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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...

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.

  • AREA                  The total square footage of the house
  • LOTSIZE            The size of the lot in acres
  • BEDROOMS   The number of bedrooms in the house
  • BATHDUM       A dummy variable for the number of full bathrooms where 1 = 2 or more bathrooms, 0 = 1 bathroom
Summary of FitRSquare1RSquare Adj0.6934Root Mean Square Error34.9853Mean of Response237.2661Observations (or SumWgts)131Analysis of VarianceSourceDFSum of Squares Mean SquareF RatioModel2364730.6534Error561223.97Prob > F<.0001C. Total130518950.89Parameter EstimatesTermEstimateStd Errort RatioProb>|t|Intercept56.766919.05572.9790.0035AREA0.04960.00935.3322<.0001LOTSIZE117.04716.93657<.0001BEDROOMS18.1655.97133.04210.0029BATHDUM35.47147.00065.0669<.0001What is R-square for the model (1)? I just want the numerical answer as a proportion. Use 4 decimal places and use theproper rules of rounding.
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