Question:

Juan Martinez wants to develop a good regression model that can predict the market value of a firm. He has data that is given in the following table.

The following are the results of a single variable regression run in Excel in which he modeled market value in terms of assets only.

1. [10 points] Based on the computer output, evaluate the regression model.
2. [10 points] Suppose that a firm had assets of $1,800 million. Calculate a 95.4% confidence interval for this firm’s market value.
   Subsequently, Juan decided to see if he could get an improved regression model by adding sales and
   employees as independent variables. The results of the regression are presented in the following Excel
   spreadsheet.

   Regression Statistics
   Multiple R			0.7697
   R Square			0.5925
   Adjusted R Square			0.4814
   Standard Error			636.9900
   Observations			15
   Analysis of Variance
   	df	Sum of Squares		Mean Square		F	Significance F
   Regression	3	6489548.83		2163182.94		5.3312	0.0164
   Residual	11	4463318.77		405756.251
   Total	14	10952867.6
   	Coefficients	Standard Error		t Statistic		P-value	Lower 95%		Upper 95%
   Intercept	451.8043	242.1464		1.8658		0.0832	-81.1566		984.7652
   Assets	-0.0100	0.8770		-0.0115		0.9910	-1.9402		1.9201
   Sales	0.4698	0.7479		0.6281		0.5400	-1.1764		2.1159
   employees	-0.0169	0.0275		-0.6148		0.5486	-0.0776		0.0437

3. [10 points] Based on the computer outputs from both the first and second regression models, which of the two models would you prefer and why?
4. [10 points] What is multicollinearity? How would you check for multicollinearity?

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Solution: The downloadable solution consists of 5 pages
Deliverable: Word Document