CIS 4011-N Research Methods Learning Outcomes Personal and Transferable Skills 1. Communicate complex academic issues effectively to specialist and non-specialist audiences. 2. Defend the rationale and decisions made for the research proposal. Research, Knowledge and Cognitive Skills 3. Select appropriate research strategies and data generation methods for a computing context, and critically evaluate their effectiveness within the development and evaluation of computing-related artefacts. 4. Use a systematic search, analysis, synthesis and critique of the literature within computing to articulate how their work, or planned work, contributes to knowledge within the computing field. 5. Design a research proposal to address significant areas of computing-related theory and/or practice. Professional Skills 6. Critique the professional, legal, and ethical implications of their work within a computing context. Assessment Strategy Assessment will be by means of a single individual in-course assessment comprising: Element 1: A literature search report on a chosen topic (2,500 words) (40%) LO 4. Element 2: A poster outlining an individual proposal for a masters project that could be undertaken in the final stage of a masters programme (60%) LOs 1, 2, 3, 5 and 6. Assessment Criteria Literature Search Report (40%): · Description of a repeatable search process · Appropriate searching techniques · Review of literature · Limitations of the literature review Poster of Project Proposal (60%): · Research question & anticipated product(s) · Background context of the proposed research · Research Methodology · Evaluation Methodology · Schedule and dissemination plans · Professional, legal and ethical issues Element 1: Literature Search Report (2500 words) 40% Identify a computing related research topic or area and carry out a systematic search of Teesside University library’s online databases to identify relevant peer-reviewed academic journal or conference papers (not books). The topic you select for this element will be used for your project proposal (Element 2 of this ICA). Your list of papers should include some primary research, not just articles that summarise the literature or give a personal opinion. Prepare a report to describe your full literature search and review process, including the following: Report Heading Description Introduction Short introduction to your topic (a couple of paragraphs. What area are you interested in? Why do you think it will make a good research topic? Search Results Ideally in table format with columns for keywords, filters, number of results and notes. Here you should show your skill in using the databases. · Specify the database that you use for each search. · Say why you have chosen each database. · Aim to search at least 3 databases. · For each database, continue to refine your results until you have 30 or less. · Notes should contain your thoughts on the number of returned results and your reasoning behind your next search terms to expand/reduce the number of matches. Criteria Define criteria for determining which papers will be included or excluded from the final list. Consider the relevance of each paper identified in the short list (from the searches) and state the reasons (based on the criteria) for including or excluding from the final list. At the end of your literature search you will have a number of papers e.g. 30 or less from each database you searched. This is your short list. You must now decide which papers to include in your final list, and which to exclude. Your final list should contain the 10-30 papers that are most relevant to your chosen research topic. Conclusions What did you learn about doing a systematic literature search? How well do you feel the literature search went? Did it go as you expected? What difficulties did you have? How easy did you find the different databases to use? What limitations did you encounter? Reference List A reference list with full reference details for your final set of papers, Teesside University Harvard style.: https://libguides.tees.ac.uk/computing or Bibtex if using the Latex editor. Element 1, your literature search report, should be a maximum of 2500 words excluding references. Upload your report (.docx or .pdf) to TurnItIn via the Element 1 submission area for this module on Blackboard. Assessment Marking Criteria Element 1 Element 1 Literature Search Report (40%) Introduction · Is the topic chosen clearly identified? · Is the topic/research area well explained? Search · Is there a clear description of a systematic and iterative process? Is the whole process traceable? · Is the literature search adequate? E.g. use of appropriate keywords, filters, Booleans and suitable databases/sources. Have 3 (or more) databases been searched? · Has choice of databases been justified? · Have results been narrowed down to 30 or less for each database? · Have notes been included to explain decisions made? InclusionExclusion Criteria · Are appropriate criteria for including/excluding particular papers identified and applied? · Does the final list include empirical research? Conclusions and Limitations · Are valid conclusions drawn about the results of the literature search? · Are the limitations of the search well identified? References List · Are references in Harvard/Bibtex format as appropriate? · Are all elements in the final list of references correct & complete? Overall Professional Quality · Is there a good standard of report writing? E.g. are all elements written in good English, spell-checked & proof-read? · Does the report conform. with the specified content? Element 2: Research Proposal Poster 60% Create a research proposal poster, which could form. the basis of your master’s project. Your research poster must be based on the topic you chose in Element 1. Your poster should be A3 size. You may use any software to create the poster but the final version must be in pdf format. Your poster will contain a research proposal, a plan for a masters project. It can form. the basis of your masters project but does not have to. There is no word count for the poster but note that you should aim to minimise the word count. This is not a report. Your proposal should mostly draw upon the papers that you found in Element 1 (but you can include additional references). It should contain the following: Poster Heading Description Research Purpose Introduce and explain the research’s purpose: · Short introduction to your topic · Your overall research question that will be addressed by the research · Any research objectives · You must also define the anticipated product(s) i.e. the anticipated contribution to knowledge Background Context Justify the need for your research question. · Why is your research important? · What were your main findings from your final list of papers from element 1. Summarise them for the poster. · Cite the papers that you discuss. Research Methodology Identify and justify: · One or more research strategies that you will use · The data generation methods that you will use · If appropriate, your data analysis techniques Evaluation Methodology Justify how you plan to evaluate your end product(s)/contribution to knowledge. How will you check the usefulness of your findings/artefact/product to your target audience? Note that masters projects are expected to include third party evaluation of the end product if at all possible. Identify and justify: · One or more research strategies that you will use · The data generation methods that you will use · If appropriate, your data analysis techniques Schedule and Publishing · Identify the set of tasks, the timescale for each task and hence the timescale for the overall research which captures your schedule visually. · Allow 13 weeks (or 26 weeks if your studying part-time) from ‘project start’ to ‘hand-in of all project work’. Projects normally include a viva that should not be included in the schedule as it is normally after the hand-in. · Identify the academic journal or conference for which you will prepare your project’s final research paper. · You can use a project planning tool e.g. produce a GANTT chart to show your tasks. Professional, legal and ethical issues · Professional, legal and ethical issues that your proposal raises, and how they will be addressed. Upload your poster (.pdf) to Turnitin via the Element 2 submission area for this module on Blackboard. Assessment Marking Criteria Element 2 Element 2 Project Proposal Poster (60%) Research Purpose · Are the purpose of the project, · the overall research question · and the anticipated product(s) · all clearly defined and appropriate to masters level? Background Context · Have the main findings from the papers found in element 1 been summarised? · Is there a clear justification for the need for the project? · Is there good use of references, cited correctly? Research Methodology · Is an appropriate research methodology clearly described and justified? · Are strategy, data collection methods and data analysis techniques identified? Evaluation Methodology · Is there an appropriate plan (evaluation methodology) to evaluate your end product(s)/contribution to knowledge? · Is the evaluation methodology clearly described and justified, with 3rd party evaluation if possible? · Are strategy, data collection methods and data analysis techniques identified? Schedule and dissemination plans · Are all the main tasks identified? · Is there an appropriate schedule for them? · Has an appropriate journal or conference been identified? Professional. legal and ethical issues · Are all relevant professional, legal and ethical aspects clearly identified and well addressed? Poster overall quality · Is there a good standard of report writing? E.g. are all elements written in good English, spell-checked & proof-read? · Is the poster visually appealing and does it flow well? · Is there a suitable amount of text i.e. has content been summarised suitably for a poster? · Have diagrams been used appropriately?
List of Project Topics L1094 Applied Finance Project Spring Term 2024/2025 Students are required to read the research topics of Applied Finance Project that are listed below and choose three topics in order of their preferences, then submit the topic preference form available on the Units view on the Canvas site for this module by the following deadline: 12pm Monday in Week 2 (3th February 2025) Preferences are respected on a first come first served basis. Note that more than one student can do the same topic title if, for instance, it is applied to another case study or context. The projects will be allocated once we receive the topic preference forms, and each student will then be allocated a supervisor and notified to this effect by the end of Week 2 of the Spring Term. Each topic has a title, a short description, suggestions on how to conduct research, and a short list of key references. This is meant to provide an introduction to the topics only and students may want to go beyond these core reading. Students can identify additional reading using, for example, the Library’s on-line searching facilities or Google. In consultation with project supervisors, students may focus their research on a particular direction and/or exploit different sources of data. In the topic list there are often several topics which are related (e.g., doing the same project but for different datasets which may be different countries or time periods). It might be useful to cross-reference to the related topics as there may be useful suggestions on methods, data and readings in broadly similar topics. 1. Are Chinese Stock Markets Efficient? Outline: There are three major stock markets now active in China: Hang Seng (Hong Kong), Shenzhen and Shanghai. The purpose of this project is to determine whether the stock markets are efficient. In other words, do the returns follow a random walk? Methodology: The project will require use of a variety of unit root tests and cointegration econometric techniques. A good paper that describes the background to the stock markets in China is Chan et al. (2007), and the empirical methodology to be used is described in Liu et al. (1996). Data: The period of interest and the frequency of the data (e.g., daily, weekly time intervals) can be determined in consultation with you supervisor. Data is available on we sites of stock exchanges or Yahoo Finance. Reading Chan, K., H. Fung and S. Thapa, 2007. “China Financial Research: A Review and Synthesis.” International Review of Economics and Finance 16 (3): 416-428. Liu, X., H. Song and P. Romilly, 1996. “Are Chinese Stock Markets Efficient? A Cointegration and Causality Analysis,” Applied Economics Letters 4 (8): 511-515. 2. Does Activity on the Hang Seng Stock Market Lead the Shanghai and Shenzhen Stock Markets? Outline: There are three major stock markets now active in China: Hang Seng (Hong Kong), Shenzhen and Shanghai. The project will investigate whether the movements in the Hang Seng market determine or lead the activity in the others. Methodology: This will involve use of causality testing techniques from econometrics. A good paper that describes the background to the stock markets in China is Chan et al. (2007), and part of the empirical methodology to be used is described in Liu et al. (1996). Data: The period of interest and the frequency of the data (e.g., daily, weekly time intervals) can be determined in consultation with the supervisor. Data is available on the stock markets website or Google Finance. Reading: Chan, K., H. Fung and S. Thapa, 2007. “China Financial Research: A Review and Synthesis.” International Review of Economics and Finance 16 (3): 416-428. Liu, X., H. Song and P. Romilly, 1997. “Are Chinese Stock Markets Efficient? A Cointegration and Causality Analysis,” Applied Economics Letters 4 (8): 511-515. 3. Is there a Relationship between Stock Market Returns and Consumer Confidence in China? Outline: Stock markets have become an increasingly important part of China’s economy over the last two decades. The re-opening of the Shanghai stock market and the inauguration of the Shenzhen stock market in the early 1990s was partly motivated by China’s need for private capital. There is a literature on the relationship between the stock market and consumer confidence. There is the possibility of a causal relationships operating in both directions: consumer sentiment influencing stock returns, and stock returns influencing consumer confidence (and hence consumers’ expenditure through either a traditional wealth effect or as an indicator of favourable future economic conditions). This project investigates whether or not there is a short-run relationship between Hang Seng stock market returns and changes in consumer confidence in China. The econometric analysis will use causality testing, among other approaches, to determine and interpret the direction of the relationship. Data: Data regarding consumer confidence can be collected from the Organisation for Economic Development (OECD), comprised of values from the Westpac-MNI China Consumer Sentiment Index. Data on the Hang Seng (or Shanghai) stock market can be found on Yahoo Finance or other financial databases. Reading Jansen, W. K. and N. J. Nahuis, 2003. “The Stock Market and Consumer Confidence: European Evidence,” Economics Letters 79 (1): 89-98. Otoo, M. W., 1999. “Consumer Sentiment and the Stock Market,” Finance and Economics Discussion Series, Federal Reserve Board. 