MGT 2009 Data and Statistics Academic Year 2022-23
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MGT 2009 Data and Statistics
Academic Year 2022-23
Assignment Requirements
Individual Assignment 2: Retail Performance Study
(60% of the final grade)
Retail Study Scenario
To recap, amidst wider economic uncertainty and ongoing challenges for UK High Street traders, figures for retail sales have been mixed. Based upon UK data for the 4th quarter of 2021, some retailers have shown signs of comparable, or indeed increased sales volumes, compared to pre- Covid levels. Others however have seen a drop in sales volumes. Meanwhile, in terms of profitability, pressures on retail businesses and their profit margins are coming from diverse sources and are increasing. This is the case for businesses who have only ‘bricks and mortar’ stores and no online presence, as well as those who maintain both.
As a consultant at RetailAnalysis Ltd, you have been tasked with producing a brief report on financial performance among UK retailers, based upon the analysis of a data set, as per the guidance below. Following the preliminary analysis of the data employing descriptive statistics and visualisations/ graphical presentations (Assignment 1), further analysis is required with the key aim of developing a model which can be used to make predictions of retailer profitability, as measured by Net Margin. The development of this model will also help the UK Government’s Department for Business, Energy and Industrial Strategy in assisting UK retailers with advice and financial support. To provide realistic advice, it is necessary to understand which factors are related to Net Margin and how strongly they are related.
Again, the data to be analysed relates to three main types of retailer and includes primary data (e.g. collected through an online survey of customers; on-site visits) and secondary data (e.g. publications/ online information from local authorities, government or chambers of commerce). The dataset is contained in the Retail Performance Data.xls file, and the explanation of all variables is found in the file Retail Performance Assignments_Variable Listing.docx. The dataset contains data relating to 32 variables for 230 retailers, each identified by a unique ID (Store ID). The variable Net Margin (Net Profit Margin) measures a retailer’s financial performance, and is expressed as a %.
You are to employ Multiple Linear Regression to analyse the data, identifying variables that act directly as predictors, or antecedents of Net Margin. It is part of your task to decide which variables to include as predictors in the regression analysis, bearing in mind the guidance below.
Analysing the data requires the statement of hypotheses to be tested. These hypotheses will be formulated based upon a review of some of the relevant literature on retail profitability, including in particular, peer-reviewed literature, but also where relevant e.g. professional body or governmental reports. These sources of literature are also needed when interpreting the findings.
§ The instructions below show the tasks you must complete and include in your report.
§ The tasks are structured into three sections, each of which must be included in your report.
§ Clear communication of the research findings and recommendations is essential.
§ The marks for each task are shown in brackets, for a total of 100 marks, which will be converted to a final mark out of 60% for Supplementary Assignment 2.
N.B. 5 marks will be allocated for layout, spelling, and grammar.
Section 1: Research Question(s) and Variable Selection and Hypotheses
As in all reports, you need to set out clear questions that you are attempting to answer in your report. Please clearly state the broader research questions in the introduction of the report. For example, what HRM related variables/ factors most affect… or alternatively.. are good predictors of Net Margin?
Note that students are expected to provide enough broader questions to cover all the main predictors in the final model (with a minimum of 2 broader questions). So 2 - 5 questions are required, but 2 is often sufficient. Answering one of the latter questions will therefore, possibly (though not necessarily), imply analysing data for more than one variable that falls under the broader category (e.g. there is more than one HRM-related variable covered by the example question above, but you may have only one HRM-related variable in the final model).
(5 marks)
Based on your knowledge and review of the literature on retailer profitability, and citing sources in your justification for choosing each predictor, formulate hypotheses (a null and alternative for each) between each of the 5 predictor/ independent variables and the outcome/ dependent variable Net Margin.
(15 marks)
Section 2: Data Analysis
3. Produce descriptive statistics for each variable you have selected in Task 2. Nominal and Ordinal variables also require a full frequency table in the report (Interval or Ratio variables do not require a frequency table in the report).
Also, and as appropriate, produce measures of correlation/ association i.e. Pearson’s r (Pr), between each of the variables you have selected in Task 2 and Net Margin
(10 marks)
4. Produce a multivariate regression model focusing on the relationships between the 5
predictor variables (in task 2) and Net Margin. You may include further predictor/ control
variables, up to a maximum of 3 additional variables, giving a potential maximum of 8
predictor variables in the model (if e.g. you are trying to increase R Square).
Aim for an R Square greater than .70 (i.e. .71 or more), to avoid penalties.
