. For example, printing or freeing a null Card struct must be handled gracefully. An attempt to delete a null card using deleteCard() must result in nothing happening. And attempt to print a null card using cardToString() would result in the function returning an appropriate string, e.g. "null". Similarly, trying to print a non-existent error using const char* errorToString(VCardErrorCode err); must have a consistent, predictable behaviour - and not crash. If the error code is unknown - i.e. it is not defined in the VCardErrorCode type - return the string "Invalid error code".
CBE 9450 Advanced Chemical Reaction Engineering Assignment #3 Due date: Monday Feb 3rd, 2025 Problem 1: Tracer results illustrated in the enclosed figures were obtained for an F(t) step response in a given chemical process. Using what you have learned in the course, create anon-uniform model (A combination of different ideal reactor models) that will provide an adequate representation of the response to a tracer step. Your model must: a) Be presented as an equation F(t) = f(t, C, V, etc) b) Present a plot of the model data compared with the experimental data. c) Include an introduction explaining why you are using each unit and how they affect the stream outcome. d) Present all the equations, assumptions, and derivations used to come up with the final model. e) Specify the average holding time (t(̅)) for every reactor in your model. Note: You may find the Web Plot Digitizer website useful. https://automeris.io/WebPlotDigitizer/ Problem 2: Show that the conversion of areactor represented by the Cholette and Cloutier model with v2 /v being the volumetric flow fraction circulating in the active reactor volume, can be assessed as follows using a “macrofluid model”: a) For zero order reactions b) For the first-order reactions c) Compare how do these conversions compare with the ones for “microfluid model” in the same system? Note: A step-by-step derivation with specification of units is required here. A direct use of species equations will not do it.
EC203 Empirical Economics Problem Set #2 10) JacketRatings. OutdoorGearLab is an organization that tests outdoor gear used for climbing, camping, mountaineering, and backpacking. Suppose that the following data show the ratings of hardshell jackets based on the breathability, durability, versatility, features, mobility, and weight of each jacket. The ratings range from 0 (lowest) to 100 (highest). a) Compute the mean, median, and mode. b) Compute the first and third quartiles. c) Compute and interpret the 90th percentile. 28) VarattaSales. Varatta Enterprises sells industrial plumbing valves. The following table lists the annual sales amounts for the different salespeople in the organization for the mostrecent fiscal year. a) Compute the mean, variance, and standard deviation for these annual sales values. b) In the previous fiscal year, the average annual sales amount was $300,000 with a standard deviation of $95,000. Discuss any differences you observe between the annual sales amount in the mostrecent and previous fiscal years. 45) iPads. The New York Times reported that Apple has unveiled a new iPad marketed specifically to school districts for use by students (The New York Times website). The 9.7-inch iPads will have faster processors and a cheaper price point in an effort to take market share away from Google Chromebooks in public school districts. Suppose that the following data represent the percentages of students currently using Apple iPads for a sample of 18 U.S. public school districts. a) Compute the mean and median percentage of students currently using Apple iPads. b) Compare the first and third quartiles for these data. c) Compute the range and interquartile range for these data. d) Compute the variance and standard deviation for these data. e) Are there any outliers in these data? f) Based on your calculated values, what can we say about the percentage of students using iPads in public school districts? 57) StockComparison. The file StockComparison contains monthly adjusted stock prices for technology company Apple, Inc., and consumer-goods company Procter & Gamble (P&G) from 2013–2018. a) Develop a scatter diagram with Apple stock price on the horizontal axis and P&G stock price on the vertical axis. b) What appears to be the relationship between these two stock prices? c) Compute and interpret the sample covariance. d) Compute the sample correlation coefficient. What does this value indicate about the relationship between the stock price of Apple and the stock price of P&G? 61) BestPrivateColleges. A random sample of 30 colleges from Kiplinger’s list of the best values in private college provided the data shown in the file BestPrivateColleges (Kiplinger website). The variable named Admit Rate (%) shows the percentage of students that applied to the college and were admitted, and the variable named 4-yr Grad. Rate (%) shows the percentage of students that were admitted and graduated in four years. a) Develop a scatter diagram with Admit Rate (%) as the independent variable. What does the scatter diagram indicate about the relationship between the two variables? b)Compute the sample correlation coefficient. What does the value of the sample correlation coefficient indicate about the relationship between the Admit Rate (%) and the 4-yr Grad. Rate (%)?
Assignment Remit Programme Title CS in IB Module Title Cooperative Strategy in International Business Module Code 37986 Assignment Title Group Assignment Level PGT Weighting 30% Hand Out Date 20/01/2025 Deadline Date & Time 05/02/2025 12pm Feedback Post Date 21st working day after the deadline date Assignment Format Presentation Assignment Length 10 minutes Submission Format Online Team Module Learning Outcomes: This assignment is designed to assess the following module learning outcomes. Your submission will be marked using the Grading Criteria given in the section below. LO 1. Explore the role of cooperative strategy, and its expression through strategic alliances, in the modern business context. LO.2 Explain and appraise the considerations involved in establishing alliances, selecting partners and choosing an alliance form. LO. 3 Demonstrate a critical awareness of issues arising in the management of cross-border and/or cross-sector alliances. LO.4 Critically evaluate factors impacting on the achievement of alliance objectives (including learning) and on how they may evolve over time. LO.5 Demonstrate group learning and judgement skills, systematically applied to different international business scenarios. Assignment: · You are employed by a large Consultancy and have been asked to identify a potential international strategic partner for an MNE of your choice, with the key objective of entering a new foreign market. As a group of 5 to 6 students, you are asked to develop a video presentation in the form. of a 10 min-recorded presentation (+/-10%) using PowerPoint. Your video should contain a prominent view of the presentation slides along with audio of the spoken presentation. You are required to create voice over Power Point slides (follow these instructions ) and generate an MPEG-4 (.mp4) file (follow these instructions) from your slides and audio. · You can use information/evidence from secondary sources, such as company websites and reports, reputable policy organisations and media such as the World Bank or Transparency International, Financial Times, The Wall Street Journal, industry/market reports, and academic journal articles to support your analysis and recommendations. Please ensure that all evidence is adequately referenced (with citations in text) and with a final slide containing a full list of references. Harvard referencing guidance is available through the following link: https://intranet.birmingham.ac.uk/as/libraryservices/library/referencing/icite/harvard/index.aspx · Ideally, you should select a well-known MNE and potential international strategic partner with publicly available company data. Your presentation brief must address the following issues, drawing from the tools and frameworks discussed in the module: · Briefly introduce the MNE and the potential international partner you have identified. · Drawing from the frameworks discussed in class, evaluate the fit between the potential alliance partner and the MNE, considering the pre-set objective. Please explain/justify your views. · Drawing from your evaluation of the two potential partners (MNE and Strategic Partner), make final recommendations about the form. of alliance to be entered. · Your recorded presentation should draw from, apply, and display the partner selection and fit evaluation tools and frameworks discussed during the module. · Individuals must attend all seminars where most of the group assignments will be developed. If there's any lack of engagement in the group assignment, group members can fill in a peer review form. that can be found in the module canvas page. Then the module team will investigate and review the evidence; if the claim of lack of contribution is substantiated, then the concerned students will receive a minimum of mark reduction of 10%, with the reduction being proportionate to the lack of contribution. · The final recorded presentation should be submitted on Canvas by 5 February 2025, 12pm. Grading Criteria / Marking Rubric Your submission will be graded according to the following criteria: 1. Addresses the task/question(s) that was set and structure of arguments (20%) 2. Make appropriate use of relevant literature, theoretical frameworks, tools and concepts discussed during the module (20%) 3. Understanding / Critical thinking (20%) 4. Conclusions /recommendations grounded in analysis (20%) 5. Video presentation skills /teamwork /presentation/Referencing (20%) See the marking rubric at the end of the remit for more information on how your work will be marked and graded.