4. What Is the Effect of Good and Bad “News” on Stock Market Prices? Outline: The recent BP oil spill in the Gulf of Mexico is by far the largest oil spill in the history of the oil industry. Aside from the environmental and economic impacts on people in and around the Gulf of Mexico, what effect did it have on share prices of BP, and other large oil companies? We might expect that the share price was affected by the release of news about the severity of the spill and clean up costs (i.e. new information) and also by public anti-BP statements by Obama and others. Were other oil companies affected, and by how much compared to BP? The effect of good and bad news on share prices is a particular area of study for economists interested in the efficient market hypothesis. How did the SARS outbreak in China in April 2003 affect the Hang Seng? How did the fake powdered milk case in Fuyang in 2004 affect stock market confidence in China and more specifically shares in food processing or food producing companies? What about the ill health and eventual death of Steve Jobs – how was the Apple share price affected? You could investigate the effect of news of a merger, or collapse of a deal, discovery of a new resource, outbreak of war or peace etc, election of a new party to government etc. Rather than follow an individual firm’s share price, you could examine an overall share price index and examine the effect of “news” on its value. This topic can potentially be chosen by several students, as long as each student chooses their own firm and news event (or events). Methodology: A very simple approach would be to regress the daily share price on say, the market interest rate, and one or more dummy variables that capture “news” events. You will find that the literature on stock market efficiency has more sophisticated models. Start with something simple and then build on that. Data: Share price data are easily accessible online and if you have relevant cases you can choose a Chinese stock market. You need to decide if you want daily prices or something averaged over a period, or measures of volatility that you will construct form. the raw price data. You need to make sure you cover a sufficiently long period before and after the “events”. You might also want to access share price data on either the relevant industry as a whole, or of main rivals, to test if the share price you are following moved in line with other firms. You will need to construct 'news' events that might have affected the share price – good and bad news. This may require piecing together lots of info from media coverage. You may find it useful to construct a timeline of events (speeches, press releases, real events) and see if any of those are followed by drops or rallies in the share price. Of course, some news may have been good. Reading Cebula, Richard J., James V. Koch and Robert N. Fenili, 2011. “Do Investors Care if Steve Jobs is Healthy?” Atlantic Economic Journal Special Issue: Current Issues in Financial Economics" 39 (1): 59-70. Copeland, L. S., 1989. “Market Efficiency before and after the Crash,” Fiscal Studies 10(3): 13-33. Firth, M., 1986. “The Efficient Markets Theory”, in M. Firth and S. M. Keane (eds), Issues in Finance, Philip Allan. Jackson, P. D. and A. D. O'Donnell, 1985. “The Effects of Stamp Duty on Equity Transactions and Prices in the UK Stock Exchange,” Bank of England Discussion Paper 25. Thaler, R. H., 1987. “Anomalies”, Journal of Economic Perspectives 1(1)” 197-201 and 1(2): 169-178. On oil spills (pre-BP Gulf of Mexico): Alsop, R. J., 2004. “Corporate Reputation: Anything but Superficial – The Deep but Fragile Nature of Corporate Reputation,” Journal of Business Strategy 25 (6): 21-29. Alli, K., S. Thapa and K. Yung, 1994. “Stock Price Dynamics in Overlapped Market Segments: Intra and Inter-Industry Contagion Effects,” Journal of Business Finance & Accounting 21 (7): 1059–1070. Anthony, H., J. Marshall and J. Wingender, 1996. “An Analysis of the Stock Market Response to the Exxon-Valdez Disaster,” Global Finance Journal 7(1): 101-114. 5. What are the Determinants of Remittances by Migrants in China? Outline: For this project the student will investigate what factors determine the proportion (rather than the size) of annual labour market income that male Chinese migrants remit home each year. Methodology: The project will require use of OLS regression analysis where the share of annual income remitted home each year will be regressed on a set of individual level demographic and other factors (e.g., age, educational level, sector of work, time spent as a migrant etc.). The project will thus permit an investigation of the key motivations for these transfers. The theoretical issues governing individual remittances are contained in the two readings below and the references cited in them. Data: Data is available on the module Canvas site. Reading Carling, J., 2008. “The Determinants of Migrant Remittances.” Oxford Review of Economic Policy 24 (3): 582-599. Liu Q. and B. Reilly, 2004. “Income Transfers by Chinese Rural Migrants: Evidence from Jinan.” Applied Economics 36 (12): 1295-1314. 6. The Impact of Oil Prices on Asset Prices Outline: This project will examine the impact of oil price changes and/or its volatility on asset prices such as stock returns and/or exchange rates. Data and Methodology: Time series of oil prices, stock prices and exchange rates can be obtained from many sources, including Thomson Reuters Eikon and Datastream. The tests and econometrics methods employed in this project include descriptive statistic, correlations, and regression analysis. More advanced time series models such as cointegration analysis and volatility models can also be used. Reading Filis, G., S. Degiannakis and C. Floros, 2011. “Dynamic Correlation between Stock Market and Oil Prices: The Case of Oil-Importing and Oil-Exporting Countries.” International Review of Financial Analysis 20 (3): 152-164. Kilian, L., and C. Park, 2009. “The Impact of Oil Price Shocks on the U.S. Stock Market.” International Economic Review 50 (4): 1267-1287. Li, S., H. Zhu and K. Yu, 2012. “Oil Prices and Stock Market in China: A Sector Analysis using Panel Cointegration with Multiple Breaks.” Energy Economics 34(6): 1951-1958. Lizardo, R. and A. Mollick, 2010. “Oil Price Fluctuations and U.S. Dollar Exchange Rates.” Energy Economics 32 (2): 399.408. 7. What Determines the Price of Gold? Outline: Gold is an asset that is sometimes interpreted as a long-run ‘hedge’ against inflation and short-run ‘hedge’ against exchange rate movements. The fact that the volume of gold mined is minimal, increases in global real income and the demand for jewellery may also exert an impact on gold prices. In addition, gold prices also respond to extreme economic crises and political risks. Thus, it would be useful to model the relationship between movements in gold prices and US inflation and exchange rates, global income, and any relevant political or other global events that have occurred. Methodology: The empirical methodology use cointegration techniques and an appropriate specification would be in the form. of an error correction mechanism (ECM) model that allowed an investigation of the short-run and long-run effects Data: It is suggested that you use annual data for this project starting as early as possible (e.g., the early 1960s). The gold prices can be downloaded from the London Bullion Market Association homepage. Consult the Oxford Economics Report (2011) in the readings below for information on the data sources for other macroeconomic variables. Reading Oxford Economics Report, 2011. “The Impact of Inflation and Deflation on the Case for Gold,” A Report Commissioned for the World Gold Council, Oxford Economics. Sumner, S., R. Johnson and L. Soenen, 2010. “Spill-over Effects between Gold, Stocks and Bonds,” Journal of Centrum Cathedra 3 (2): 106-120. 8. The Role of Technical Indicators in Forecasting the Crude Oil Price Changes. Outline: This project will investigate the ability of a variety of technical indicators and macroeconomic fundamentals to forecast crude oil price changes. Methodology: This will involve the use of linear regression techniques. A good paper that can be used as a background reading is Neely et al. (2014). Data: Students will have to collect their own data. Reading Neely, C. J., D. E. Rapach, J. Tu and G. Zhou, 2014. “Forecasting the equity risk premium: The role of technical indicators.” Management Science 60 (7): 1772-1791. 9. Do Retail Petrol Changes Respond Asymmetrically to Crude Oil Price Changes? Outline: There is a view by consumers that retail petrol prices at the pump respond asymmetrically to crude oil price changes. The argument is that retail prices rise like a rocket when crude oil prices increase but fall like a feather when crude oil prices decline. The purpose of this project is to investigate the evidence for this proposition using monthly retail price data from the UK over the last 20 or so years. Data: Students will have to collect their own data on their selected country. Methodology: The basic empirical methodology will be OLS but the analysis is best situated within an error correction mechanism (ECM) model framework. For example, the template to use to start with could be Reilly & Witt (1998) below. Reading Reilly, B. and R. Witt, 1998. “Petrol Price Asymmetries Revisited,” Energy Economics 20 (3): 297-308. Frey, G. and M. Manera, 2007. “Econometric Models of Asymmetric Price Transmission,” Journal of Economic Surveys 21(2): 349-367. 10. Corporate Governance and Capital Market Responses: Does Good Corporate Governance Affects Market Valuation by Investors? Outline: This study examines the relationship between corporate governance e and firm performance in the UK pre and during the financial crisis. The rationale for an association between corporate governance and firms’ performance arises because better governance enhances efficiency in the monitoring of managerial activities. This in turn, encourages managers to pursue value-maximizing projects and to avoid expropriation of firms’ resources such as perquisites consumption (Love, 2011). In addition, better governance increases investors’ protection by limiting expropriation of firms’ resources from the majority shareholders (La Porta et al., 2002; Lemmon and Lins, 2003). Data: The data covers corporate governance and financial information of a sample of UK non-financial companies listed on London Stock Exchange (LSE) over the period pre and during crisis, or the student will have to collect their own data on their selected country. Reading Li, C., Li, J., Liu, M., Wang, Y., and Wu, Z., 2016. “Anti-misconduct policies, corporate governance and capital market responses: International evidence”. Journal of International Financial Markets, Institutions & Money 48: 47-60. Essen, M., Engelen, P. and Carney, M., 2013. “Does ‘Good’ Corporate Governance Help in a Crisis? The Impact of Country‐and Firm‐Level Governance Mechanisms in the European Financial Crisis.” Corporate Governance: An International Review 21(3): 201-224. Erkens, D., Hung, M. and Matos, P., 2012. “Corporate Governance in the 2007–2008 financial crisis: Evidence from financial institutions worldwide.” Journal of Corporate Finance 18: 389-41. Leung, S., and Horwitz, B., 2010. “Corporate governance and firm value during a financial crisis.” Review of Quantitative Finance and Accounting 34(4): 459-481. 11. International Trade and Its Impact on Growth Outline: Globalisation has been one of the most important and controversial issues in the twenty-first century. As an indicator of globalisation, trade remains as a major branch of economics. Students in the past explored growing trade between industrial and developing countries, the effects of globalisation on growth, pollution and poverty, problems of international trade policy. Data and Methodology: Students are expected to choose countries and time periods of interest. Time series of openness and growth can be obtained from many sources including the World Bank Development Indicators. Time series analysis include descriptive statistics, correlation coefficients, unit root tests. Reading Heckscher, E. and Ohlin, B., 1991. Heckscher-Ohlin Trade Theory, translated, edited, and introduced by H. Flam and M. Flanders, Cambridge: MIT Press. Krugman, P. and Lawrence, R., 1994. “Trade, Jobs and Wages”, Scientific American, 270: 44-49. Leamer, E., 1980. “The Leontief Paradox, Reconsidered”, Journal of Political Economy, 88: 495-503. Frankel, J. and Romer, D., 1999. “Does Trade Cause Growth?” American Economic Review 89 (3): 379-399. 12. On the Link between Stock Prices and Exchange Rates Outline: There are two main types of theoretical models analysing the linkages between exchange rates and stock prices. The traditional approach based on ‘flow-oriented’ models posits that causality runs from the former to the latter, whereas the portfolio approach based on ‘stock-oriented’ models suggests the opposite. This project will examine the linkages between stock prices and exchange rates within national economies to further test these theories. Data and Methodology: Time series of stock prices and exchange rates can be obtained from many sources, including Thomson Reuters Eikon and Datastream. The tests and econometrics methods employed in this project include descriptive statistic, correlations, and regression analysis. More advanced time series models such as cointegration analysis and volatility models can also be used. Reading Caporale, G.M., Hunter, J., and Menla Ali, F., 2014. “On the linkages between stock prices and exchange rates: Evidence from the banking crisis of 2007-2010.” International Review of Financial Analysis 26: 150-170. Phylaktis, K., and Ravazzolo, F., 2005. “Stock prices and exchange rate dynamics.” Journal of International Money and Finance 24: 1031.1053. Ülkü, N., and Demirci, E., 2012. “Joint dynamics of foreign exchange and stock markets in emerging Europe.” Journal of International Financial Markets, Institutions & Money 22: 55-86. 13. Football Finance Outline: The world of football is now replete with high quality financial data at the club level. This includes data on football clubs which are (or have been) quoted on the stock exchange. It also includes many years of club accounts data. It will be possible to take one of the elite clubs which are quoted on the stock exchange to examine the influence of on-field performance on the weekly stock market price. It is also possible to analyse the effect of close competitors in the same league of their rival’s financial performance. Long run financial effects of changes in attendances and TV revenues on club’s profitability can be studied using annual report data from Deloittes. Possible titles to explore this topic include the following: 1) Stock market share price effects of team performance in elite football.* 2) Team Rivalry and performance on football club share price.* 3) The financial redistributive effects of TV revenue in UK professional football. 4) The effect of financial fair play (FFP) of Premier League football. 5) Stadium capacity as a constraint on the demand for football. Note that titles marked with an * are most suitable for Finance students who are predominantly interested in a project which involves stock market share price data. Reading Szymanski, S. 2015. Money and Football: A Soccernomics Guide, Nations Books. Dobson S. and J. Goddard, 2001. The Economics of Football, Cambridge University Press. Maguire, K., 2021. The Price of Football, Agenda Publishing. Peeters, T. and S. Szymanski, 2014. “Financial fair play in European football,” Economic Policy 29 (78): 343–390. Bryson, A., P. Dolton, J. Reade, D. Schreyer and C. Singleton, 2021. “Causal effects of an absent crowd on performances and refereeing decisions during COVID 19,” Economics Letters 198. Deloitte, 2022. “Annual Review of Football Finance 2022”.
