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Wal-Mart is the second-largest retailer in the world. The data file (Wal-Mart Revenue 2004-2009 data.xlsx) is...

Wal-Mart is the second-largest retailer in the world. The data file (Wal-Mart Revenue 2004-2009 data.xlsx) is posted below the case study two file, and it holds monthly data on Wal-Mart’s revenue, along with several possibly related economic variables.

  1. Develop a multiple linear regression model to predict Wal-Mart's revenue, using CPI, Personal Consumption, and Retail Sales Index as the independent variables. You also need to create residual plots and scatter plots by selecting residual plots box and line fit plots box under “Residuals”. Note: Those plots are for the individual independent variables.
  2. To generate the residual plot for the entire model, you need to follow the instruction below. Check the Residuals box under “Residuals” and Excel will generate predicted values and residuals at the bottom of the output for the multiple regression model. Then, highlight these two output values to create the residual plot by Excel’s scatter chart (Insert tab > Charts > Scatter chart). Comment on what you see on the plot. Note: This residual plot is for the entire model.
  3. Does it seem that Wal-Mart’s revenue is closely related to the general state of the economy? Use all the plots and statistical criteria on the Regression Analysis output generated by Excel to explain it.

Identify and delete those five cases (rows) corresponding to December revenue. Then, use the new data set (without the December data) to complete the following problems.

  1. Develop a multiple linear regression model to predict Wal-Mart's revenue, using CPI, Personal Consumption, and Retail Sales Index as the independent variables. You also need to create residual plots and scatter plots by selecting residual plots box and line fit plots box under “Residuals”. Note: Those plots are for the individual independent variables.
  2. Check the Residuals box under “Residuals” and Excel will generate predicted values and residuals at the bottom of the output for the multiple regression model. Then, highlight these two output values to create the residual plot by Excel’s scatter chart (Insert tab > Charts > Scatter chart). Comment on what you see on the plot. Note: This residual plot is for the entire model.
  3. Does it seem that Wal-Mart’s revenue is closely related to the general state of the economy? Use all the plots and statistical criteria on the Regression Analysis output generated by Excel to explain it.
  4. Compare these two multiple regression models, and decide which of these two models is better? Use R-square values, adjusted R-square values, Significance F values, p-values, scatter plots and residual plots to explain your answer.

Solutions

Expert Solution

R² 0.716
Adjusted R² 0.703
R   0.846
Std. Error   2.406
n   68
k   3
Dep. Var. Revenue
ANOVA table
Source SS   df   MS F p-value
Regression 935.1778 3   311.7259 53.85 1.72E-17
Residual 370.5055 64   5.7891
Total 1,305.6833 67  
Regression output confidence interval
variables coefficients std. error    t (df=64) p-value 95% lower 95% upper
Intercept 58.6340
CPI -0.2915 0.0693 -4.206 .0001 -0.4299 -0.1531
Personal Consumption 0.00001247 0.00000243 5.136 2.84E-06 0.00000762 0.00001732
Retail Sales Index 0.00005849 0.00001080 5.415 9.83E-07 0.00003691 0.00008006

The multiple linear regression model to predict Wal-Mart's revenue, using CPI, Personal Consumption, and Retail Sales Index as the independent variables is:

Revenue = 58.6340 - 0.2915*CPI + 0.00001247*Personal Consumption + 0.00005849*Retail Sales Index

The residual plots are:

Wal-Mart’s revenue is closely related to the general state of the economy.

orchestra answered 1 year ago
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