Comp 251: Assignment 1 Answers must be returned online by February 7th (11:59:59pm), 2025. General instructions (Read carefully!) • Important: All of the work you submit must be done by only you, and your work must not be submitted by someone else. Plagiarism is academic fraud and is taken very seriously. For Comp251, we will use software that compares programs for evidence of similar code. This software is very effective and it is able to identify similarities in the code even if you change the name of your variables and the position of your functions. The time that you will spend modifying your code, would be better invested in creating an original solution. Please don’t copy. We want you to succeed and are here to help. Here are a couple of general guidelines to help you avoid plagiarism: Never look at another assignment solution, whether it is on paper or on the computer screen. Never share your assignment solution with another student. This applies to all drafts of a solution and to incomplete solutions. If you find code on the web, or get code from a private tutor, that solves part or all of an assignment, do not use or submit any part of it! A large percentage of the academic offenses in CS involve students who have never met, and who just happened to find the same solution online, or work with the same tutor. If you find a solution, someone else will too. The easiest way to avoid plagiarism is to only discuss a piece of work with the Comp251 TAs, the CS Help Centre TAs, or the COMP 251 instructors. • To some extent, collaborations are allowed. These collaborations should not go as far as sharing code or giving away the answer. You must indicate on your assignments (i.e. as a comment at the beginning of your java source file) the names of the people with whom you collaborated or discussed your assignments (including members of the course staff). If you did not collaborate with anyone, you write “No collaborators”. If asked, you should be able to orally explain your solution to a member of the course staff. At the end of this document, you will find a check-list of the behaviours/actions that are allowed during the development of this assignment. • This assignment is due on February 7 th at 11h59:59 pm. It is your responsibility to guarantee that your assignment is submitted on time. We do not cover technical issues or unexpected difficulties you may encounter. Last minute submissions are at your own risk. • Multiple submissions are allowed before the deadline for the coding questions. We will only grade the last submitted file. Therefore, we encourage you to submit as early as possible a preliminary version of your solution to avoid any last minute issue. Please notice that for the proof question, only a single submission is allowed. • Late submissions can be submitted for 24 hours after the deadline, and will receive a flat penalty of 20%. We will not accept any submission more than 24 hours after the dead-line. The submission site (Ed-Lessons and MyCourses) will be closed, and there will be no exceptions, except medical. • In exceptional circumstances, we can grant a small extension of the deadline (e.g. 24h) for medical reasons only. • Violation of any of the rules above may result in penalties or even absence of grading. If any-thing is unclear, it is up to you to clarify it by asking either directly the course staff during office hours, by email at ([email protected]) or on the discussion board (recom-mended). Please, note that we reserve the right to make specific/targeted announcements affecting/extending these rules in class and/or on one of the communication channels used in the course. It is your responsibility to monitor MyCourses and the discussion board for announcements. • The course staff will answer questions about the assignment during office hours or in the online forum. We urge you to ask your questions as early as possible. We cannot guarantee that questions asked less than 24h before the submission deadline will be answered in time. In particular, we will not answer individual emails about the assignment that are sent the day of the deadline. Programming component • You are provided some starter code that you should fill in as requested. Add your code only where you are instructed to do so. You can add some helper methods. Do not modify the code in any other way, and in particular, do not change the methods or constructors that are already given to you, do not import extra code or libraries, and do not touch the method headers. The format that you see on the provided code is the only format accepted for programming questions. Any failure to comply with these rules will result in an automatic 0. • Public tests cases are available on ed-Lessons. You can run them in your code at any time. If your code fails those tests, it means that there is a mistake somewhere. We highly encourage you to modify our tests and expand them. Do not include it in your submission. • Your code should be properly commented on and indented. • Do not change or alter the name of the files that you must submit or the method headers in these files. Files with the wrong name will not be graded. Make sure you are not changing file names by duplicating them. For example, main (2).java will not be graded. • Do not add any package or import statement that is not already provided • Please submit only the individual files requested. • You will automatically get 0 if the files you submitted on ed-Lessons do not compile, since you can ensure yourself that they do. Note that public test cases do not cover every situation and your code may crash when tested on a method that is not checked by the public tests. This is why you need to add your own test cases and compile and run your code from command line on linux. Homework Exercise 1 (60 points). Building a Hash Table We want to compare the performance of hash tables implemented using chaining and open addressing. In this assignment, we will consider hash tables implemented using the multiplication and linear probing methods. Note that the multiplication method described here is slightly differ-ent from the one that was seen in class, but the principle remains the same. We will (respectively) call the hash functions h and g and describe them below. Note that we are using the hash function h to define g. Collisions solved by chaining (multiplication method): h(k) = ((A · k) mod 2w) >> (w − r) Open addressing (linear probing): g(k, i) = (h(k) + i) mod 2r In the formula above, r and w are two integers such that w > r, and A is a random number such that 2 w−1 < A < 2 w. In addition, let n be the number of keys inserted, and m the num-ber of slots in the hash tables. Here, we set m = 2r and r = ⌈w/2⌉. The load factor α is equal to m/n. We want to estimate the number of collisions when inserting keys with respect to keys and the choice of values for A. We provide you a set of two template files that you will complete. This file contains two classes, one for each hash function. Those contain several helper functions, namely generateRandom that enables you to generate a random number within a specified range. Please read the provided code describing the hashtable classes with attention. Your first task is to complete the two java methods Open_Addressing.probe and Chaining.chain. These methods must implement the hash functions for (respectively) the linear probing and mul-tiplication methods. They take as input a key k, as well as an integer 0 ≤ i < m for the linear probing method, and return a hash value in [0, m[. Next, you will implement the method insertKey in both classes, which inserts a key k into the hash table and returns the number of collisions encountered before insertion, or the number of collisions encountered before giving up on inserting, if applicable. Note that for this exercise, we define the number of collisions in open addressing as the number of keys encountered, or "jumped over" before inserting or removing a key (note that this definition only makes sense if the key is in the hash table). For chaining, we simply consider the number of other keys in the same bin at the time of insertion as the number collisions. You can assume the key is not negative, and that we will not attempt to insert a key that already exists in the hash table. You will also implement a method removeKey, this one only in Open_Addressing. This method should take as input a key k, and remove it from the hash table while visiting the minimum number of slots possible. Like insertKey, it should output the number of collisions if the key is found. If the key is not in the hash table, the method should simply not change the hash table, and output the number of slots visited before giving up. You will notice from the code and comments that empty slots are given a value of −1. If applicable, you are allowed to use a different notation of your choice for slots containing a deleted element. Make sure to test your assignment thoroughly by thinking about all the different situations that can occur when dealing with hash tables. Build your own hash table and try inserting and