Multiple Regression This data is based on a random sample of housing sales in Newark, DE from 2005 to 2008 The total sample size is 131 houses The dependent variable is PRICE in $1,000s (the actual sale price is divided by 1,000) We will look at four independent variables AREAThe total square footage of the house LOTSIZEThe size of the lot in acres BEDROOMSThe number of bedrooms in the house BATHDUMA dummy variable for the number of full bathrooms where 1 2 or more bathrooms, 0 1 bathroom Summary of Fit RSquare 1 RSquare Adj 0 6934 Root Mean Square Error 34 9853 Mean of Response 237 2661 Observations (or Sum Wgts) 131 Analysis of Variance Source DF Sum of Squares Mean Square F Ratio Model 2 364730 65 3 4 Error 5 6 1223 97 Prob F It Intercept 56 7669 19 0557 2 979 0 0035 AREA 0 0496 0 0093 5 3322

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Question: Multiple Regression. This data is based on a random sample of housing sales in Newark, DE from 2005 to 2008.The total sample size is 131

Multiple Regression.This data is based on a random sample of housing sales in Newark, DE from 2005 to 2008.The total sample size is 131 houses.The dependent variable is PRICE in $1,000s (the actual sale price is divided by 1,000).We will look at four independent variables.

AREAThe total square footage of the house
LOTSIZEThe size of the lot in acres
BEDROOMSThe number of bedrooms in the house
BATHDUMA dummy variable for the number of full bathrooms where 1 = 2 or more bathrooms, 0 = 1 bathroom

Summary of Fit RSquare 1 RSquare Adj 0.6934 Root Mean Square Error 34.9853 Mean of Response 237.2661 Observations (or Sum Wgts) 131 Analysis of Variance Source DF Sum of Squares Mean Square F Ratio Model 2 364730.65 3 4 Error 5 6 1223.97 Prob > F <.0001 c. total parameter estimates term estimate std error t ratio prob>It/ Intercept 56.7669 19.0557 2.979 0.0035 AREA 0.0496 0.0093 5.3322 <.0001 lotsize bedrooms bathdum what is the degrees of freedom for model this a whole number>

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