You are required to find a management decision problem in any company This report requires you to build and estimate a model using Microsoft Excel to address a management problem. Using Excel, conduct analytics for the problem, and interpret the results. You are required to submit a technical report (2000 words) that discusses the managerial problem, the results, their implications and makes recommendations for improvement.
7.2.2 Overall Structure of the report
The weightage of the report will be approximately distributed as follows:
Section 1. Introduction (5%)
Section 2. Descriptive analysis (20%)
Section 3. Regression analytics (25%)
Section 4. Managerial interpretations and implications (40%)
Section 5. Conclusion (10%)
How to write the introduction section
In what follows, I would like to provide you with some suggestions on how to explore the provided EXCEL data to write the report. Let’s assume if you were given a data that had information about HR and in that data, you had variables such as age, gender, work experience, education, salary, tenure and so on.
How to define a problem or research question?
It is sufficient to define just one research question. In this example, you can propose a question such as “Does there exist a link between gender and wage differential?”. In other words, you want to study whether female employees and male employees have the same salary level if they have the same level of education, work experience…. It is also important to elaborate on why you think this is an interesting and relevant question. To do so, it’s a good idea to incorporate some existing academic references that address the link between gender and salary.
Use any of the following publications for describing a suitable decision problem for the excel based provided data set.
- Bögenhold, D., & Fachinger, U. (2000). The social embeddedness of consumption: towards the relationship of income and expenditures over time in Germany.
- Hoxby, C. M. (1998). How much does school spending depend on family income? The historical origins of the current school finance dilemma. The American Economic Review, 88(2), 309-314.
- Treas, J., & De Ruijter, E. (2008). Earnings and expenditures on household services in married and cohabiting unions. Journal of Marriage and Family, 70(3), 796-805.
How to write the descriptive analysis section:
2.1 Describe the variables by presenting them in a tabular form, such as shown below:
| Variables | Types | Description |
| Instructor | Categorical or numerical | |
| Office Hours | ||
| Evaluation | ||
| Articles | ||
| Salary |
2.2 Check if the provided data requires any cleansing, such as Blank cell, abnormal/outbound values inside the column and them cleanse it using appropriate Excel techniques.
2.3 Report some suitable measures of central tendencies such as mean, median, standard deviation, frequency of the main data variables which are provided in the excel file. Also use the excel functions of max and min values to report the corresponding values from the provided data.
2.4 Report relevant visualizations of the data using charts such as: Histogram, Bar chart and pie chart etc.
Section 3. Regression analytics (25%)
3.2. insert the scatter chart to represent the distribution and relationship of data
From the scatter chart interpret if the relationship between the variables is linear or nonlinear.
Also evaluate if by increasing the values of independent variables, the amount of variance is increased or decreased.
3.3 Regression
If the data you are dealing with in the regression is categorical, then convert it into numerical dummy variables first
Perform multiple regression (Y=b+a1*X1+a2*X2) of the provided cross sectional data in the excel file.
Interpretation and significance of t static, p-value and adjusted R2values need to be done in the context of the problem that is being analysed.
Remember to remove variables with p-values greater than 0.05 and t static less than 1 for an accurate regression analysis.
Use the forward, backward and stepwise regression methodologies to include/exclude the independent variables into/from the regression equation (in order to use only the most suitable independent variables in the regression), in order to make the regression analysis as accurate and meaningful as possible.
Report how much of the variance of the dependent variables is covered by the regression equation (generally it is more than 0.7, but if it is lower, then do several iterations, to add or exclude the independent variables from the regression equation, until the value of variance is around 0.7 or greater).
Section 4. Managerial interpretations and implications (40%)
4.1. Simulation, generate random numbers and use the developed regression model for analyzing – generate different scenarios and evaluate them
4.2. Suggestions and how the company or supply chain can improve the performance – recommendations – you can provide different recommendation for different scenarios
Section 5. Conclusion (10%)
Findings and report if the used method is suitable for the analysis