removing keys! For this question, you will need to submit your Chaining.java and Open_Addressing.java source files to the Assignment 1 => Q1 - Hash lesson in Ed-Lessons. You will not be tested on execution time for this question, but you will be tested on the efficiency of your program in terms of number of steps. You must implement your own hash table. Using the built-in hash table from Java will result in a 0 on this question. Exercise 2 (110 points). Disjoint Set We want to implement a disjoint set data structure with union and find operations. The tem-plate for this program is available on MyCourses and named DisjointSets.java. In this question, we model a partition of n elements with distinct integers ranging from 0 to n − 1 (i.e. each element is represented by an integer in [0, · · · , n − 1], and each integer in [0, · · · , n − 1] represent one element). We choose to represent the disjoint sets with trees, and to implement the forest of trees with an array named par. More precisely, the value stored in par[i] is parent of the element i, and par[i]==i when i is the root of the tree and thus the representative of the disjoint set. You will implement union by rank and the path compression technique seen in class. The rank is an integer associated with each node. Initially (i.e. when the set contains one single object) its value is 0. Union operations link the root of the tree with smaller rank to the root of the tree with larger rank. In the case where the rank of both trees is the same, the rank of the new root increases by 1. You can implement the rank with a specific array (called rank) that has been added to the template, or use the array par (this is tricky). Note that path compression does not change the rank of a node. (50 points) Part A: Building a Disjoint Set Download the file DisjointSetsB.java, and complete the methods find(int i) as well as union(int i, int j). The constructor takes one argument n (a strictly positive integer) that indicates the number of elements in the partition, and initializes it by assigning a separate set to each element. The method find(int i) will return the representative of the disjoint set that contains i (do not forget to implement path compression here!). The method union(int i, int j) will merge the set with smaller rank (for instance i) in the disjoint set with larger rank (in that case j). In that case, the root of the tree containing i will become a child of the root of the tree containing j), and return the representative (as an integer) of the new merged set. Do not forget to update the ranks. In the case where the ranks are identical, you will merge i into j. Once completed, compile and run the file DisjointSets.java. It should produce the output available in the file unionfind.txt available on MyCourses. (60 points) Part B: Modifying a Disjoint Set For this part of the assignment, we will modify the Disjoint Set data structure coded in the previous question. In particular, we want now also to support the following two additional oper-ations/methods. • The method move(int i, int j) will move the i to the set containing j. Please notice that if i and j are already in the same set, the command must be ignored. • The method sum_elements(int i) must return the sum of elements in the set containing i. Let’s see an example reporting a sequence of commands to make sure that the information is clear. • new DisjointSets(6): {0}, {1}, {2}, {3}, {4}, {5} • union(1,2): {0}, {1,2}, {3}, {4}, {5} • move(3,4): {0}, {1,2}, {3,4}, {5} • union(3,5): {0}, {1,2}, {3,4,5} • sum_elements(4): 12 • move(4,1): {0}, {1,2,4}, {3,5} • sum_elements(4): 7 • sum_elements(3): 8 Please notice the following important information. • Complete your code using the DisjointSetsB.java template provided in MyCourses. – The main difference of this new file (with respect to DisjointSetsA.java) is that it contains the header for the move(int i, int j) and sum_elements(int i) functions. – You will need to copy and paste the code that you already generated for the find(int i) and union(int i, int j) functions in the previous question. – For this question, you are allowed to add helper functions and to modify (if needed) the class attributes in the provided code; however, you should not change the methods or class headers. It is your responsibility to guarantee that your modifications do not make the autograder to crash. • Given that this question represents a modification of a Union Find data structure, you are not required to guarantee that the move(int i, int j) and sum_elements(int i) operations run in O(α(n)). Your code will be evaluated in terms of correctness and not efficiency; however, your implementation must still run on the time resources provided by the autograder. Exercise 3 (80 points). Ed-discussion board The company who created our discussion board (Ed) contacted us to develop a new feature in their software. The feature must identify important topics discussed on Ed by filtering via specific keywords. Given a list of Ed messages, these keywords correspond to the words that were used by all users in their messages. The feature is expected to return a sorted list of keywords, from most to least used words (i.e., the word with the highest frequency must be the first one). In case of a frequency tie, the word needs to be sorted in alphabetical order. Let us see now some features of the discussion board posts. The list of posts will be provided to you as an array of strings (String[]), where every slot in the array will contain a message. All messages will have the following characteristics. • Each message is represented in Java as a String • Each message begins with a user name of no more than 20 characters. • After the name, each message continues with the content of that user’s post all in lower cases. • Each word in the content of the user’s post is separated by a single space character. • The total number of characters across all messages, including spaces, will not exceed 2 ∗ 106 Let’s see now two examples to make sure that everything is clear. Given the following list of posts: David no no no no nobody never Alexia why ever not Parham no not never nobody Brian no never know nobody Jeremy why no nobody Jeremy nobody never know why nobody David never no nobody Alexia never never nobody no Your algorithm must return the array [no, nobody, never] (exactly in that order). Those three words were used by every single user of our discussion board and they are reported in order of frequency (i.e., “no” is the most frequently used word). In case of a tie, the order was decided lexicographically. Now, if the following list of posts is given to you: David comp Ziqi music Your algorithm must return an empty array [] given that any of the words in the post were used by every single user. For this question, you must implement your solution in the function Discussion_Board(String[] posts) which is inside the class/file A1_Q3.java. Please note that for this question the correctness and efficiency of your algorithm will be tested, then it is of your interest to code your solution using the right algorithms and data-structures. What To Submit? For the coding exercises: Attached to this assignment are Java template files. You have to submit only this java files. Please DO NOT zip (or rar) your files, and do not submit any other files. For the proof exercise: You have to submit a PDF document (in the form. of a report) answering the proposed questions/exercises. Please DO NOT zip (or rar) your files, and do not submit any other files. Where To Submit? For the coding exercises: You need to submit your assignment in ed - Lessons. Please note that you do not need to submit anything to myCourses. For the proof exercise: You need to submit your assignment in myCourses. Please note that you do not need to submit anything to ed - Lessons. When To Submit? Please do not wait until the last minute to submit your assignment. You never know what could go wrong during the last moment. Please also remember that you are allowed to have multiple submission. Then, submit your partial work early and you will be able to upload updated versions later (as far as they are submitted before the deadline). How will this assignment be graded? Please remember that we are only considering the highest 4 scores as the overall grade of your assignment. For the coding exercises: Each student will receive an overall score. This score is the combina-tion of the passed open and private test cases. The open cases correspond to the examples given in this document plus other examples. These cases will be run with-in your submissions and you will receive automated test results (i.e., the autograder output) for them. You MUST guarantee that your code passes these cases. In general, the private test cases are inputs that you have not seen and they will test the correctness of your algorithm on those inputs once the deadline of the assignment is over; however, for this assignment you will have information about the status (i.e., if it passed or not) of your test. Please notice that not all the test cases have the same weight.
