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III. An appliance store collected data for 5 months (so n=5 (x, y)pairs) to study the linear relationship between their monthly Advertising Expenditures (ADVEXP, the independent variable aka “x”) and their monthly Sales Revenue (SALESREV, the dependent variable, aka “y”). The data collected was used to run an SLR (“Simple Linear Regression”) on Excel, and the results are given in the following tables:

1.   The Regression Summary Statistics Table

Regression Statistics
Multiple R	0.903696114
R Square	0.816666667
Adjusted R Square	0.755555556
Standard Error	605.5300708
Observations	5

2.   The Regression ANOVA Output Table

ANOVA
Source	df	SS	MS	F	Significance F
Regression	1	4900000	4900000	13.363636	0.035352847
Residual	3	1100000	366666.67
Total	4	6000000

3.   The Regression Coefficients Table

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	–100	635.085	- 0.1574	0.8849	- 2121.125	1921.1249
ADVEXP	7	1.91485	3.6556	0.0354	0.906079	13.093921

Q1: Write out the SLR (Simple Linear Regression) equation relating ADVEXP to SALESREV.

Q2: Use the SLR Equation given in this Excel output to predict the monthly SALESREV if the monthly ADVEXP = $250.

Answer 1

Q1: Write out the SLR (Simple Linear Regression) equation relating ADVEXP to SALESREV.

SALESREV = -100 + 7ADVEXP

Q2: Use the SLR Equation given in this Excel output to predict the monthly SALESREV if the monthly ADVEXP = $250.

SALESREV = -100 + 7ADVEXP

SALESREV = -100 + 7*250.

SALESREV = 1650