Econ 275 (Baseler): Problem Set 2 Total Points: 100 1 Information frictions in the migration decision (40 points) Although incomes are increasing rapidly in many developing countries, this growth has typically been much faster in urban regions. Today, rural workers in Kenya earn about half what their urban counterparts do, even after controlling for differences in education. Why don’t more people move to cities? In January 2017, I ran an experiment to test whether bad information about urban wages restricts migration out of rural villages in Kenya. I identified 497 households in rural Western Kenya who had at least one member of “migration age", i.e., between 18 and 35 years of age. I randomly gave out information about typical earnings in 3 big cities: Nairobi (the capital), Kisumu, and Eldoret. I also told households about employment levels for migrants, common jobs in each city, and food price differences. The dataset migration_survey.dta includes the following data on these 497 households: Variable name Definition Source __c8=1,amountsaved lfeesinpastyearBaselineSurveyb__hh2RespondentageBaselineSurveyb__hh3RespondentgenderBaselineSurveyb__num__rightageNumberofpeopleaged18-35inhouseholdBaselineSurveytreatment=1ifgotlabormarketi YearFollowup
Econ 275 (Baseler): Problem Set 4 ** Due Thursday Apr 17 at the beginning of class** ** Please use a pen and not a pencil if you hand-write.** 1 Credit Market Model Consider the following model of the credit market: A borrower needs to invest W + L = I in a high-yield technology, where W denotes her initial wealth and L her requested loan. The source of capital market imperfection is ex post moral hazard. Namely, once the return σI is realized, the borrower can either repay immediately, or she can stall (voluntarily default). Stalling revenues away from the lender has a cost to the borrower (who has to keep ahead of the lender), and let this cost be a fixed proportion τ of total revenues σI. Finally, if the borrower defaults on her repayment obligation, the lender can invest effort into debt collection. Specifically, assume that a lender who incurs a non-monetary effort cost L · C(p) has probability p of collecting his due repayment rL. 1. What is the borrower’s expected payoff if she defaults and anticipates that the lender will exert monitoring effort level p? 2. Write down the condition such that the borrow will decide not to (strategically) default. 3. Assume that a larger loan increases the value of defaulting to the borrower, and that the lender does not want the borrower to try to default. Show that this implies a limit on how much the lender will be willing to lend at a fixed p (Hint: use your answer to part 2 to find the loan size limit L ∗ such that the borrower will default if L > L* and will not default if L ≤ L*). What happens to this limit when productivity of capital σ goes up and when the interest rate r goes down? Give concise intuitions for your results. 4. Turning to the choice of the optimal monitoring policy p, write down the maximization problem for a risk-neutral lender. Hint: choose p to solve the profit function of the lender when the borrower is trying to default. 5. In the special case where C(p) = −c · ln(1 − p), what p will the lender choose in terms of r? (Hint: you need to differentiate the profit function with respect to p and set this equal to 0. Because the profit function is concave, the highest profit will be achieved where = 0.) 6. Show that with p optimally chosen by the above formula (i.e. assuming C(p) = −c·ln(1−p)), the credit limit does not depend on the interest rate. Give some intuition for your result. 2 Information frictions in the migration decision (part 2) This problem is a continuation of Problem 1 from PS2. In that problem set you measured the impact of an information experiment that informed rural Kenyan households about average urban earnings on migration over the next year. On Blackboard, under the PS4 folder, download two datasets called migration_baseline.dta and migration_endline.dta. Remember the Stata tips from previous problem sets - they apply similarly here. You can find new Stata tips at the end of this problem set. Note that these datasets are for class use only. If you want to use these data for any purpose other than the class, you need to discuss with me first. Questions 1. The baseline dataset includes information collected at baseline, before households were as-signed to treatment or control. The endline dataset includes information collected about 1 year after baseline on the same set of households. It includes information about how many people migrated from the household, where they went, how much money they earned and in what type of job, and other aspects of their lives in the city. To evaluate the impact of the experiment, you will need to merge the baseline and endline data. You should merge using the hh_id variable, which uniquely identifies households in each dataset (see PS3 for merging tips if you’ve forgotten how to merge). How many households are in the baseline dataset? Out of those households, how many are you able to match to endline data? 2. The households which could not be matched to endline data are called “attriters” - households that could not be found for an endline survey. This can happen if the entire household relocates, or refuses to participate in the survey. One potential concern is attrition bias: that the type of households that attrit varies across treatment status. Tell a plausible story that could generate differential attrition in this context. 3. Let’s test for differential attrition formally. Generate a new variable called “attrited” which equals 1 if the household is not in the endline survey, and 0 if it is in the endline survey (Hint: when a household was not administered an endline survey, all their endline data will be missing). Test whether the attrition rate significantly different between treatment and control. Report and interpret your result. 4. Are the characteristics of the non-attriters comparable across treatment and control? In other words, do we have “balance” if we compare the baseline characteristics (each baseline variable listed in the table above) of those individuals observed at endline? 5. An important debate in economics is whether the allocation of workers across space is efficient. Ignoring for a moment differences in amenities, the implication of an efficient equilibrium al-location is that workers who move across space will not earn more. In other words, the marginal return to migration is 0. Recall from PS2 that this experiment increased the mi-gration rate in the treatment group - that is, it created exogenous variation in the number of migrants. In this question you are going to use that exogenous variation to evaluate the efficiency hypothesis. (a) The test you want to run is asinh(Yi) = α + βMi + ϵi where Yi is Nairobi income of family i, Mi is the number of migrants who traveled to the capital city of Nairobi from household i, and ϵi is an error term. asinh is called the inverse hyperbolic sine function: it has approximately the same interpretation as log, but unlike log it is defined for Yi = 0 (Hint: you will need to generate a new variable that takes the asinh transform. of Yi). If Mi were randomly assigned, then β would measure the causal effect on income of sending 1 additional migrant to Nairobi. Estimate this exact specification using endline data. What coefficient do you find for β? Explain that coefficient in words. Is this equal to the causal returns to migration, or not? Why/why not? (b) The experiment offers a suitable instrument for Mi . What variable is it? Test the inclusion restriction with a regression and report your result. Justify the exclusion restriction in words. (c) Estimate the instrumental variable specification of the equation in 5a. See Stata tips below for how to do this. What do you find for β now? Is it larger or smaller than the estimate from 5a? What does this tell you about the nature of selection into migration? Interpret the coefficient on β in terms of the efficiency hypothesis discussed above. (d) The β you estimated in 5c has another name: the treatment on the treated. That is, it’s the return to migrating among households who migrated because of the information treatment. Do you expect this number to be smaller, larger, or the same as the intent-to-treat (ITT)? Why? (You do not need to estimate the ITT - just explain in words.) (e) There is one concern we need to rule out: what if migration to Nairobi decreases income earned outside of Nairobi? Then it’s possible that the experiment could be income-decreasing even if it increases income earned in Nairobi. Test this concern by regressing total household income (taking the asinh transform) at endline on treatment. Do you find evidence for or against this concern? Interpret your finding. Stata Tip: Merging Datasets Sometimes, different variables for the same units of observations (for example, households) belong to different datasets. You might need to merge these datasets to perform. your statistical analysis. To do it in Stata, you just need to know the following: • The merging variable (id): the variable that uniquely identifies each observation and does not change across datasets. • Why does Stata need it? Imagine that households are not sorted in the same way across the datasets that you need to merge. The id variable is going to inform. Stata that – say – the first household in dataset 1 is the 45th in dataset 2. Then, Stata uses this information to match the values of each variable to the right household. • What do you need to do to merge in Stata? Imagine that you need to merge dataset 1 and dataset2. The merging variable is id. They are both in your working directory (you don’t need to write the full path to the files in Stata). Then you type: *Open dataset1 use dataset1 *Merge in the second dataset merge 1:1 id using dataset2 rename _merge merge1 Adapt this script. to answer question 1. Look at the Stata help of the command merge – help merge – to find out what the variable _merge is. This variable is created after each merge and it provides fundamental information about the merging process. Stata Tip: IV Regression Instrumental variable (IV) regression proceeds in two stages: • First stage: Xi = γ + δZi + νi • Second stage: Yi = α + βXˆ i + ϵi Here Z is called the instrument and X is the endogenous variable. Xˆ i are the fitted values from the first stage (that is: Xˆ i = γ + δZi). You don’t need to worry about this, as Stata lets you estimate the second stage with a single command. That command is: ivreg Y (X = Z), r This tells Stata to estimate the effect of X on Y, instrumenting X with Z. The ,r at the end tells it use robust standard errors (don’t worry about what this means). Stata tip: Asinh transform The inverse hyperbolic sine (asinh) is a function with a derivative very similar to that of the logarithm function. But, conveniently, it does not drop observations of “0”. Taking a log transform. drops these observations because log(0) does not exist. In this problem set you will analyze the effect of migration on income after taking an asinh transform. To generate a new variable called asinh_income from a variable called income, type gen asinh_income = asinh(income)
Econ 584 Spring 2023 Problem Set #8 Due April 18 before class 1. Explain the product life-cycle model as developed by Bass. Explain how a PLC model is estimated. What data is employed in the estimation of the Bass model? 2. Using the Kodak Polaroid data set (aggr.sav or aggr.txt) I’d like you to: (a) Show the product life-cycle for instant cameras (i.e., produce a graph of sales over time remembering to remove the seasonal component and to show annual sales instead of bimonthly sales. (b) Estimate a Bass type product life-cycle model for this data. Does the product life cycle fit the data in your opinion? Hint: Please remember to remove seasonality during estimation. Also, Kodak and Polaroid instant camera units sold are variables “ku” and “pu” respectively. You may create a total units sold as instant = ku + pu. Seasonal dummies are included in the data file. (c) Optional for extra credit: Create a demand model using other factors included in the dataset. Compare the fit of the Bass model to a simple demand model. Do you think simple R-squared is useful here for comparing the fit of Bass model and simple demand model? Why or why not? Please explain your reasons of preferring one to the other based on RMSE standard. (Hint: Since the dependent variables is the same (demand for instant cameras) then RMSE is a proper way to compare models (similar to picking the model with the higher likelihood).You may look at https://en.wikipedia.org/wiki/Root-mean-square_deviation for more details of RMSE if you are not familiar with this concept.) 3. Briefly explain the Polaroid econometric model of camera demand. (Hint: limit yourself to less than one page!) What is the relationship between the PLC and Polaroid models? 4. What are some of the variants of the simple Bass model? What are the limitations of the Bass model? 5. What is a reservation value? How are reservation values used in economic theory? Can reservation values be measured?