Experiment AM1.6 - Mechanical Testing (compressive) Learning outcomes By the end of this experiment you should: • Understand how standard materials deform under load including at larger strain. • Be able to calculate key material properties from raw data. Description This laboratory examines the behaviour of a polymer under load. For which you are required to determine some key material properties of each: o Engineering stress o Engineering strain o Young's modulus o True strain o Tangent modulus Procedure Using the Mechanical Testing Machine (ElectroForce 5500, TA), a polymer material is to be tested under high strain. You will be told how to operate the MTM by the Teaching Assistant (Standard Operating Procedures are available on CANVAS). Ask your Teaching Assistant as to the specific polymer undergoing testing. WHEN THE ACTUATOR IS ‘ON’, NO USER OR OBSERVER SHOULD TOUCH (OR BE NEAR) THE ACTUATOR AND/OR OTHER MOVING PARTS. THE ACTUATOR MUST BE ‘OFF’ WHEN SPECIMENS ARE BEING PLACED/REMOVED AND/OR the compression is being ATTACHED. Take note of the strain rate used, as the mechanical properties of the specimen can change drastically at differing strain rates. Observe the computer screen where the load (F) vs. extension (dl) graph is generated automatically, note that these values can be used to calculate stress (ε) and strain (σ),as follows: Raw data will be provided on CANVAS following the session; however, note down at least 10 sets of force- displacement data (Table 1), to enable you to plot the figures suggested in this report (Figure 2). Observe the test specimen for its elongation and fracture point during the test. Use the force-displacement data (Table 1 ), to plot a stress-strain graph (Figure 2). Calculate the values listed in Table 1. Table 1. Table of force-extension data for the polymer tested. This table should be filled out within your lab session. Ensure you select a range of points to cover the dataset. It is recommended that when writing up this laboratory, you transfer the full data set provided on canvas to an excel document. Time [s] Displacement [mm] Force [N] Stress [MPa] Strain [%] True Strain [%] Repeat the experiment at a greater strain rate, and complete Table 2. Table 2. Table of force-extension data for the polymer tested at astrain rate of . This table should be filled out within your lab session. Time [s] Displacement [mm] Force [N] Stress [MPa] Strain [%] True Strain [%] Figure 1. Engineering stress-strain curves for the polymer at astrain rate of . It is recommended that when writing up this laboratory, transfer the full data set provided on canvas to an excel document to plot your curves. Marks are deducted consistently for poorly presented data. Figure 2. Engineering stress-strain curves for the polymer at astrain rate of . It is recommended that when writing up this laboratory, transfer the full data set provided on canvas to an excel document to plot your curves. Marks are deducted consistently for poorly presented data.
CCT461H5S LEC0101 Speculative Design III Course Outline - Winter 2025 Course Description Emerging technologies have the potential to transform. business models and architectures. In this course students learn the functional and technical underpinnings of selected emerging technologies and critically analyse how these technologies are impacting business functions. Students also gain hands-on experience with emerging technologies and consider how they may be applied or adapted to solve management issues. Prerequisite: CCT361H5 (SSc) Distribution Requirement: SSc It is your responsibility to ensure that the prerequisites for course have been met. Students without the prerequisites can be removed at any time. No waivers will be granted. Goals and Learning Objectives The goal of this course is to develop skills necessary for understanding, interpreting, and thinking about future designs. Students will study the theoretical perspectives of speculative design, methods for creating flexible and innovative designs of the future, and techniques for critiquing culture and design. They will also learn the technical basis for the creation of several emerging technologies and have the opportunity to create prototypes with them. Upon completing this course, students should be able to: Discuss the impact of disruptive technologies on project design, implementation, and transformation. Review current literature on the selection, implementation, and evaluation of new and emerging technologies and their impacts. Compare and contrast current and emerging technologies and their implications for social ethics and the global workplace. Follow various methodologies to create designs that explore the capabilities of emerging technologies Design a project plan that incorporates a new and emerging technology and illustrates its impact on organizations and industries. Create functional prototypes within the selected emerging technologies Assemble a portfolio that highlights and discusses their technical work in a meaningful and accessible manner Required Materials Required texts: Horton, J. (2021). Android Programming for Beginners: Build in-depth, full-featured Android apps starting from zero programming experience, 3rd Edition. Packt Publishing. LaValle, S. M. (2023). Virtual Reality. Cambridge University Press. http://lavalle.pl/vr/ Other assigned readings will be posted to Quercus. Assessment and Grading Policies Class Participation Throughout the course On-going 10% Assignment "In-Class" Group Case Study #1 2025-01-31 10% Assignment Group Mobile Design Project 2025-02-16 20% Assignment "In-Class" Group Case Study #2 2025-03-14 10% Assignment Group Virtual Reality (VR) Design Project 2025-03-28 20% Assignment Individual Assignment - Research Portfolio 2025-04-04 30% 100% You should receive at least one significant mark (15%) before the last day you can drop a course without academic penalty. Requirements and Criteria Class Participation: 10% (Due Date: On-going). Students are expected to participate both "in-person" and online via Quercus (https://q.utoronto.ca). a) "Higher grades" will be awarded for "higher quality" contributions. i.e. emphasis is on "quality" and not "quantity" of contributions. b) Last day to submit online participation comments (via Quercus) isFriday, April 4th at 11:59 pm. c) Students are limited to a maximum of ten (10) daily "online participation" submissions. i.e. to prevent "mass contributions" on the last few days leading to the last submission day. "In-Class" Group Case Study #1 Note: "In-Class" Group Case Study #1 is due on Friday, January 31st by 3 pm via Quercus. Students will work in groups to solve a real-life case study. The case study will be released at 1 pm ET and one (1) person on behalf of their group must upload their group's written answer by 3 pm ET. *** You will also work with the same group members for ALL Group-related deliverables in the course *** Group Mobile Design Project (Due Date: Sunday, February 16th by 11:59 pm). Details will be posted to Quercus. "In-Class" Group Case Study #2 Note: "In-Class" Group Case Study #2 is due on Friday, March 14th by 3 pm via Quercus. Students will work in groups to solve a real-life case study. The case study will be released at 1 pm ET and one (1) person on behalf of their group must upload their group's written answer by 3 pm ET. *** You will also work with the same group members for ALL Group-related deliverables in the course *** Group Virtual Reality (VR) Design Project (Due Date: FRIDAY, March 28th by 11:59 pm). Details will be posted to Quercus. Individual Assignment - Research Portfolio (Due Date: FRIDAY, April 4th by 11:59 pm). Details will be posted to Quercus. Teaching Methods This course is taught through active lectures, including discussion of course readings and coding techniques, with student participation in design and programming activities. Practical sessions will allow students to receive further technical instruction and collaborate on group work. Procedures and Rules E-Culture Policy Only student U of T email accounts should be used for course communication and all emails from students must include the course code in the subject line and should be signed with the full student name and student number. Quercus (https://q.utoronto.ca) will serve as the primary out-of-class communications medium for this course. For all course related questions or comments please use the Quercus site. The instructor will not respond to one-to-one emails regarding the course content or clarification on assignments. This is for the good of the whole class since there are likely others present who will have similar questions to your own. Please note that important course announcements and supplemental information will be posted to the course site in Quercus. Please check Quercus at least once daily for updates. Every effort will be made to answer questions posted to Quercus in two (2) business days. For matters to the attention of the instructor only, it is expected that written communication will originate from your University of Toronto email accounts. All emails from students must START with the course code in the subject line (i.e. "CCT 461"). Remember that most questions not related to content can be answered by consulting a) Course outline; b) Quercus; and c) U of T website. It is your responsibility to read your U of T email on a regular basis. This will ensure that you receive important information from your instructors and the university. It is your responsibility to read your email regularly and check course information updates and announcements through Quercus. Students who choose to opt out of receiving messages through Quercus are still responsible for actions required, or changes communicated through those announcements and messages. Late Penalties You are expected to complete assignments on time. There will be a penalty for lateness of 10% deducted per day and work that is not handed in one week after the due date will not be accepted. Accommodation for Missed Tests and Late Assignments Starting Summer 2024, students must use the new UTM Special Consideration Request [Pilot] application for all ICCIT courses. Students in CCT109H5, CCT110H5, CCT111H5, CCT112H5 and CCT208H5 should always follow the Special Consideration Request (SCR) process outlined below. Students in other classes should follow the process below only if they are seeking accommodation for tests or assignments worth 20% or more of the final grade. Students in other classes seeking accommodation for tests or assignments worth less than 20% should contact their instructors directly. Reasons for special consideration could include: Accident Illness Emergency procedure Bereavement University-sponsored athletics/competitions Compulsory legal duties e.g. (jury duty) Religious accommodations Disability accommodations Reasons for special consideration do not include*: Pre-planned vacations or social commitments Transportation delays Technology malfunctions Time management, course loads Course conflicts, team work conflicts Misreading a deadline/timetable Late course enrolment Scheduled elective medical appointments * For these situations, refer to your course syllabus and speak directly with your instructor. SCR Process: You have three days or 72 Hours (including weekends) from the assignment deadline or date of the missed test/quiz to complete the SCR process in full. The first time in the semester that you are seeking accommodation, please complete the following steps: 1. Login to ACORN, and click on Profile & Settings from the left-hand menu. 2. Click on Absence Declaration 3. Record each day that you are absent – as soon as it begins up until the day you return to campus for classes or other activities. 4. Login to the UTM Special Consideration Request [Pilot] application system and complete the required steps. Documentation is not required. For all subsequent times that you require an accommodation, you should only login to thethe UTM Special Consideration Request [Pilot] application system and complete the required steps. Documentation is not required, however, it may be requested after the SCR has been reviewed. On your SCR form, please attach a screenshot from Quercus showing the assessment title and deadline. This will help us process your SCR accurately and avoid any misunderstandings. Important note about missed makeup tests: As stated in the Academic Calendar, "If the student is granted permission to take a makeup test and misses it, then they are assigned a mark of zero for the test unless the instructor is satisfied that missing the makeup test was unavoidable. Students are not automatically entitled to a second makeup test." If you are registered with AccessAbility at UTM and/or Accessible Learning Services at Sheridan, and the reason for missing a test or a deadline pertains to a disability, you are still required to submit an SCR at the link above. In such cases, the department will accept documentation supplied by the UTM AccessAbility Resource Centre. If you require further information, please speak with your accessibility services advisor. Please see the section on "AccessAbility" for more information. Further details regarding SCR policy are available here: https://www.utm.utoronto.ca/iccit/student-resources/policies-procedures/special-consideration-requests-scr-late-assignments Re-marking Pieces of Term Work General A student who believes that his or her written term work has been unfairly marked may ask the person who marked the work for re-evaluation. Students have up to one month from the date of return of an item of term work or from the date the mark was made available to inquire about the mark and file for an appeal. For example, should the work be returned or the mark be made available on March 3rd, the student has until April 3rd to inquire in writing and start the re-marking process. Instructors must acknowledge receipt of a student request for re-marking within 3-working days, and decisions should be provided in a timely fashion. If an academic misconduct case is in progress for the piece of term work in question, a student may not appeal until the matter is resolved. Details Regrade requests for term work worth less than 20% of the final mark may be submitted to the person who marked the work for re-evaluation. The student must submit (1) the original piece of work and (2) a written explanation detailing why they believe the work was unfairly/incorrectly marked. If the student is not satisfied with this re-evaluation, he or she may appeal to the instructor in charge of the course if the work was not marked by the instructor (e.g., was marked by a TA). In these instances where the instructor was not the one who marked the work, the student must now submit to the instructor (1) the original piece of work, (2) the written reasons as to why he or she believes the work was