QUESTION 3
The managing director of a real estate company investigated how advertising budget (in $000s) and number of agents affected annual sales ($ million). He used data from 15 offices, and obtained the following regression output:
SUMMARY OUTPUT 

Regression Statistics 

Multiple R 
0.72 

R Square 
0.52 

Adjusted R Square 
0.44 

Standard Error 
7.36 

Observations 
15 

ANOVA 

df 
SS 
MS 
F 
Significance 

Regression 
2 
716.58 
358.29 
6.61 
0.01 

Residual 
12 
650.35 
54.20 

Total 
14 
1366.93 

Coefficients 
Standard Error 
t Stat 
Pvalue 
Lower 95% 
Upper 95% 

Intercept 
19.47 
15.84 
1.23 
0.24 
53.98 
15.05 

Advertising 
0.16 
0.06 
2.82 
0.02 
0.04 
0.28 

Agents 
0.96 
0.78 
1.24 
0.24 
0.73 
2.66 

1. The fitted regression model is:
y = 19.47 + 0.16*x1 + 0.96*x2
2. 52% of the variation in the model is explained. This is not a wellfitted model.
3. The hypothesis being tested is:
H0: β1 = 0
H1: β1 ≠ 0
The pvalue is 0.02.
Since the pvalue (0.) is less than the significance level (0.05), we can reject the null hypothesis.
Therefore, we can conclude that the slope is significant.
The hypothesis being tested is:
H0: β2 = 0
H1: β2 ≠ 0
The pvalue is 0.24.
Since the pvalue (0.24) is greater than the significance level (0.05), we fail to reject the null hypothesis.
Therefore, we cannot conclude that the slope is significant.
4. This is not the best model because one of the variables is insignificant and the explained variation is not good enough.
Please give me a thumbsup if this helps you out. Thank you!
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