FIN10004: Customers in the Corporate segment are often more likely to place larger orders than their consumer counterpart: Financial Statistic, Report, UTM, Malaysia

Part C 4. Hypothesis testing

a. Customers in the Corporate segment are often more likely to place larger orders than their consumer counterpart. Thus, the average quantity orders for corporate customers is more than the average quantity ordered for general consumers. Investigate this contention by carrying out an appropriate hypothesis test.

b. It is often felt that the average profit per transaction would be different between technology and office supplies categories. Test if there is a difference in the average total profit for technology and office supplies.

Note: sample data is to be used. 5. Correlation and regression Investigate the relationship between Sales and Profit. Estimate the linear relationship of the two variables and use the model to predict profit from sales.

Note: Use either Excel or Python to estimate the linear relationship and to predict sales.

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Full regression analysis with appropriate discussion should include the followings:

a. A scatterplot and a brief discussion

b. An estimate of the linear regression model c. The coefficients of correlation and determination

d. A test of the hypothesis that there is no linear relationship between sales and profit.

6. Conclusion (see mark rubric for information to include in this section)

7. Video – Presentation of results Provide a concise summary presentation in video form, showing yourself and the result (via PowerPoints or similar software package). Presentation for report – presented in the form of a business report to a senior manager who cannot be assumed to have any knowledge of statistical methods. – informative but concise and use a non-technical style that is, do not include in the report formulae, calculations, definitions of statistical terms or discussions on how graphs are constructed. Explain the results or statistics. – formulae, calculations, definitions of statistical terms (where appropriate) should be included in the appendices. – the values calculated have to be correctly analysed, discussed and interpreted, and that a written description of the main features of the tables and graphs that have been constructed is included. – This assignment emphasizes interpretation and analysis, not just the computation of statistics and construction of graphs. – marks will be deducted if these are inaccurate or incorrect.

Part C must contain the followings:

Part A Title page Table of contents Introduction (about half a page) Body of the report – Data presentation and descriptions of statistics Appendix (data set of 200 observation, Python code for the charts generated)

Part B: Body of the report on ‘confidence interval’ Appendix – workings for calculation of confidence interval Plus

Part C: Executive Summary

1. Report only the highlights of the findings.

2. Entice an Executive to read on.

3. Essentially a lively summary of the main conclusions.

4. No longer than one page; this is not counted in the word count and must be on a separate page from the rest of the report.

Analysis 1. Contains a thorough yet non-technical description of all the findings (graphs and tables will be included only where they help this discussion).

2. Details the results that were highlighted in the Executive Summary

3. Do not include any calculations here but include appropriate graphs, results and tables which are needed to support your discussions.

Conclusion

1. Report the findings and results of your work.

2. Essentially an expansion of the executive summary written from the point of view that the Executive Summary has not been read.

3. End with a discussion of the limitations of your analysis.

Appendices

1. Must be referred to in the main body of the report.

2. Must contain your selection of random numbers and related random data (Done in Part A).

3. Include the raw data, charts and tables that are not essential, but support the ANALYSIS section.

4. Include your EXCEL output for descriptive statistics, confidence intervals, hypothesis testing and regression. Include Python code and output for tasks 2 and 6.