unfairly/incorrectly marked, and (3) communications from the original marker as to why no change in mark was made. If a re-marking is granted by an instructor, the student must accept the resulting mark as the new mark, whether it goes up or down or remains the same. Continuing with the remark or the appeal means the student accepts this condition. Instructors and TAs should ensure all communication with the student is in writing (e.g. follow-up email) and keep a copy for later reference. Only term work worth at least 20% of the course mark may be appealed beyond the instructor. Such appeals must first follow the same guidelines as those mentioned directly above for work worth less than 20%. To escalate an appeal beyond the instructor, the student must submit to the department (1) all previous communications between the student, original marker, and the instructor (2) the detailed reason(s) documenting why the mark for the work was inappropriate and (3) the original piece of work. If the department believes that re-marking is justified, the department shall select an independent reader. The student must agree in writing to be bound by the results of the re-reading process or abandon the appeal. Again, the student must accept that the mark resulting from the appeal may be higher or lower or the same as the original mark. Where possible, the independent reader should be given a clean, anonymous copy of the work. Without knowing the original assigned mark, the reader shall determine a mark for the work. The marking of the work should be considered within the context of the course of instruction for which it was submitted. If the new mark differs substantially from the original mark, the department shall determine a final mark taking into account both available marks. The final level of appeal is to the Dean’s Office. Appeals must already have been considered at the two previous levels (Instructor followed by Department), with the decision reviewed by the head of the academic unit, before they will be considered by the Dean’s Office. Appeals must be submitted in writing, and include all previous correspondence, as soon as possible after the student receives the final response from the academic unit, but no later than one month after. Appeals to the Dean’s Office about the marking of term work will be reviewed to ensure that appropriate procedures have been followed in earlier appeals, that the student has been treated fairly, and that the standards applied have been consistent with those applied to other students doing the assignment. Any mark resulting from such an appeal will become the new mark, whether it is higher or lower or the same as the previous one. This process applies only to term work; appeals for re-reads of final examinations are handled directly by the Office of the Registrar. Issues Pertaining to Term Work and Instructional Activities Issues arising within a course that concern the pedagogical relationship of the instructor and the student, such as essays, term work, term tests, grading practices, or conduct of instructors, fall within the authority of the department. Students are entitled to seek resolution of these issues, either orally or in writing to the course instructor and, if needed, the ICCIT Director for resolution. Following a response from the ICCIT Director, students may submit an appeal, in writing, to the Vice-Principal, Academic and Dean.
Professional and Research Skills in Practice [PGGE11238] Assessment 2 – Data handling and analysis exercise This assessment is based on the class dataset which consists of results of the survey undertaken by SRUC undergraduates as well as the MSc class. The data can be found in an Excel spreadsheet on Learn. Assignment Aim: To select appropriate data to answer three specified research questions, to present appropriate summaries of the data, and perform. and interpret 3 different and appropriate statistical tests. The data set created from the results of the questionnaire will include both quantitative (eg. pulse rate) and qualitative (e.g. how do you rate your diet) information. Your investigation must include at least one qualitative (categorical) data set and at least one quantitative data set. For your third question you can select a qualitative or quantitative data set. Please make sure that you use three different tests (variations on a type of test count as ‘different’ – for example using a one-way ANOVA for one test, and then a two-way ANOVA for another, is acceptable). You will be assessed on the clarity of your objectives and hypotheses; the appropriateness of data selection and manipulation, descriptive statistics and graphs; choice and execution of appropriate tests; concise, informative and correct presentation; and valid conclusions. For each of THREE investigations in turn: 1. State a research question that could be investigated using these data. The question should be sensible, require some analysis (i.e. not self-evident), and answerable from the data available. You should introduce the topic and state why you have decided to look at this question, and if you have any expectations in terms of the results. A good answer would backup some statements by briefly referring to some literature (though a detailed use of literature is NOT expected). 2. Formulate hypotheses. State appropriate null and alternative hypotheses that can be tested using this data. These should be clearly stated ensuring that your hypothesis is answerable by the statistical analysis that you have performed. 3. Summarise data using descriptive statistics. Identify the data sets relevant to the investigation and present a summary table of descriptive statistics, according to the nature of the data. The analysis can be done in Excel, Minitab or any other software package, but do not simply present statistical output. You should select the statistics relevant to the data and the hypothesis and summarise this as a suitable table in the report. Write a summary of what this data output shows, do not assume that the reader will be able to interpret the summary output by itself. 4. Perform. an appropriate statistical test to test your hypothesis. Use a suitable statistical test to answer your research question. State why you have chosen this test and what assumptions you have made when selecting it. Test of relevant data can be performed in any appropriate software package with the output being summarised in the text. If necessary, make sure you summarise this in a suitable table and do not simply paste software output into the text. Comment on the results in the conclusions to show that you have interpreted the output correctly. 5. Draw appropriate graphs or charts. Produce at least one relevant and appropriate graph orchart showing data. The figures must be properly captioned and include a full explanatory legend if necessary. Graphs can be produced in any software package. 6. Conclusions State your preliminary conclusions based on the descriptive statistics, statistical analysis and visual evidence. How do your results relate to other information that is available? Do your results tend to support the Null hypothesis or not: is the outcome clear-cut or not? Expand on your answer where possible referring to any other literature you may have read (but again, only briefly if necessary). There is no word or page limit for this assessment, but it is suggested that 2 pages per question is likely to be sufficient.