DMS/DAC/DIB/DBF Apr - Jun 2025 QTS0109 BUSINESS STATISTICS CONTINUOUS ASSESSMENT 1 INDIVIDUAL ASSIGNMENT 100 marks (30%) Read the instructions in your course documents, in your student portal, Course Outline, CA outline and Canvas portal carefully. You will be penalized with marks deduction if the instructions are not strictly followed. If you have queries, read the Canvas Help and then email queries via your student portal. Students are to upload their assignment by the due date to through their student portal account. After the due date, students’ submissions will not be entertained. Students should a keep a copy of assignment submitted. All workings must be shown. The submitted report must show evidence that this is students’ own work. Write your FULL Name AND your student ID as in the register on the answer script. Please be reminded that plagiarism and collusion is a serious offence, and all cases will be referred to the administration. Grades will be withheld if the submission is suspected for plagiarism or collusion till investigations are completed. Due date: 25 Apr 2025 (Fri), 11.59 am Penalty Marks for Late Submission of Assignment Within 24 hours: 20% to be deducted from total marks More than 24 hours: submission will be graded zero CA Submission CAs must be submitted online via student portal. Please read through instructions in your student portal, CA outline and your Canvas carefully before submitting. If you have further queries, please read the Canvas Help. If after you have read the Canvas Help, you need assistance on Canvas submission, please email to [email protected] or call 6248 9393. For non-Canvas issues please email [email protected].Please email with your student portal accounts. Email from other addresses will not be entertained. If issues raised are covered, you will be directed to read through instructions first. Please take time read through before raising issues. CONTINUOUS ASSESSMENT 1 [Total marks: 100 marks (30%)] Answer ALL questions in Sections A and B Submit an Excel answer file in Canvas: (1) Use Section A sheet in the Excel data template provided in Canvas to answer questions from Section A. (2) Use Section B sheet in the Excel data template provided in Canvas to answer questions from Section B. (Save your Excel file using the prescribed format QTS0109yourname e.g. QTS0109johntan.xls) Section A (50 marks) (1) For each variable listed below, state the variable type. [Qualitative, Quantitative (Discrete) or Quantitative Continuous)] (8 marks) (2) For each of the following variables, state the Level of Measurement (Nominal, Ordinal or Ratio). , )NationalityofthediplomastudentsQ5aThepriceofthecourseisreasonable .12345Q5cIamsatisfiedwiththeenvironmentofthecenter rontdeskor hotline .12345Q5eIamsatisfiedwiththeteachingofthecenter udying in thecenter.12345Tuition purposeMeanMedianStandardDeviationPassing the exam/ m Discuss your results. (10 marks) (10) You would like to look at the relationship between the marketing fee tuition center put in per year and revenue (‘000) made yearly. The following data were collected: (a) Input above data in an excel worksheet. (3 marks) (b) Construct a scatter diagram using Marketing fee on the horizontal axis and Revenue on the vertical axis. State the direction of relationship. (4 marks) (c) Explain why “Marketing fee” is plotted on the horizontal axis. (3 marks) (11) Given the following frequency distribution of final revenue (‘000) for a sample of 10 years. Calculate the grouped mean and grouped sample standard deviation. (Format finalanswers to 2 decimal places) (5 marks) (12) Create a contingency table with Q2 (Continue the current programme for next semester) as the row variable and Q1 (Recommend tuition center) as the column variable. Label clearly and comment on the results. (6 marks) (13) Create a pie chart for the variable Q5e (Satisfied with the teaching of the center). Label your chart clearly. (4 marks) (14) Data preprocessing (maximum: 250 words) Read through the core module “Data Analytics” before you attempt this question. Data preprocessing is a crucial step in data analysis. If the data itself contains typo, missing values and outliers etc, it can significantly impact the results of data analysis. (a) Please discuss the importance of data preprocessing. (5 marks) (b) Please demonstrate two aspects of data preprocessing. [Hint: you can consider what data scientist do during data preprocessing and what methods they are using.] (5 marks)
CIS444 – Spring 2024 - Project Specification Secure Two-Party Communication with Hybrid Encryption and Attack Simulations Overview This project requires students to develop a secure communication application using Python (or another programming language you prefer – Assembly perhaps?) that simulates a two-party conversation over an insecure channel. The system employs a encryption scheme where RSA (public key cryptography) is used to securely exchange an AES symmetric key, which is then used for subsequent communication. In addition, the application simulates attacker activities including confidentiality attacks (eavesdropping), integrity attacks (impersonation), and availability attacks (channel flooding). The attacker’s observations and interventions will be logged and analyzed. Objectives - Ensure confidentiality by implementing both asymmetric (RSA) and symmetric (AES) encryption. - Use RSA to exchange a symmetric key securely between two parties. - Develop a UI with two distinct textboxes (along with the send button) on the same screen for User A and User B, and three buttons that activates attacks for the attacker - Simulate an attacker impersonating one of the parties to modify or inject messages. - Simulate a Denial-of-Service (DoS) scenario where the channel is overwhelmed with traffic. - Record all data transmitted over the communication channel, including any attack traffic and modifications. Functional Requirements 1) User Interface - Dual Textboxes: Display two input areas along with “send” buttons on a single screen: - User A’s textbox: For plaintext message entry. - User B’s textbox: For plaintext message entry. - Attacker: Buttons that start integrity and availability attacks - Message Display: Show both sent and received messages along with status notifications (e.g., “public key received,” “symmetric key exchanged,” “message altered by attacker”). - Attack Alerts: Display status on screen if an integrity or availability attack is simulated during the session. 2) Communication Flow 1. Initial Message: - User A initiates the conversation by entering any plaintext. - User B receives the initial message and must reply with their RSA public key. 2. Public Key Exchange: - The first exchanged message containing a public key triggers the key exchange process. - The party that receives the public key generates a new AES symmetric key. 3. Symmetric Key Exchange: - The party generating the symmetric key encrypts it using the received RSA public key. - The encrypted symmetric key is sent to the other party. 4. Secure Messaging: - Once both parties share the symmetric key, subsequent messages are encrypted and decrypted using AES. - Each message transmitted is subject to logging and potential interception or alteration by the attacker. 3) Attacker Simulation and Logging Confidentiality Attack: Eavesdropping: - Record all communication (both plaintext during initial key exchange and ciphertext during secure messaging) that is visible to an eavesdropper. Integrity Attacks: Impersonation: - Simulated Impersonation: The attacker pretends to be one of the legitimate parties by injecting or modifying messages. How? Figure it out. - Logging Changes: Record the attacker’s injected messages or alterations and include a note indicating that the message has been tampered with. - Verification Mechanisms: Optionally, implement message authentication codes (MACs) or digital signatures to detect such integrity violations. Availability Attacks: Channel Flooding: - Simulated DoS: The attacker generates a high volume of spurious traffic to overwhelm the communication channel. - Logging Overloads: Record the timestamps and volume of traffic associated with the flooding to analyze the impact on the communication process. Technical Requirements Programming Language: Python (recommended) – or any other programming language you prefer. Cryptographic Libraries: - Use off-the-shelf libraries such as PyCryptodome or similar for AES and RSA operations. Encryption Protocols: - AES: For symmetric encryption after key exchange. - RSA: For encrypting the AES key during the exchange. User Interface Framework: - A simple GUI (Tkinter, PyQt, or a web-based interface) that supports two separate text inputs on the same screen. Attack Simulation Modules: - Implement modules or threads that simulate integrity attacks (by altering or injecting messages) and availability attacks (by flooding the channel with data). One-Page Report Security Analysis: Discuss the vulnerabilities demonstrated by the simulated attacks and propose potential mitigation strategies. How-to: Add an installation manual Evaluation Criteria Functionality (40%) Code Quality (20%) Security Understanding (20%) Documentation (20%)
ECON7310: Elements of Econometrics Research Project 1 April 4, 2025 Instruction Answer all questions following a similar format of the answers to your tutorial questions. When you use R to conduct empirical analysis, you should show your R script(s) and outputs (e.g., screenshots for commands, tables, and figures, etc.). You will lose 2 points whenever you fail to provide R commands and outputs. When you are asked to explain or discuss something, your response should be brief and compact. To facilitate our grading work, please clearly label all your answers. You should upload your research report (in PDF or Word format) via the “Turnitin” submission link (in the “Research Project 1” folder under “Assessment”) by 16:59 on the due date April 17, 2025. Do not hand in a hard copy. You are allowed to work on this assignment in groups; that is, you can discuss how to answer these questions with your group members. However, this is not a group assignment, which means that you must answer all the questions in your own words and submit your report separately. The marking system will check the similarity, and UQ’s student integrity and misconduct policies on plagiarism apply. Background Use the cps09mar.csv dataset to estimate the effect of education on earnings. Data description and variable definitions can be found in the document cps09mar description.pdf. For all questions below, use the sub-sample of non-Hispanic women at least 23 years old. Research Questions 1. (20 points) Load this dataset in R (2 points). Create a new variable wage = earnings/(hours × week). Obtain summary statistics (mean, standard deviation, 25, 50 (median), and 75 percentiles) for wage and education (5 points). Plot histograms for these two variables to explore their distributions. Make your histograms reader-friendly; that is, give informative titles and variable names instead of just using the default titles and variable names (6 points). For example, you could use Years of Schooling in place of education. Create a new variable ln(wage) and draw a scatter plot of ln(wage) versus education (5 points). Comment on the correlation between these two variables (2 points). 2. (25 points) Estimate the simple linear regression model: ln(wagei ) = β0 + β1educationi + ei . (1) where ei is the error and β0 and β1 are the unknown population coefficients. (a) (3 points) Report the estimation results in a standard form. as introduced in Lecture 5. For example, see page 5, where the estimates are presented in an equation form, along with standard errors (SE) and some measure for goodness of fit. (b) (3 points) Plot the estimated regression line you obtained in (a) on the scatter plot you constructed in Question 1. (c) (6 points) Interpret the estimated coefficient on education (3 points) and test whether or not the population coefficient β1 is zero at the 1% significance level (3 points). (d) (6 points) The hourly wage could also depend on one’s work experience. Under what condition(s) would the estimates in (a) be biased and inconsistent due to the omission of the work experience (4 points)? Explain whether the coefficient on education in (a) would be overestimated or underestimated (2 points). Hint: Review pages 4 and 5 of Lecture 4. (e) (7 points) Create a new variable experience = age − education − 6 to measure one’s work experience. You want to include experience in regression (1) and regress ln(wage) on education and experience. However, you are not sure whether to also add a quadratic term, such as experience2 , to the regression equation along with experience. Use a hypothesis test to help you choose the more appropriate model (4 points). Estimate your selected model and report the results in a standard form. (3 points). 3. (43 points) With the regression model that you selected in 2(e), you are still concerned about omitted variable bias. For that reason, you decide to include more control variables in the regression. (a) (11 points) Include a set of dummy variables for regions and marital status and estimate the extended model (4 points). For regions, create dummy variables for Northeast, South, and West so that Midwest is the excluded group. For marital status, create variables for married (marital ≤ 3), widowed or divorced, and separated, so that single (never married) is the excluded group. Report a 95% confidence interval for the slope coefficient on education (2 points), explain the relationship between the confidence interval and hypothesis testing (2 points), and test the hypothesis that one year of additional education would increase hourly wage by 12% (3 points). (b) (5 points) Using the estimation results, test the hypothesis that the hourly wage is not affected by the geographic location (3 points). Explain how you reach your conclusion (2 points). (c) (8 points) Include a dummy variable black for black workers (race = 2) in the model you considered in 3(b) and run OLS estimation. Explain what the estimated coefficient on black means on hourly wage (3 points), compare the effect of being a black worker and the effect of one year of additional education (2 points), and test whether these two effects are of the same magnitude (3 points). (d) (7 points) How would you modify the model to test if the effects on hourly wage of one additional year of education differ between black and non-black workers (4 points). Implement your proposed test and report the results (3 points). Hint: See pages 27–39 of Lecture 6. (e) (7 points) Kate has 31 years of work experience. Using the regression model of 3(d), test if one additional year of work experience has significant effects on her hourly wage (5 points). Provide a formula for calculating this effect (2 points). Hint: Read pp. 9–17 of Lecture 6. (f) (5 points) Betty is a married, white woman, working in Boston. After she obtained her college degree (= 16 years of schooling), she got a job and started working instead of getting a higher education. Now she has a five-year of experience in the industry. Predict Betty’s hourly wage. 4. (12 points) It may be more useful to estimate the effect on earnings of education by using the highest diploma/degree rather than years of schooling. Define four dummy variables to indicate educational achievements: • lt hs = 1 if education < 12 • hs = 1 if education = 12 • col = 1 if education ≥ 16 • some col = 1 for all other values of education. (a) (4 points) Create the dummy variables lt hs, hs, col, and some col as defined above and compute the sample means of hourly wage for each of the four education categories. (b) (5 points) Replace the education in the regression model of 3(d) with these dummies and estimate their coefficients. Can you obtain the OLS estimates for all these four dummies? Explain your answer (3 points). Interpret the coefficient on hs (2 points). (c) (3 points) Report estimation results of regressions in 2(e), 3(a), 3(c), 3(d), and 4(b) using a table similar to those presented in your Tutorials 5–6. Hint: If you are not familiar with LATEX, you can use the screenreg() function instead of texreg().