Penalties will be applied as follows:
R Square of 0.6 to 0.7: -2 marks
R Square of 0.5 to < .6: -3 marks
R Square of < .5: -8 marks
It is also essential to check for Multicollinearity [when considering each predictor, use a bench mark of Pr > 0.60 as indicative of a potential collinearity problem (so .61 or more)] and address the issue appropriately in formulating your model. Any collinearity issues with the final model will be penalised (once) by -5 marks.
Other potential penalties may apply e.g. re: dummy variables (see recorded video on Canvas).
Tip: Use the decimal place adjuster in Excel to see what your R Square rounds up to at two decimal places, or when checking for potential multicollinearity.
(10 marks)
Section 3: Discussion and Interpretation of Results
5. Discuss and interpret (citing supporting literature) the descriptive statistics and the measures of association/ correlation in Task 3 above. (10 marks)
6. Discuss and interpret the findings in relation to the model developed in Task 4 above. Does the model support your hypotheses? (20 marks)
7. Illustrate how your model could be used for predicting/ estimating the Net Margin for an
imagined retailer, choosing appropriate data for the predictors in your model when
calculating. (5 marks)
8. In your conclusions and recommendations, critically reflect on the factors that might limit the prediction accuracy of your regression model. Thus e.g., when one uses the regression model to predict Net Margin, what are the caveats and cautions to bear in mind? What other factors, considerations, or approaches, if any, might the UK Government’s Department for Business, Energy and Industrial Strategy take into account, when developing their plans for interventions to assist retailers. Use literature to corroborate your arguments.
(20 marks)
Assessment Criteria
The assignment will be assessed based on how you have executed each task. The marks for each task are shown in brackets.
§ Key area for assessment of section 1 is to what extent your hypothesis development is logical and draws on solid literature.
§ Key area for assessment of section 2 is the accuracy of your analysis.
§ Key areas for assessment of section 3 include the accuracy of your interpretation and analysis and the degree to which you identify the critical issues and engage in a critical, thorough and analytical discussion (with supporting literature).
The statistical analysis MUST be carried out using Microsoft Excel.
The assignment MUST be word processed – any statistical analysis (charts; tables; excel output) MUST be transcribed into the word document. Please use Times New Roman, Size 12. Double line spacing should be used. Please insert page numbering.
Harvard referencing should be used.
Late assignments which are not the subject of Extenuating Circumstances (EC) will be penalized 5% for every day late, or part thereof, deducted from the final mark. This includes weekends but excludes days that the University is officially closed. Work that is submitted more than five days after the deadline will be given a mark of zero. Where students are citing a medical reason for late submission it should be accompanied by a medical certificate. Except in extreme circumstances, extensions will not be permitted retrospectively.
The word limit for this assignment is 1,800 (+/- 10% rule applies), excluding the cover page, tables, appendices and reference list.
Submission:
You are required to submit two files for this assignment:
1. The word processed report; and
2. The excel file containing your workings for the statistical analyses.
The assignment should be submitted via Turnitin in Canvas by the deadline of
Monday 12th December 2022 [23:59].
See: ‘Student Guide – How to use to Turnitin in Canvas’ for guidance on using Turnitin in Canvas;
See Appendix: Turnitin Originality Report and Plagiarism for further guidance.
Feedback
Queen’s University policy is that students are provided with feedback within 3 weeks of submission of their coursework. We anticipate providing each of you with written feedback by the end of this time period.
Appendix: Turnitin Originality Report and Plagiarism
Key points
• The percentage given is not a percentage of plagiarism BUT of the level of content taken from somewhere else. This may be referenced correctly and therefore completely acceptable. So if this is high, it does not mean to say that a student has plagiarised. It may be the case that they have read widely and included a number of sources which have been referenced correctly. Likewise a low percentage does not necessarily indicate a lack of plagiarism. There may be fewer sources used but ideas may be taken from someone else and the credit not given to them.
• If referencing is something that you are unsure about, there is help available through Queen’s Learning Development Service- http://www.qub.ac.uk/directorates/sgc/learning/WritingSkillsResources/Referencing/
• If you click on the tab for Harvard referencing, you can access a short video tutorial called cite2write which is very helpful.
• According to Cottrell (2008:28) plagiarism is;
“using the work of others without acknowledging the source of information or inspiration”.
Examples
o Summarising a text using virtually the same words that have been used (e.g. journals, books, websites).
o Using but not acknowledging the ideas of others.
o Re-writing authors’ work in your own words but not giving them credit.
2022-12-01