CIS*2750 Assignment 1 Deadline: Monday, February 10, 11:59pm Weight: 16.6% Module 1: Primary functions This is Module 1 of Assignment 1. Module 2 will be much smaller, and will deal specifically with error values and error handling. It will be released in about a week. Description Our overall project, discussed in Lecture 1, will span Assignments 1 - 3. It will be an app written in C and Python that will create class lists from vCard files and do some basic class and grade management. In this assignment, you will implement a the first component of this app - a library to parse vCard files. The link to the format description has been posted in the Assignment 1 description. Make sure you understand the format before doing the assignment. According to the specification (RFC 6350), a vCard object contains: - one specific required property that must appear at least once - multiple optional properties, which may contain parameters Our Assignment 1 parser will assume a somewhat simpler vCard file: - You can always assume that the parameters of a property (if any) will NOT contain the ":" character. In other words, you can rely on having the ":" character separate the value(s) of a property from everything else This structure is represented by the Card type in VCParser.h. According to the specification (RFC 6350), a property component contains: - 1 or more values - 0 or more parameters These elements are represented by the Property and Parameter types in VCParser.h. Property values are strings. The header file VCParser.h also contains a type for representing the date-time property, a couple of helpful definitions, and the various standard C headers you will need. Your assignment will be graded using an automated test suite, so you must follow all requirements exactly, or you will lose marks - and possibly get a zero on the assignment. Required Functions Read the comments in VCParser.h carefully. They provide additional implementation details and are an important part of the documentation for this assignment. You must implement every function in VCParser.h; they are also listed below. If you cannot complete any of the functions, you must provide a stub for every one them. Applications using the parser library will include VCParser.h in their main program. The VCParser.h header has been provided for you. Do not change it in any way. VCParser.h is the public “face” of our card API. All the helper functions are internal implementation details, and should not be publicly/globally visible. When we grade your code, we will use the standard VCParser.h to compile and run it. If you create additional header files, include them in the .c files that use them. Card parser functions VCardErrorCode createCard(char* fileName, Card** newCardObject); This function does the parsing, allocates a Card struct, and initializes it. It accepts a filename and an address of a Card pointer (i.e. a double pointer). If the file has been parsed successfully, the card object is allocated and the information is stored in it. The function then returns OK if the file is successfully parsed and the Card object is created. However, the parsing can fail for various reasons. In that case, the obj argument is set to NULL and createCard returns a variety of error codes to indicate this. What VCardErrorCode value should be returned in what situation will be discussed in Module 2. For now, you can return OK if the file is successfully parsed, and INV_FILE otherwise. Regarding file extensions - both .vcf and .vcard are stated in the specification, so both must be accepted by the parser. All other file extensions must not be accepted. char* cardToString(const Card* obj); This function returns a humanly readable string representation of the entire card object. It must not modify the card object in any way. The function must allocate the string dynamically. The format of the output string is up to you - it will be useful to you when debugging your own code. void deleteCard(Card* obj); This function deallocates the Card object - i.e. frees all memory associated with it, including all of its subcomponents. const char* errorToString(VCardErrorCode err); This function is meant to make the error codes more humanly readable. It returns a string based on the VCardErrorCode value to make the output of your program easier to understand - i.e. “OK” if err is OK, “INV_FILE” or “invalid file” is INV_FILE was passed, etc.. The function must allocate the string dynamically. Additional details will be provided in Module 2. Helper functions In addition the above functions, you must also write a number of helper functions. We will need to store the types Property, Value, and Parameter in a list. We will also need to print and delete DateTime values, and may need to compare them in future assignments: void deleteProperty(void* toBeDeleted); int compareProperties(const void* first, const void* second); char* propertyToString(void* prop); void deleteParameter(void* toBeDeleted); int compareParameters(const void* first, const void* second); char* parameterToString(void* param); void deleteValue(void* toBeDeleted); int compareValues(const void* first, const void* second); char* valueToString(void* val); void deleteDate(void* toBeDeleted); int compareDates(const void* first, const void* second); - this function can be a stub for now, e.g. always return 0. We will flesh it out later, when we need it. char* dateToString(void* date); Additional guidelines and requirements It is strongly recommended that you write additional helpers functions for parsing the file - e.g. parsing a property, parameter, or a date-time. You should also write delete...() functions for all the structs, since they will all be dynamically allocated. All required functions must be in files prefaced with VC - e.g. VCParser.c, VCHelpers.c, etc.. You are free to create your additional "helper functions" in a separate .c file, if you find some recurring processing steps that you would like to factor out into a single place. Do not place headers for these additional helper function in VCParser.h. They must be in a separate header file, since they are internal to your implementation and not for public users of the utility package. This separate header file also must be prefaced with VC, e.g. VCHelpers.c. For your own test purposes, you will also want to code a main program in another .c file that calls your functions with a variety of test cases, but you won't submit that program. The file containing the main function must not have the VC prefix. Do not put your main() function into any of the files with the VC prefix. Otherwise, the test executable will fail due to multiple definitions of main(); you will lose marks for that, and may get a zero for the assignment. Your functions are supposed to be robust. They will be tested with various kinds of invalid data and must detect problems without crashing. If your functions encounter a problem, they must free all memory and return. Function naming You are welcome to name your helper functions as you see fit. However, do not put the underscore (_) character at the start of your function names. That is reserved solely for the test harness functions. Failure to do so may result in run-time errors due to name collisions - and a grade of zero (0) as a result. Linked list You are expected to use a linked list for storing various vCard components. You can use the list implementation that I use in the class examples. You can also use your own. However, your implementation must be compliant with the List API defined in LinkedListAPI.h. Failure to do so may result in a grade deductions, up to and including a grade of zero. Recommended development path: 1. Start by implementing a simple parser that extracts the FN property 2. Add a basic deleteCard functionality and test for memory leaks 3. Add a basic cardToString functionality and test for memory leaks 4. Add handling of multiple optional properties with single values 1. Update deleteCard and cardToString functions 2. Test for memory leaks 5. Add handling of properties with compound values 1. Update deleteCard and cardToString functions 2. Test for memory leaks 6. Add line unfolding. Line unfolding is part of the format specification and you must implement it to receive full marks. 1. you guessed it 2. you guessed it 7. Add handling of properties with parameters 1. Have a beer, and ignore the rest of the assignment. 