SEHH1071 Computational Tools for Statistics (Semester 2, 2024/25) Group Project & Individual Assignment Intended Learning Outcome of the Group Project & Individual Assignment Ø Apply statistical concepts in data analysis and problem solving. Ø Present the analyse data using basic statistical techniques. Ø Analyse large data sets with the use of computer software. Ø Formulate a mathematical and/or statistical solution for real life problems Project Description DPM Travel is an upcoming travel booking website. The group has recruited your team as the consultant to find out the customers’ behaviors and preferences on travel booking for bettering targeting in future market expansion. Your team had set up an online questionnaire to collect responses from a group of randomly selected customers who had used the website before. The response period was from 12 to 22 August 2024. 1019 responses were obtained and saved in the Excel file named “Travel.xlsx” Your team are required to: - Carry out data cleaning and processing to the dataset - Provide descriptive analysis on behaviors and preferences on the Travel booking website Each team member is required to - Test the independence of specific variables using the Chi-square independence tests - Carry out specific regression analysis Project Objective - Make the dataset available for analysis through data cleaning and processing - Provide descriptive analyses to a large dataset (over 1000 of customers) using the Software (e.g. Excel, SPSS and/or R) - Choose appropriate data analysis tools and apply relevant statistical concepts to a real-life problem (using behaviors and preferences on travel booking website and services data for descriptive analysis) - Convert the problem behaviors and preferences on hotel) into mathematical solution (Regression models and Chi-Square independence tests) - Present the result in an appropriate manner (report and presentation) Project Requirement Group Project 1. Project Member List among the team members - You are required to form. a team of 6 (can be across tutorials) - The list and the group name are to be submitted by the team leader 2. Interim Report - The interim report should include n Work Assignment n Team Schedule (e.g. progress or completed task(s) by week) n A brief description on the data cleaning and processing n A brief description on the proposed descriptive analyses with explanation (can be calculation, charts, table….) 3. Final Report - The final report should include n Cover Page u Title of the Course u Group member names n Table of Content u Headings and sub-headings of various sections u Page numbers n Introduction u A brief description on the background of the study (e.g. the travel booking market) u Specific objective(s) of the project (determined by your group) u Tasks allocation n Data Processing and cleaning u Detailed descriptions about the tasks that your group has carried out (e.g. missing data treatment, problematic data…) n Descriptive Analysis u Various types of charts and tables u Statistical analyses on qualitative, quantitative and demographic variables in general sense (e.g. sample composition, average score on various preference criteria…) Individual Assignment After and based on the data cleaned and processed as well as the descriptive analyses conducted by the group, each member should then select one of the following tasks (not to be repeated in the same group). Consultant Task A Qualitative: 1. Last Booking x Type of Booking 2. Last Booking x Booking time before the trip Quantitative: Maximum companion in a booking B Qualitative: 1. Channel knowing the website x Type of Booking 2. Booking time before the trip x Type of Booking Quantitative: Average spending per booking (HKD$) C Qualitative: 1. Booking time before the trip x Channel knowing the website 2. Type of Booking x Education level Quantitative: Probability to book a trip in the next month D Qualitative: 1. Form x Booking time before the trip 2. Last Booking x Education level Quantitative: Average confirmation time (in minutes) E Qualitative: 1. Type of Booking x Payment Type 2. Booking time before the trip x Payment Type Quantitative: Maximum time that can accept for confirmation (hours) F Qualitative: 1. Payment Type x Last Booking 2. Channel knowing the website x Payment Type Quantitative: Maximum queue time that can tolerate under promotion (in minutes) You need to write a report to include: - Cover page u Title of the Course u Student name u Title of the project u Consultant Role (A-F, not to be repeated within the group) u - Complete analysis on the following: u For Qualitative analysis, you need to l Carry out Chi-square independent test for each combination of variables l Full steps as stated in the lecture and lab notes l One p-value approach and one critical value approach with level of significance = 0.05 u For Quantitative analysis, you need to l Carry out the regression analysis with level of significance = 0.05 (1 F-test and 1 t-test) l Conduct assumption tests and report what assumption(s) are is/are not fulfilled (if any) (Note: At least 2 out of the 5 independent variables being significant) u Recommendation l Based on the facts and results obtained in the analyses Presentation Slides - The presentation slides will be prepared by each group but assessed in an individual manner based on each member’s own dedicated contribution u The presentation should include the following section l Introduction l Data cleaning and processing (Very brief) l Descriptive Analysis (Very brief) l Interpretation of the Regression models and Chi-square tests (to be conducted by each member) Project Timeline Date (Sunday 23:59) Group Project Task Individual Assignment Task Week 3 Group Enrollment Week 8 By Group Leader Interim Report - No more than 3 pages (docx) Week 12 By Group Leader Final Report - no more than 15 pages (docx) By individual Final Report - no more than 8 pages (docx) Week 13 Presentation Slides (Submitted by Group Leader but evaluated individually) - no more than 30 pages (pptx) (according to the number of students submitting the individual assignment Peer Evaluation Form (only if you think it is necessary)
MKF5911 – Theory and Process of Buyer Behaviour Assessment 2: Apply One Buyer Behaviour Theory to Marketing 1. Overview Students find it challenging to make the links between buyer behaviour theory and the development, execution and management of marketing activities. This assessment seeks to address this issue by providing you with opportunities to communicate your theoretical insights on buyer behaviour to a senior marketing person within an organisation How does this assignment benefit you? First, the skill of integrating theoretical insights and information into meaningful business insights is highly regarded in business (see definition of insight below in Section 3). Just like consumers, marketing managers (and other business people) are bombarded by too much information and tend to ignore or not respond to information that does not seem immediately relevant. This is particularly the case for decision-makers who are required to sift through large amounts of information to arrive at decisions. Polishing your skills in presenting meaningful, succinct, and well written insights will therefore help build your competitive advantage in the workplace. Second, the process of integrating marketing theory and concepts to create business insights helps to build logical and structured thought process that refines your capacity to think critically and make evidenced based recommendations as opposed to simply offering opinions. You are expected to communicate your ideas clearly and concisely as is fitting for an insights report to a business person, which allows the recipient to grasp the content quickly and easily. Developing your insights report will take time and require that you carefully edit your work for clarity and conciseness. Note that 10% of marks have been allocated to written expression and report structuring (see Marking Rubric). Your report should include meaningful and relevant visualisations and analysis of visualisations. If you create an academic, word dense “essay” you will be penalised. If you cannot “market” information you are not thinking about your audience (senior marketer). 2. Assessment 2 has 6 compulsory components You should develop this assessment as though you were going to present it to a senior marketer in the organisation. For this reason, your analysis and insights MUST BE TAILORED to this person and their organisation. 1. Component 1 requires you to research and profile a company in one of the following business areas: entertainments (e.g., cinema, theatre, concert, amusement park, tourism attraction that charges an admission ticket, etc.) or games (e.g., video game, mobile game, board game, game venue). If a company’s operation is geographically dependent (e.g., McDonald’s), it is recommended to select one regional business (e.g., McDonald’s Australia or McDonald’s USA). Taking this approach will make completing component 3 (profiling target segment) easier. The business does not necessarily have to operate in Australia. It can be in either Australia, or a country you currently live in, or another country you are familiar with. You are instructed NOT to profile Business-to-business (B2B) companies, advertising firms, or consulting firms (assessments based on these will be awarded zero marks). 2. Component 2 requires you to research and profile a senior marketer in the company you have selected. Ideally you should select someone who has been at the organisation for at least one year (e.g., marketing manager, marketing director, vice president marketing, senior brand manager, digital marketing director or manager, marketing executive, etc.). Note - actual titles may vary by organisation and organisational structure. 3. For component 3, you will profile one consumer segment that the company currently targets or wishes to target. Your profile must address the following segmentation variables: a. Psychographic / Demographic / Geographic/ Behavioural. (“/” means “or”, not “and”) b. Perceived needs and wants of the selected target segment c. Value being sought by target segment (benefit – cost) Based on your profile, you are expected to provide analytical insights into their potential behaviour; the type of person are they, and what are they seeking from the company’s service. You are expected to justify your insights. 4. Component 4 requires you to draw on your profiling and investigation, to develop one key consumer- related insight for the company (e.g., what marketing challenges / opportunities is the company currently / potentially facing in relation to its customers’ behaviour?). This challenge or opportunity must be addressable within the capacity of the firm’s marketing department. (e.g., avoid things like sudden changes of laws, increased manufacturing cost, technology-based changes, etc.) 5. Drawing on your responses to components 3 & 4, explain how one element* of buyer behaviour (e.g. perception) could influence the profiled target market’s decision-making in relation to the profiled business. *The element of buyer behaviour you will focus on for Assessment 2 can come from weeks 1~7. 6. Drawing on your responses to component 5, propose one recommendation that you would want the senior marketer to implement. Your recommendation must relate to the design of one marketing mix activity (e.g., pricing, product, or promotion). Your recommendation must be ORIGINAL (i.e., something that the company is not already doing). Put differently, you are NOT to simply describe what the company has already been doing. Your recommendation must be detailed and ACTIONABLE. A basic or general recommendation is not actionable. Finally, your recommendation must be based on and connected to the theoretically justified analysis in component 5. 3. Definition of insight Your business report is expected to provide insights for the senior marketer. For the purpose of this unit, an insight is defined as; a previously unrevealed fact that can be used to build marketing action that will lead to a sustainable competitive advantage. • Unrevealed – not previously understood, not common knowledge, not generally known. • Fact – it must be supported by evidence; numbers, graphs, trends AND academic, contemporary business or field research. • Marketing action – input to overall strategy, overall marketing mix or specific marketing mix component/s. • Sustainable competitive advantage – input to achieving objective/s in the market place.
