c. How useful is this model for predicting the monthly rent? d. What other varia
ID: 3056659 • Letter: C
Question
c. How useful is this model for predicting the monthly rent?
d. What other variables might explain the variation in monthly rent?
An agent for a real estate company in a large city would like to be able to predict the monthly rental cost for apartments, based on the size of the apartment, as defined by square footage. A sample of eight apartments in a neighborhood was selected, and the information gathered revealed the data shown below. For these data, the regression coefficients are bo 294.0733 and b10.9073. Complete parts (a) through (d) Monthly Rent (S) 950 1,500 800 1,500 1,950 900 1,800 1,300 Size (Square Feet 850 1,300 950 1,100 2,000 650 1,300 1,050 a. Determine the coefficient of determination, r', and interpret its meaning (Round to four decimal places as needed.)Explanation / Answer
SUMMARY OUTPUT of Regression of Monthly rent on Size
Regression Statistics
Multiple R
0.865867
R Square
0.749725
Adjusted R Square
0.708013
Standard Error
230.5022
Observations
8
ANOVA
df
SS
MS
F
Significance F
Regression
1
954962.3
954962.3
17.97363
0.005443
Residual
6
318787.7
53131.29
Total
7
1273750
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
294.0733
259.2598
1.13428
0.299952
-340.313
928.4592
Size
0.907328
0.214016
4.239532
0.005443
0.383649
1.431006
One can think of number of rooms location, floor, parking space, life of the building, nearby amenities, water facility and such other may be useful in predicting monthly rent.
SUMMARY OUTPUT of Regression of Monthly rent on Size
Regression Statistics
Multiple R
0.865867
R Square
0.749725
Adjusted R Square
0.708013
Standard Error
230.5022
Observations
8
ANOVA
df
SS
MS
F
Significance F
Regression
1
954962.3
954962.3
17.97363
0.005443
Residual
6
318787.7
53131.29
Total
7
1273750
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
294.0733
259.2598
1.13428
0.299952
-340.313
928.4592
Size
0.907328
0.214016
4.239532
0.005443
0.383649
1.431006
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