2. Just kidding. Yes, keep updating delete/toString functions, and testing for leaks 8. Implement proper error code returns (Module 2) 9. Add errorToString functionality 10. Test for memory leaks Important points Do: - Do be careful about upper/lower case. - Do include comments with your name and student ID at the top of every file you submit - Do use the VC prefix for all .c and .h files that you wan to include in your graded A1 Do not: - Do not change the given typedefs or function prototypes VCParser.h - Do not hardcode any paths or directory information into #include statements, e.g. #include "../include/SomeHeader.h - Do not put the main() function into any of the files with the VC prefix. - Do not exit the program from one of the parser functions if a problem is encountered, return an error value instead. - Do not print anything to the command line. - Do not assume that your pointers are valid. Always check for NULL pointers in function arguments. Failure to follow any of the above points may result in loss of marks, or even a zero for the assignment if they cause compiler errors with the test harness. Submission structure The submission must have the following directory structure: assign1/ - contains the Makefile file. assign1/bin - should be empty, but this is where the Makefile will place the shared lib files. Note: be careful if you use Git, since it can ignore empty directories, and this can mess up your submission assign1/s rc - contains VCParser.c , LinkedListAPI.c, and your additional source files. assign1/include - contains your additional headers. Do not submit VCParser.h and LinkedListAPI.h. If you do, they will be deleted and replaced with the standard ones that are posted on the course website. Makefile You will need to provide a Makefile with the following functionality: - make parser creates a shared library libvcparser.so in assign1/bin - make clean removes all .o and .so files - You are welcome to add additional targets for your own purposes, e.g. testing. We will not run them. Evaluation Your code will be tested by an automated harness. You will submit a Makefile, which will be used to produce a shared library. This library will then be tested on the standard CIS*2750 Docker images by the TAs, who will use a precompiled executable file containing the test harness, as well as another executable file with simple memory leak tests. Your library must implement the assignment API exactly as specified, or you will get run-time errors because the executable files will not find functions in the library that they expect. Your code must compile, run, and have all of the specified functionality implemented. Any compiler errors will result in the automatic grade of zero (0) for the assignment. Infinite loops will also result in a grade of zero (0). Marks will be deducted for: - Incorrect and missing functionality - Deviations from the assignment requirements - Run-time errors, including infinite loops - Compiler warnings - Memory leaks reported by valgrind - Memory errors reported by valgrind - Failure to follow submission instructions, e.g. incorrect archive type, incorrect naming of targets, etc.. Submission Submit your files - source code and Makefile, as described above - as a Zip archive using Moodle. File name must be A1FirstnameLastname.zip. Please do not use any other archive formats, or you will lose marks. Late submissions: see course outline for late submission policies. This assignment is individual work and is subject to the University Academic Misconduct Policy. See course outline for details)
BEEM012 – Problem Set #1 Assignment Overview The goal of these problem sets is to use the tools you learn in your R assignments and apply them to an independent project on time series data of your choice. If you do not have a particular topic of interest, or are unable to find your own interesting data, I have provided a few sample datasets that you can use. Note: You can always subtract one time series from another if you are inter- ested in the diference between two outcomes. For example, we consider the term spread, the diference between long and short run interest rates, in some of our R assignments as a predictor of GDP growth. You can also use this as an outcome, and look at the diference between profits in two diferent sectors as your Yt or Xt or diferences in outcomes for men and women as your Yt or Xt , etc. A Second Note: If you want to use this empirical work as the basis for your dissertation that would be an excellent use of your efort. You should be aware, however, that you cannot submit the exact same report for your dissertation as you submit for this module, and your dissertation would need to contain substantively additional content. The first task is choosing an outcome variable that will be your Yt for your analysis, and a primary Xt that will be the main explanatory variable you explore. Once you have chosen some data of interest, the first part of this assignment will involve using the tools we learned in the first part of the module up to but not including Dynamic Causal Efects. You will complete the analytical tasks outlined below by adapting the code provided in R tutorials and write up an explanation of the task and the results. Grading Criteria Your assignment will be assigned a grade based on three equally-weighted cate- gories: • Interpretation and Understanding of Econometric Tools Part of your grade will be based on whether you correctly use and interpret the tools of Time Series Econometrics that we learned. This means that you use the appropriate models for the given task, that you interpret results correctly, using the proper critical values for inference as well as interpreting null hy- potheses correctly. This also depends on whether you explain why you use diferent tools, and the problems these are selected to deal with. • Programming and R Code Part of your grade will depend on correctly using R to implement the tasks you are assigned and whether your R code correctly implements the work that you describe in the write-up of your as- signmnet. Marks will be given for R code that is correct, and with comments to clarify you understand the tools you are using. • Economic Analysis and Discussion This part of your grade will depend on the economic analysis of your results and the depth of your discussion. Marks will be given for the economic content of your analysis and your interpretation of the economic reasoning of your results. Assignment Outputs to Submit • A write-up of the results of your analysis, including graphs and tables. See the outline of the analysis tasks to complete below for details on exactly what tables & graphs you need to complete. Word Count: Maximum 1,000 words. Your R script. for the assignment should be copied and pasted at the end of your write up. Analysis Tasks to Complete 1 Descriptive Analysis – Week 1 Exercises Before running regressions, we will first examine our data and use some simple tools to look at the time series. 1.1 Data Description First, write a very brief (just a few sentences) description of the outcome variable you are interested in analysing. Next write a brief description of your primary explanatory variable, and the rough research question. What is your hypothesis about the relationship between Yt and Xt 1.2 Time Series Plots Next, plot your Yt time series., and give a few sentences of description. Does it appear to have a trend? Does it appear to be highly autocorrelated? Are there any important outliers you need to remove? 2 Autoregression Analysis of a Time Series 2.1 Estimate an Autoregression Model • First, run an AR(1) regression of your outcome variable. Then use the Bayes Information Criterion to select the appropriate lag length for your model, setting a maximum of four lags. Write down the four values of the BIC(p) you calculate, and explain which model length you end up selecting. Now, estimate this model. (See Week 1 & 2 Exercises) • Next, test for violations of our key Time Series Assumptions: (See Week 3 Exercises) – Use the appropriate model to test for a unit root process. Does economic theory suggest that your time series should exhibit a roughly linear time trend? Justify your answer briefly, and explain what this means for the model you use for this test and the hypotheses you test. Write a brief explanation of the result, and what this means for your time series. If you conclude your time series has a unit root, perform. the necessary transformation and add this model to your table. – Use the appropriate test for a break in your time series where you don’t know the exact date of the break. Write a brief explanation of the result, and what this means for your time series. If you conclude your time series has a break and you identify the likely break date, make the necessary adjustment to your model and add this model to your table. • Report estimated coefficients from both the AR(1) and AR(p) models in a table, along with the coefficients from your modified model in the case that your time series either has a break or a unit root. If your BIC results select an AR(1), then present the results of an AR(2) as well to compare. • Is the coefficienton Yt-1 in yourAR(1) significant? Write a brief explanation of whether it is statistically significant, and an additional brief interpreta- tion of the economics of this result. How about the coefficient Yt-1 in your AR(p) model - is it similar? Discuss the implication of these results, and the persistence of shocks. If you correct for a trend or a break, discuss how your analysis of the non-transformed time series might be misleading. 2.2 Estimate an Autoregressive Distributed Lag Model - Week 2 Exercises • Now we are going to introduce a second variable Xt. First, estimate an ADL(1,1) model. • Repeat the exercise you conducted above using the BIC to select the length of lag, but now you will select a lag p to use for your ADL(p,p) model. For simplicity, consider again up top = 4 and use the same lag length for Yt and Xt. • Use a Granger causality test to test whether the lags of your explanatory variable Xt are jointly significant predictors of Yt. Report the test statistic in the text (no need to add it to a table). If your selected model was an ADL(1,1) then estimate an ADL(2,2) so you can jointly test the two lags of Xt. • Produce a table with your coefficient estimates from the ADL(1,1) model as well as the ADL(p,p) model. Again, if you selected an ADL(1,1) then use an ADL(2,2) to compare. • Interpret the results from the above. Are the lags of Xt jointly predictive of Yt in a model where we also include lags of Yt? Discuss the economic significance of this result. 2.3 Check Out-Of-Sample Forecast Performance – Week 4 Exercises • Using the Pseudo Out-Of-Sample forecasting method, with your ADL(1,1) model and with the final 25% of your sample as your excluded sample, and compare the within-sample SER (from the regression including none of your excluded observations) and the out-of sample fit using your estimate of the Root Mean Squared Forecast Error. • Compare the size of the SER to the size of your RMSFE. Which is larger? Does this suggest your forecast errors are larger, smaller, or the same as your within-sample errors? Is your model capable of predicting out-of-sample?