218.323 MEASUREMENT 2 Semester 1, 2025 PORTFOLIO ONE (A) GENERAL INSTRUCTIONS: 1. This is a group assignment. Your group should contain a maximum of 5 students. Should you choose, you do have the option to work individually. If you work individually, it should be presented to the same quality required expected from a group. 2. This PORTFOLIO ONE contains ONE question. 3. Total marks for PORTFOLIO ONE: 100 marks 4. Total course weightage and contribution of the assessment component = 40% 5. Submission deadline: Tuesday, 15 APRIL 2025, no later than 11:50 pm 6. NO HARD COPY SUBMISSION IS REQUIRED 7. Online submission on Stream: COMPULSORY. 8. Late assignments will be penalised as per the course guide and the school’s policy. 9. You must include your names and IDs, the course number, the course title and the names of the course teaching team on the cover page. 10. You are only allowed to submit PDF and COSTX files on Stream. 11. Please read and consider all documents in the assessment folder. 12. Include your assumptions in preparing your assessment in query sheets. 13. Covered learning outcomes a. Use industry-standard software to measure building quantities according to the Standard Method of Measurement. b. Prepare Bills of Quantities using industry standard software. PORTFOLIO ONE: 100 MARKS QUESTION 1 The client for Project Drako has decided to use a Lump Sum pricing method for the contract. Your company has been selected as the contractor for this project. Your manager has instructed your team to measure the following items for Levels 3 and 4 of the IC building: 1. Doors 2. Windows and façade systems 3. Hardware 4. Applied finishes, render and textured finishes for walls 5. Painting for walls 6. Joinery The client requires the following: 1. Accurate Measurement of Items: Measure all items meticulously to ensure precise project quantities using the CostX education version and ANZSMM 2022. 2. Bills of Quantities: Provide a detailed Bill of Quantities, supported by clear documentation and explanations of the quantity calculation methods. 3. Documentation: Submit all documents suitable for industry practice in PDF format, clearly referencing how the quantities were developed. Assessment instructions • Some drawings are not to scale. You can use drawings and dimensions from detail drawings to measure. If there are no dimensions shown on thedrawings, then develop an appropriate scale for measurement. • Accuracy of descriptions accounts for a considerable part of the final mark. • You can choose to use Costx or other software to prepare the document. However, your submission should follow examples. You are required to show how you came up with the quantities where necessary. • Think about how BOQ should look like industry practice and prepare accordingly. • You should include the following files in your submission (refer to the file generation steps mentioned below) o One PDF document which includes Front page (example 1) General and specific preamble notes for each section BOQ summary (refer to example 2) Detailed calculations of each item (refer to example 3) Drawings with dimensions take off details Query sheet - (refer to example 4) Peer review report (refer to example 5) Generative AI use statement (attached separately) [100 marks] (A) MARKING RUBRIC Assignment & Marking criteria Marks Portfolio 1 1. Question 1 a. Preparation of BOQ with supportive documentation suitable for industry practice with clear documentation. i. Presentation of each section (6) ii. Preambles and Query sheet of each section (12) iii. Cover page and table of contents (2) b. Correct descriptions and appropriate headings / sub-headings for the measured items including generic & specific preamble notes i. Doors ii. Windows and façade systems iii. Hardware iv. Applied finishes, render and textured finishes for walls v. Painting for walls vi. Joinery c. Correct quantities for the measured items, including side-casting, entry of measures, and quantity referencing. The schedule of quantities for the trades is presented in the correct SoQ format. i. Doors ii. Windows and façade systems iii. Hardware iv. Applied finishes, render and textured finishes for walls v. Painting for walls vi. Joinery 20 4 5 3 7 6 3 4 9 6 12 10 5 2. Peer review report with Student name, ID, Work section completed by each student 5 3. Generative AI use statement. 1 GRAND TOTAL 100 (B) LATE SUBMISSION & ACADEMIC INTEGRITY a. Plagiarism: Plagiarism is a very serious offense. DO NOT PLAGIARISE. Read the rules here:https://owll.massey.ac.nz/referencing/plagiarism.php. b. Late submission: A penalty of 3 marks will be deducted for each calendar day (including weekends and public holidays) or a part of a day an assignment is submitted late after the Stream deadline. Assignments submitted more than 7 calendar days after the Stream deadline will not be marked and will get a mark of 0. c. Extension of time: An extension may only be granted by the course coordinator based on the circumstance.
ACC/ACF 1100 Introduction to Financial Accounting Assignment Two In this assessment, you must use MYOB software to complete the practical component of the task. You must NOT use generative artificial intelligence (AI) to generate any materials or content in relation to the assessment task. Due Date: Sunday 4th May 2025 at 11:55pm (week 8) Value: 10% Purpose: To gain experience using accounting software and demonstrate understanding of the recording process. Submission: uploaded via Moodle Components: A practical (recording keeping) component, and a written response. Requirements: Please read the details carefully. There are two separate tasks involved. Background and details: Darcy Smith has a good friend who also started their own sole proprietorship gardening business around the same time! Meg Adams named her business ‘Mega Gardening Services’. Details about her activities relating to gardening and lawn mowing for the month of February 2025 are provided (ignore GST): Date Details 1 Feb Meg contributed $2,000 cash, gardening equipment valued at $1,300 and a van valued at $12,000 to start the business. 1 Feb Meg purchased a mobile phone for $1,600 on credit from Harvey Norman. 4 Feb Meg paid $900 for advertising in letterboxes for the months of February, March and April. 7 Feb Meg completed weeding services for clients and received $320 cash in total. 8 Feb Meg purchased a supply of disposable gloves and rubbish bags for $250 cash. 9 Feb Meg weeded Mrs Brown’s garden (a neighbour) for $180 on credit. 10 Feb Meg withdrew $195 from the business bank account for personal use. 11 Feb Meg completed pruning services for clients and collected $430 cash. She also disposed of rubbish at the transfer station for $75. 13 Feb Meg was called to urgently weed a garden at a property before an open day. She left the business (Sunrise Estate) an invoice for $600. 14 Feb Meg refuelled her van for $90 cash. 14 Feb Meg purchased a trailer from Trailer World for $4,990 on credit terms n/90. 15 Feb Meg made a $200 payment to Harvey Norman related to the phone purchase. 16 Feb The neighbour (Mrs Brown) paid Meg the amount she owed from the weeding on 9 Feb. 20 Feb Meg withdrew $320 from the business bank account for personal use. 22 Feb Meg completed a pruning job for $310 cash. 23 Feb Garden waste disposal fees were paid for $168. 26 Feb Meg received $400 from a client for weeding and pruning to be done in the coming weeks. 27 Feb Meg paid Trailer World $500 towards the balance owing on the trailer. 28 Feb Meg withdrew $210 from the business bank account for personal use. 28 Feb Information regarding end-of-month adjustments: o Meg estimates that she has $95 worth of gardening supplies on hand. o The advertising has been consumed as planned. o Of the amount that was received from a client in advance, Meg estimates that 40% of the client’s job has now been completed as pruning services. Depreciation details (all straight-line method): o Gardening equipment (Life = 2 years, $100 residual) o Van (Life = 6 years, $3,000 residual) o Phone (Life = 3 years, $700 residual) o Trailer (Life = 8 years, residual $990) Required: Part A (Practical component) The general journal entries for Meg’s transactions are included below. You are required to record the information in MYOB software and produce financial statements. You MUST follow the instructions provided in Moodle to enter this data, as marks will be awarded based on the accuracy of the data entry recording process you follow. You need to submit the following reports from MYOB at 28/02/2025: Ø Journal entries Ø Profit and Loss Ø Balance Sheet Part B (Written component) Answer both parts (place your answers in a word document with the reports you generated for Part A of this assignment): (i) Based on your experience in completing Darcy’s financial records in Assignment 1, and Meg’s financial records in this assignment, compare the opportunities to make errors during the recording process under each approach. Based on this, would you recommend that Darcy use accounting software for his business? Explain why or why not and justify with any assumptions you make. Maximum 200 words. (ii) Reflect on the process you undertook in Part A to complete this assignment. Identify which part of the MYOB recording process you found to be the most challenging and explain why. Maximum 100 words. CHART OF ACCOUNTS No. Account Name 111 Cash 120 Gardening Supplies 130 Accounts Receivable 140 Prepaid Advertising 150 Gardening Equipment 151 Accumulated Depreciation – Garden Equip 160 Van 161 Accumulated Depreciation – Van 170 Mobile Phone 171 Accumulated Depreciation – Mobile Phone 180 Trailer 181 Accumulated Depreciation – Trailer 200 Accounts Payable 210 Unearned Income 300 Capital – Meg 310 Drawings - Meg 400 Weeding Services Income 410 Gardening Services Income 500 Rubbish Disposal Expense 510 Fuel Expense 520 Gardening Supplies Expense 530 Advertising Expense 540 Depreciation Expense – Gardening Equip 550 Depreciation Expense – Van 560 Depreciation Expense – Mobile Phone 570 Depreciation Expense – Trailer
Econ 275 (Baseler): Problem Set 3 ** Due Thursday April 3 at the beginning of class** ** Please use a pen and not a pencil if you hand-write** 1 What is the impact of microfinance? What impact do micro-loans have on the welfare of borrowers? This problem illustrates different methods of evaluating impact. 1.1 Newspaper article: Huge gains for microloan clients “Statistics released today reveal that prominent South African bank, SAB, and its new micro-lending program to working class clients in Johannesburg and Cape Town has led to a 78 percent increase in client household incomes. When clients approached SAB, their typical monthly income was 3070R; when households were interviewed 12 months after taking their loans, their incomes had jumped to 5454R. This provides only further evidence to what has already been recognized – largely via the 2006 Nobel Peace Prize to Muhammad Yunus and his revolutionary Grameen Bank – as one of the powerhouse new methods to fight poverty and raise incomes worldwide.” • What do you think of this article’s claim? 1.2 Letter to the Editor I have been a follower of the microfinance revolution since its beginnings in the 1970s. And while I’m happy to see South Africa join the rest of the world in exploring this exciting new space, I have some qualms with your newspaper’s article from last week. Indeed I’ve done my own calculations – and I found a healthy impact of 51 percent, not 78 percent. Indeed, while SBA borrowers had an income of 5,454R after 12 months, those who did not take loans had an income of 3,616R at that time. So the correct comparison is 5,454 to 3,616 and that is a 51% difference. Still something to be proud of, but almost 30 percent less than what you found! • What do you think of this reader’s claim? 2 Kenya household dataset This problem uses household data collected by KNBS (Kenya National Bureau of Statistics) be-tween 2015 and 2016. On Blackboard, under the problem set 3 folder, download the datasets called HH_Information.dta and Consumption_aggregate.dta. Also download the do-file called PS3_dofile to get yourself started. The survey questionnaire (“Questionnaire_Q1B.pdf”) is also available and may be helpful for finding the right variables. The section “STATA TIPS” at the end of this problem set contains some notes on the dataset and how to handle some of the variables. * Please attach your completed do-file to the end of the problem set. Please do not hand in a log file. * 1. A number of variables are expressed in Kenyan shillings (KSh or KES). What was the exchange rate between the USD and KSh around the time of the survey? What does 100 KSh at the time correspond to in USD at the same time? 2. Researchers often do not use purely random sampling. For example, we might over-sample poor households if we want to study poverty - that means that the probability of appearing in the dataset is higher for poor houseohlds than rich households. The reason we do this is because we want to have enough poor households in our sample to make precise statements about them. In this case, we have to re-weight our data to reflect the fact that the sample is non-random; otherwise, the mean income in the sample will be much lower than the popu-lation mean. The data you are working with for this problem set used a two-stage sampling design: that is, they first selected regions within Kenya, and then randomly selected an equal number of households per region. They reason they did this is that they wanted to have enough households even in places with low populations. The do-file posted on Blackboard gives you the commands to re-weight the data to adjust for this sampling design. Without the weights, the sample would over-represent households in sparsely populated areas. Cal-culate the weight-adjusted average household size using “svy: mean” and compare it to the unweighted average. Which one is higher? Why might that be? Hint: Remember that the design over-sampled households in sparsely-populated areas. 