Yummy Lunch Restaurantneeds 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 343965.68 5970.26 41522.96 2.8 5.68 Standard Error 5307.89863 139.0845281 582.1376385 0.142857 0.051030203 Median 345166.5 6032 41339.5 3 5.75 Mode #N/A 5917 #N/A 3 6 Standard Deviation 37532.51115 983.47613 4116.334718 1.010153 0.360838027 Sample Variance 1408689393 967225.2984 16944211.51 1.020408 0.130204082 Sum 17198284 298513 2076148 140 284 Count 50 50 50 50 50 SUMMARY OUTPUT Regression Statistics Multiple R 0.77 R Square A Adjusted R Square B Standard Error 25139.79 Observations 50.00 ANOVA df SS MS F Significance F Regression C 40585376295 F H 3.0831E-08 Residual D 28440403984 G Total E 69025780279 Coefficients Standard Error t Stat P-value Intercept -68363.1524 78524.7251 -0.8706 0.3886 People 6.4394 3.7051 I 0.0891 Income 7.2723 0.9358 J 0.0000 Competitors -6709.4320 3818.5426 K 0.0857 Price 15968.7648 10219.0263 L 0.1251 You are required to; b) Derive the regression model

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Yummy Lunch Restaurantneeds 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	343965.68	5970.26	41522.96	2.8	5.68
Standard Error	5307.89863	139.0845281	582.1376385	0.142857	0.051030203
Median	345166.5	6032	41339.5	3	5.75
Mode	#N/A	5917	#N/A	3	6
Standard Deviation	37532.51115	983.47613	4116.334718	1.010153	0.360838027
Sample Variance	1408689393	967225.2984	16944211.51	1.020408	0.130204082
Sum	17198284	298513	2076148	140	284
Count	50	50	50	50	50

SUMMARY OUTPUT

Regression Statistics
Multiple R	0.77
R Square	A
Adjusted R Square	B
Standard Error	25139.79
Observations	50.00

ANOVA
	df	SS	MS	F	Significance F
Regression	C	40585376295	F	H	3.0831E-08
Residual	D	28440403984	G
Total	E	69025780279

	Coefficients	Standard Error	t Stat	P-value
Intercept	-68363.1524	78524.7251	-0.8706	0.3886
People	6.4394	3.7051	I	0.0891
Income	7.2723	0.9358	J	0.0000
Competitors	-6709.4320	3818.5426	K	0.0857
Price	15968.7648	10219.0263	L	0.1251

You are required to;

b) Derive the regression model

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