SUMMARY OUTPUT

Regression Statistics

Multiple R 0.79

R Square A

Adjusted R Square B

Standard Error 26563.53

Observations 34.00

ANOVA

df SS MS F Significance F

Regression C 33447876745 F H 7.94E-06

Residual D 20463013035 G

Total E 53910889780

Yummy Lunch Restaurant needs to decide the most profitable location for their business expansion. Marketing manager plans to use a multiple regression model to achieve their target. His model considers yearly revenue as the dependent variable. He found that number of people within 2KM (People), Mean household income(income), no of competitors and price as explanatory variables of company yearly revenue.

The following is the descriptive statistics and regression output from Excel.

Revenue People Income Competitors Price

Mean 341222 5930.117647 41588.70588 2.764705882 5.70588235

Standard Error 6931.732927 186.1458313 750.4835702 0.179416878 0.05631924

Median 338668.5 5967.5 41400 3 5.75

Mode #N/A 5917 #N/A 3 6

Standard deviation 40418.60125 085.407388 4376.033596 1.046171187 0.32839479

Sample Variance 1633663327 1178109.198 19149670.03 1.094474153 0.10784314

Sum 11601548 201624 1414016 94 194

Count 34 34 34 34 34

Coefficients Standard Error t Stat P-value

Intercept -122968.76 96520.13 -1.27 0.21

People 6.86 4.35 I 0.13

Income 7.64 1.21 J 0.00

Competitors -9228.87 5127.57 K 0.08

Price 22997.52 14114.89 L 0.11

You are required to;

a)Complete the missing entries from A to L in this output

b)Derive the regression model

c)Assess the independent variables significance at 10% level (develop hypothesis if necessary in the analysis)?

d)What does the ANOVA table above tell you about the model (develop hypothesis if necessary in the analysis)?