Data on the price​ ($1000s) and the size​ (1000 ft2 sqaured) for 156 randomly selected houses that have been sold is summarized in the accompanying regression output and plots. Complete parts a and b ​a) Check the assumptions and conditions for inference. Select all that apply.     A. The residuals look random.   B. The residuals are nearly normal.   C. The scatterplot looks straight enough.   D. The residual plot and the scatterplot show consistent variability.   E. None of the assumptions and conditions for inference are satisfied.

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Data on the price​ ($1000s) and the size​ (1000 ft2 sqaured) for 156 randomly selected houses that have been sold is summarized in the accompanying regression output and plots. Complete parts a and b

​a) Check the assumptions and conditions for inference. Select all that apply.
 
 
A.
The residuals look random.
 
B.
The residuals are nearly normal.
 
C.
The scatterplot looks straight enough.
 
D.
The residual plot and the scatterplot show consistent variability.
 
E.
None of the assumptions and conditions for inference are satisfied.

 

Regression Output
Dependent variable is: Price
R squared = 92.2%
s = 51.05 with 156 -2 = 154 degrees of freedom
Coefficient SE(Coeff) t-ratio P-value
- 2.8680
-0.35 0.7237
180.906
8.098
4.238 42.69 ≤0.0001
Variable
Intercept
Size
Transcribed Image Text:Regression Output Dependent variable is: Price R squared = 92.2% s = 51.05 with 156 -2 = 154 degrees of freedom Coefficient SE(Coeff) t-ratio P-value - 2.8680 -0.35 0.7237 180.906 8.098 4.238 42.69 ≤0.0001 Variable Intercept Size
Price
800-
600-
400-
200-
-200-
ore 8
2 3 4 5
Size
Residuals
140-
-150+
0
Predicted
900
Count
-150
30-
Residuals
140
Transcribed Image Text:Price 800- 600- 400- 200- -200- ore 8 2 3 4 5 Size Residuals 140- -150+ 0 Predicted 900 Count -150 30- Residuals 140
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