3. The do-file includes a command to merge the household information data with data on con-sumption aggregates (i.e., consumption totals across household members). How many house-holds are there in the HH_Information dataset? For how many of those households do we have information from the Consumption_aggregate dataset? Hint: Look at the _merge_consumption variable created within the merge command. 4. During the industrial revolution, urban mortality was higher than rural mortality across a broad set of contexts, in part because sanitation in cities was much worse. Test whether access to improved sanitation technology is significantly different in urban vs. rural areas for the following outcomes: (a) Presence of an improved toilet (flush toilet or Ventilated Improved Pit (VIP) latrine) (b) Presence of a hand-washing station near the toilet (c) Access to improved waste disposal (any method other than dumping or burning in the open) 5. The Consumption_aggregate dataset includes information at the household level on total consumption expenditure and total food expenditure per adult equivalent (consumption per adult equivalent is similar to consumption per capita, but adjusts for the fact that children do not need to consume as much as adults). Generate a new variable (call it food_share) that measures the share of total consumption that the household spends on food. (a) Plot the mean of food_share for urban, rural, and peri-urban locations (use the variable “eatype”). I suggest you use the “graph bar” command (type “help graph bar” to get instructions) along with the “over” option. There’s no “graph” command in the svy prefix that we’ve been using, so you’ll have to enter the survey weights directly into the command. So, for example, “graph bar var1 [pweight=weight], over(var2).” Save this graph and include it with your submission. (b) In which areas (rural, urban, or peri-urban) do people spend the highest share of their budget on food? The lowest? Why do you think this might be? (c) What is the definition of peri-urban? Based on this measure, do peri-urban places look more like rural areas or urban areas? (d) Regress food_share on the logarithm of per adult equivalent total consumption (the denominator in food_share). Don’t forget the “svy:” prefix! Is your estimated coefficient positive or negative? Does that mean that food is a necessary or a luxury good? Hint: Look up the definitions of necessity and luxury goods if you aren’t familiar with them. To create a log transform, use “gen newvar = log(oldvar).” STATA TIPS • Remember that often variables have “value labels.” This means that when you look at tabula-tion tables of numeric variables, Stata shows you labels like “Yes” and “No” even though the underlying data is a number. To see the values associated with each label, use the “codebook” command. For a variable called var1, I would type “codebook var1.” • Be careful not to run a regression on a Yes-No variable with code 1 for Yes and 2 for No. In that case a positive coefficient means the “No” group has a higher mean! You’ll want to generate a new variable equal to 1 when the answer is Yes, and 0 when the answer is No. You can also recode the original variable, but this can be dangerous when you forget which variables you’ve recoded. • You may find the command “lookfor” useful for finding the right variables. “lookfor” searches for instances of whatever string you specify in variable names and labels. E.g., “lookfor consumption.” • To collapse a variable that takes on several values to one that takes on only two (ex: for questions 4a and 4c), use the symbol | which means “or.” Example: to generate a variable named var2 which is equal to 1 when var1 is 1, 2, or 4, and 0 otherwise, write “gen var2 = (var1==1 | var1==2 | var1==4).”
School of Photovoltaic and Renewable Energy Engineering SOLA2540 Applied Photovoltaics SOLA9001 Photovoltaics Stand Alone PV System Design Weighting Due date Submission Deliverable 1: Preliminary design report 15% 4 April 2025 Report and MS Excel PV system design program via Moodle Deliverable 2: PV system design presentation 30% 22-24 April 2025 Presentation Deliverable 3: PV system design report 55% 5 May 2025 Report and MS Excel PV system design program via Moodle Deliverable 4: Reflection Satisfactory 5 May 2025 Report via Moodle Huddle requirements: You must demonstrate a satisfactory performance on this assessment. A minimum mark of 60% must be obtained this assessment in order to pass the course. You may re-submit the final report until a satisfactory performance is achieved. Your total mark for this assessment will be capped at 60% for the re-submitted work. 1. Overview: The aim of this assignment is to provide stand-alone photovoltaic system design experience. You will design PV system for a given project using the design principles and procedures you have learned in this course. This activity is linked to the course learning outcomes LO2 to LO6 and addresses these Engineers Australia Stage 1 Competency Standard for Professional Engineer: PE1.3, PE1.5, PE2.1, PE2.2, PE2.3, PE3.2, PE3.3 and PE3.6. 2. Project description: Consider you are a part of the design team in a reputed PV system design and installation company. You have been tasked to design a stand-alone PV system to address specific requirements of a customer. You can find your project based on your zID and weather data of the project location on the course Moodle site. The task includes: • Development of PV system design program based on MS Excel. The program should be able to perform. at least the following tasks: o calculate average daily Wh and Ah load for the given load specification; o calculate the battery size for the given days of autonomy; o calculate solar insolation (direct and diffuse) on the PV module at a given tilt angle; o calculate the energy generated by the PV array each day; o calculate the battery state of charge at the end of each day; o has ability to optimise tilt angle and PV array size; and o sizing and selection of other components such as charge controller and inverter as necessary. • Design of PV system: Using the Excel program that you have developed, design a PV system. You should design for two scenarios: (i) Rooftop installation of PV array considering roof tilt and orientation (assume there is sufficient roof space), (ii) Ground mounted system with optimum tilt angle and orientation for PV array. • Sizing and selection PV system components: Size and select suitable PV components such as modules, battery, battery charge controller and inverter. Provide justification for your selections (for example on the basis of cost, reliability, availability, maintenance requirements) and give at least two other alternatives. Note: different combinations of battery and PV sizes may satisfy the design requirements. • Drawing system layout: Select appropriate cable size (AS/NZS 3000) and draw line diagram (AS/NZS 5033). • Pricing the system: Price the total cost of the system including the cost of the components you will use, cost of installation and your company’s margin (20%). Use local labour rate to estimate installation cost. Ignore the cost of the real estate. • Financial analysis: Calculate payback period and levelised cost of energy (LCOE). Compare the LOCE with the cost of electricity from diesel generator. 3. Assessment: o Deliverable 1: Preliminary design report (15%): This document should demonstrate the evidence of progress made on the execution of the project. It should include current version of your MS Excel PV system design program which has to be functional but not necessarily complete and polished at this stage. The report must maximum 4 A4 pages long (any pages beyond this page limit will not be marked). Imagine that you are presenting this report to the client and/ or manager, so it has to be clear and concise. [See Appendix A for marking criteria] o Deliverable 2: PV system design presentation (30%): The presentation should demonstrate detail PV system design. This should include complete PV system design including load analysis, component sizing and selection and economic analysis. You should also have Excel PV system design program ready for demonstration. The presentation will be 10-15 minutes. [See Appendix B for marking criteria] o Deliverable 3: PV system design report (55%): The final report should report detail PV system design including economic analysis. It should include load analysis, sizing and selection of system components, system layout and economic analysis. You should also submit the final version of the Excel PV system design program. The report must be a maximum of 12 A4 pages long (excluding references). You can attach technical details such as datasheet of PV components as appendices. These will not be counted towards the above page limit. [See Appendix C for marking criteria] o Deliverable 4: Reflection (Satisfactory): You should reflect on the project and what you did, what you have learned about Stand Alone PV system design while completing this project and how you are going to be able to use it in the future. It should be clear and concise. It should be a maximum of 2 A4 pages long. You must demonstrate satisfactory performance (50% or above). [See Appendix D for marking criteria] 4. Resources: Some useful resources are listed below however there are a lot of online resources available. • Weather data: Course Moodle site (“Weather data” folder) • LCC, LCOE: Course Moodle site, NREL LCOE calculator (https://www.nrel.gov/analysis/tech-lcoe.html). • AS/NZS 4509.2:2010: Stand-alone power systems System design Standards Australia
COMP3065 Computer vision Coursework (40% of Module Mark) Submit an electronic copy via Moodle In class we have learned many techniques that help solve computer vision problems. Some techniques are discussed in details in the lecture or in the labs while some are only briefly discussed. In this coursework, you are required to apply these techniques to solve practical problems at your interest. You will implement/or use the techniques discussed in class or any computer vision algorithm you found through the text books or published papers, depending on the projects you select to work on. 1. Select a project. First, you need to select one of the following projects to work on. Note that the following only depicts the basic requirement of the project. You need to implement additional features at your choice in order to obtain higher marks for the coursework (see marking rubrics in moodle). Additional features could be UIs, additional steps/algorithms for improve results, being able to work with hard scenarios (such as blurred video for panorama generation, tracking multiple people at the same time) etc. Please talk to the module convenor if you are not sure whether what a good feature is for your project. a) Panorama generation from videos In this project, you are required to write a program that can successfully generate a panorama image from a given short video. You need to capture your own videos (at least 3 sets) for testing your program. The trick to capture a good video for panorama generation is to only rotate your body while taking the videos without moving yourself away. b) Person tracking from videos In this project, you are required to write a program that can track people from a given video. You need to capture your own videos (at least 3 sets) for testing your program. The output of your program should be the same video with bounding boxes indicating each person and their trajectories. c) Your own idea You can write a program to solve a computer vision problem that you are interested. This can be any of the topics covered in the class or not covered in the class but relevant to computer vision. You can also select a computer vision paper to implement. You do not need to implement the full paper as long as your program has the main idea. The scope of your own idea should be similar to project a, b, or harder. If you select your own idea, ensure you discuss with me what you want to do. The idea is subject to the module convenor’s approval. In general, I will allow it as long as it is not too simple to implement. 2. Write a program implementing your design. You are recommended to use Python although any programming languages are OK. You can use any libraries that can help you to achieve your tasks such as OpenCV, as long as it will not directly give you the output of the project you are working on. You cannot directly use the code you found online or from the lab sample code. Please consult me if you are not sure whether certain libraries are allowed. 3. Write a report (max 2500 words) which: • Describes the main objectives of your project and the key functionalities/features implemented . • Describes detailed steps included in your method and specific computer vision techniques employed. • Presents and explains the results obtained on the test images/videos. • Critically evaluates your method on the basis of those results; what are its strengths and weaknesses? This section of the report should make explicit reference to features of the results you obtained and how they compare to the expectations you had of your design. Assessment criteria: • Code: 50% • Report: o Description of key features of the implementation: 25% o Explanation of the results obtained: 10% o Discussion of the strengths and weaknesses of the chosen approach and methods: 15% What to submit: two files to submit: 1) a zip file containing source code and test images/videos; 2) a report of max 2500 words as described above, due 23:59, May 05, 2025.
