Case 4 – House Price. A small data set contains information on House Price (Y) in dollars, as well as predictors: number of Cars
the garage can hold (X1), the Age of the house in years (X2), and the number of Rooms in the house (X3). Consider the regression output below and determine the missing values A through F. Dependent variable is: Price R squared = AAAA R squared (adjusted) = 57.3% s = BBBB with 22 - 4 = 18 degrees of freedom Source Sum of Squares df Mean Square F-ratio Regression 6892069096 DDDD 2297356365 CCCC Residual 3975520758 18 220862264 Variable Coefficient s.e. of Coeff t-ratio prob Constant -26737.5 21074 -1.27 0.2207 Cars 6185.10 6640 0.932 0.3639 Age -333.303 757.8 EEEE FFFF Rooms 11154.6 2524 4.42 0.0003
KEY:
(A) = 63.4%
(B) = 14861
(C) = 10.4
(D) = 3
(E) = –0.44
(F) = 0.66
KEY:
(A) = 63.4%
(B) = 14861
(C) = 10.4
(D) = 3
(E) = –0.44
(F) = 0.66
1 answer:
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Answer:
An we can use the normal standard table and the following difference and we got this result:
Step-by-step explanation:
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represent the mean
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We have a sample size of n = 54 and we want to find this probability:
And for this case since the sample size is large enough >30 we can apply the central limit theorem and then we can use this distribution:
And we can use the z score formula given by:
And replacing we got:
An we can use the normal standard table and the following difference and we got this result:
