Regression Analysis with a Minitab output
Assume that your company owns multiple retail outlets in cities across the United States. You conduct a study to determine if daily sales levels (in hundreds of dollars) can be predicted by the number of competitors that are located within a onemile radius of each location and city population (in thousands of people). Therefore, the dependent variable is SALES and the two independent variables are NUMBER OF COMPETITORS and CITY POPULATION. Your research team utilized Minitab software to create a Regression model. The results are shown below.
Regression Analysis: SALES vs. NUMBER OF COMPETITORS, CITY POPULATION
Analysis of Variance
Source 
DF 
Adj SS 
Adj MS 
FValue 

Regression 
2 
160.362 
80.181 
15.93 

NUMBER OF COMPETITORS 
1 
138.093 
138.093 
27.43 

CITY POPULATION 
1 
2.576 
2.576 
0.51 

Error 
7 
35.238 
5.034 

Total 
9 
195.600 
Model Summary
S 
Rsq 

2.24365 
81.98% 
Coefficients
Term 
Coef 
SE Coef 
TValue 
PValue 
VIF 
NUMBER OF COMPETITORS 
3.245 
0.620 
5.24 
0.001 
1.07 
CITY POPULATION 
0.0174 
0.0243 
0.72 
0.498 
1.07 
Regression Equation
SALES 
= 
38.21  3.245 NUMBER OF COMPETITORS + 0.0174 CITY POPULATION 
Study this output then answer the following questions:
RSquare value: 81.98% of the total variation in the explanatory variables in the regression model.
The pvalue of number of competitors is 0.001< 0.05 hence the variable is significant
The pvalue of city population is 0.498 > 0.05 hence the variable is not significant
Which independent variable is the most significant? number of competitors
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