GEOG10001 Part 2 (8 %) Task outline: Imagine you are an NGO trying to publicise changes in food security in Asia between 2012 and 2022. This might be to suggest where past management/investment has worked to improve the situation, or it could help highlight those areas in need of aid. Write up to 300 (+/-10%) words explaining three variables you think would be suitable for an NGO to use to publicise improvements or reductions in Asian countries food security from 2012 to 2022. In other words, justify why they are good for understanding food insecurity. The data is at the country spatial scale and the variables chosen should highlight those countries in the region that improved or decreased in food security from 2012 to 2022. Use concepts from the lectures and tutorials to briefly justify the use of these variables. Please include references in APA format. Your reference list will not be counted in the word count. (8 %) You could use https://www.ipcinfo.org/ipc-country-analysis/en to help understand what concerns have been identified in countries in Asia during the 2012 to 2022 time period. You can also make use of the FAO mapping data https://www.fao.org/interactive/state-of-food-security-nutrition/2-1-1/en to help get a sense of what countries have declined in or increased in food security. Please do not use these maps in your submission. This is only 300 words, so does not require an introduction or conclusion. Try to use a similar amount of words addressing each variable/indicator. To reference the FAO Food security spreadsheet please follow the directions here: https://library.unimelb.edu.au/recite/referencing-styles/apa7#datasets You can use the LMS weblink and the date of access of the spreadsheet in LMS. Data requirements: Instead of the 2017 FAO data used in the tutorial please use data from the FAO food security database Tutorial_7data_10042025.csv Meta. data: The data has been collated from https://www.fao.org/faostat/en/#data in the Asia region for the period between 2012 to 2022. These data include averages over 3 year periods, so the 2012 data may be an average of 2011 to 2013 data for example. I have also added a column of difference data (diff) for each variable between time periods. You might want to think about what a significance difference is for each variable, and how you could represent this in a map. Information on the data, can be found on the fao website. I have provided an xls file Tutorial_7dataINFO_10042025 with a header row that contains more details than the .csv file can contain to be able to import. As we are joining data to the layer ‘world countries generalized’ the country names need to match. The FAO_Country names are those that the FAO uses, and the Country names are those that match with ‘world countries generalized’. There are three differences where Taiwan, Hong Kong and Macao are separated out in the FAO data. These data will not be imported as separate country polygons do not exist for them in ‘world countries generalized’. The mainland China will, therefore, erroneously apply for all these regions.
ENG2003 Assignment *Please note that the hand-in document must be in Word or PDF format. Question 1. Given that, a Library Management System has the following database with four tables: MEMBER, BOOK, AUTHOR, and BORROW. MEMBER MemberID FirstName LastName JoinDate 1 Alice Green 2020-03-15 2 Tom Harris 2021-07-22 3 Bob Allen 2022-08-23 BOOK BookID Title AuthorID 101 The Silent Forest 501 102 Beyond the Horizon 502 103 The Lost Expedition 502 AUTHOR AuthorID FirstName LastName Nationality 501 Emily Stone American 502 James Carter British BORROW BorrowID MemberID BookID BorrowDate DueDate ReturnStatus 1001 1 101 2023-11-01 2023-11-15 Overdue 1002 2 102 2023-11-05 2023-11-19 Returned 1103 3 102 2024-01-03 2024-01-08 Returned (1) Please identify the primary keys and foreign keys for these four tables. (2) Draw the RM diagram for these four tables (remember to show the attributes and keys of the entities in the diagram). (3) Write the SQL code to count the number of author from the AUTHOR table (4) Write a SQL query to list all authors and the number of books they have written. The result should include the author's first name, last name, and the book count. (5) Draw the table to show the results of the following query: SELECT BorrowID, MemberID, BookID, BorrowDate, DueDate, ReturnStatus FROM BORROW WHERE BorrowDate BETWEEN '2023-11-01'AND '2023-11-10' ORDER BY BorrowDate ASC; Question 2. The university's academic management database is designed to streamline the administration of courses, departments, professors, students, and facilities. This comprehensive system supports efficient scheduling, class management, and enrollment processes. By capturing the intricate relationships between these entities, the database ensures smooth coordination of academic activities, from assigning professors to classes, to managing student enrollments and classroom allocations. Here are the RM diagram for the database: Please write SQL statements to answer the following queries according to the above RM diagram: (1) List the names of all students who have not enrolled in any classes. (2) Find the average number of credits per course offered by the "Mathematics" department. (3) List the names of all students enrolled in courses taught by professors with the rank of "Associate Professor." (4) List the names of all courses that do not have any enrollments. (5) Find the total number of students enrolled in each course, sorted by enrollment count in descending order.
COMM5615 Assignment 3: Self-Induced Limits to Growth Due Date: Submit via Moodle by April 28 (2 pm AEST*) Weighting: 30% Length: 8-page written report (maximum) * Australian Eastern Standard Time (Sydney time) PURPOSE: This assignment will continue to build your understanding of managing growth in the presence of self-induced limits to business growth. I strongly suggest that you carefully read Chapter 7 of the Morecroft textbook to understand the assumptions for each equation in the model. I also strongly suggest that you review the slides from our class sessions in Weeks 5 and 7 that cover the market growth model BEFORE working on this assignment. 1. Open the “Customer Response to Availability.mdl” model provided as part of the assignment files. Go through the model equations and notice that some parameter values differ from the version of the model in the textbook or that we discussed in class. You will also notice that the Sales Force is represented as an auxiliary variable in this model and that the equation includes the STEP function. The STEP function enables us to test the effects of a step change in the Sales Force on the model behaviour. 1.1. Change the simulation name to “Base Case”, simulate the model, and explain the dynamic behaviour. 1.2. Change the Step Test Input to 4, change the simulation run name to “4 More Salesforces” and then simulate the model. Next, Change the Step Test Input to 16, change the simulation run name to “2 Times More Salesforces” and then simulate the model. Next, Change the Step Test Input to 40, change the simulation run name to “5 Times More Salesforces” and then simulate the model. Examine the graphs of the simulation runs and provide a written explanation of the dynamic behaviour for each simulation run. In addition, explain the differences between the four simulation runs. As part of your answer to this question, include the graphs you believe are important for your explanation. At a minimum, you should include and explain the graphs of Order Backlog, Utilization of Capacity, and Delivery Delay Indicated for each simulation run , and then explain any differences between the simulation runs. 1.3. Add the same structure to implement Stepwise change in the “Production Capacity ” (including Base Production Capacity Size, Production Capacity Step Test Input, Time to Initiate Production Capacity Step Test Input). Change the Production Capacity Step Test Input to 50 , change the simulation run name to “50 More Production Capacity” and then simulate the model. Next, Change the Production Capacity Step Test Input to 200, change the simulation run name to “2 Times More Production Capacity” and then simulate the model. Next, Change the Step Test Input to 400 , change the simulation run name to “ 3 Times More Production Capacity” and then simulate the model. Similarly, examine the graphs of the simulation runs and provide a written explanation of the dynamic behaviour for each simulation run. Importantly, you need to explain the dynamic behaviour rather than just describe it. Your written explanation should proceed step-by-step, explaining at each juncture in the graphs what is occurring. You should be able to explain the dynamic behaviour in plain terms. Aim for a lucid text that tells an interesting and informative ‘story’ about the simulations. Remember, the graphs of one or more simulation runs do not explain themselves. For each graph , there should be an accompanying storyline. The section titled ‘Simulation Experiments’ in the Morecroft textbook Chapter 7 illustrates how to explain dynamic behaviour alongside time charts, so look at this section for an example. But be sure to use your own words in your explanation because the behaviour of this version of the model differs from the version discussed in the textbook. 2. Close the “Customer Response to Availability.mdl” model and open the “Capacity Expansion.mdl” model provided with the assignment files. Go through the model equations and notice that several of the parameter values differ from the version of the model in the textbook or that we discussed in class. 2.1. Change the simulation name to “Base Cap Expansion”, simulate the model, and examine the dynamic behaviour. 2.2. Change the Step Test Input to 4, change the simulation run name to “4 More Salesforces” and then simulate the model. Next, Change the Step Test Input to 16, change the simulation run name to “2 Times More Salesforces” and then simulate the model. Next, Change the Step Test Input to 40, change the simulation run name to “5 Times More Salesforces” and then simulate the model. Examine the graphs of the simulation runs and provide a written explanation of the dynamic behaviour for each simulation run. In addition, explain the differences between the three simulation runs. As part of your answer to this question, include the graphs you believe are important for your explanation. At a minimum, you should include and explain the following graphs: i. Graph of Customer Orders AND Order Fill Rate showing the “4 More Salesforces” run ii. Graph of Customer Orders AND Order Fill Rate showing the “2 Times More Salesforces” run iii. Graph of Order Backlog, Production Capacity, and Order Fill Rate showing the “ 5 Times More Salesforces” run iv. Graph of Utilization of Capacity showing all three runs v. Graph of Delivery Delay Recognized by Factory showing all three runs Importantly, you need to explain the dynamic behaviour rather than just describe it. Note: you can create custom graphs in Vensim from Tools>Control Pannel>Custom Graphs. 3. Close the “Capacity Expansion.mdl” model and open the “Business Growth (full model).mdl” model provided with the assignment files. This is the complete model including all of the feedback loops we have discussed and that are also explained in Chapter 7 of the Morecroft textbook. Go through the model equations and notice that several of the parameter values differ from the version of the model in the textbook or that we discussed in class. Change the simulation name to “Base full model”, simulate the model, and examine the dynamic behaviour. Next, change the Normal Sales Force Productivity to 13.5 to test the effects of increasing the productivity of the sales force by 50% , change the simulation run name to “ Increase Productivity” and then simulate the model. Examine the graphs of the simulation runs and provide a written explanation of the dynamic behaviour for each simulation run. In addition, explain the differences between the two simulation runs. As part of your answer to this question, include the graphs you believe are important for your explanation. At a minimum, you should include and explain the following graphs (note, you can resize the graphs so that they do not take up too much space): i. Graph of Order Backlog showing both simulation runs ii. Graph of Delivery Delay Recognized by Customers showing both runs iii. Graph of Utilization of Capacity showing both runs iv. Graph of Capacity Expansion Fraction showing both runs Importantly, you need to explain the dynamic behaviour rather than just describe it. In addition, provide a recommendation for redesigning the Capacity Investment policy (see slides for Week 7) within the firm to overcome (as much as possible) the self-induced limits to growth from product availability. For your answer, do not change or add any model structure; just modify existing parameter values. Make sure the revised parameter values you adopt are feasible and can be implemented in the real company. Your revised policies, when combined together, should enable the firm to grow Customer Orders as much as possible. Describe the changes in parameter values for your revised policy (i.e. the parameter values you adopt and why), test the new policy by running the simulation, and discuss the results. Include a comparative graph of customer orders in your answer to this question that shows the “ Increase Productivity ” run and your new run: “Revised Capacity Policy”. Discuss the reasons for the differences and improvements in the dynamic behaviour of the model. Also discuss the feasibility of implementing your revised policy in real organisations. 4. Keep using the “Business Growth (full model).mdl” model for this question. An important assumption in the model thus far is that the market potential is unlimited. Extend the model to include a limited market potential by distinguishing between Normal Sales Force Productivity and Actual Sales Force Productivity, and where the actual sales force productivity decreases as the company grows and approaches the market potential limit. Add the following two auxiliary variables to extend the model: Actual Sales Force Productivity and Potential Orders per Month. Then define the equations for those variables as: Potential Orders per Month = 1300 {units: Systems/Month} Actual Sales Force Productivity = Normal Sales Force Productivity * (1- (Sales Force*Normal Sales Force Productivity/Potential Orders per Month)) {units: Systems/Month/ Salespople} The equation for Customer Orders also changes to: Customer Orders = Sales Force*Actual Sales Force Productivity*Effect of Delivery Delay on Orders {units: Systems/Month} Set the following parameters equal to: Normal Sales Force Productivity =9 {units: Systems/Month/ Salespople} Price = 8,000 {units: $/Systems} Change the simulation name to “ Limited Market” and simulate the model and examine the dynamic behaviour. Provide a written explanation of the dynamic behaviour of the model. As part of your answer to this question, include the graphs you believe are important for your explanation. At a minimum, you should include and explain the (separate) graphs of Delivery Delay Recognized by Customers, Customer Orders, Actual Sales Force Productivity, and Capacity Expansion Fraction.
