DSC 3120 Generalized Modeling Techniques with …



MGS 8040: Regression Analysis ExerciseConsider the following output of a regression model to predict home prices ($ thousand) in a region based on area of the house, age of the house, number of bedrooms and number of bathrooms:SUMMARY OUTPUTRegression StatisticsR SquareStandard ErrorObservations20ANOVA?dfSSMSFSignificance FRegression322.352980.588220.002804Residual12.16314Total504.8000????CoefficientsStandard Errort StatP-valueIntercept215.0003.577324.3141060.000614Age-1.0000.372639-2.805780.033304Area 0.0500.0237842.9588830.009756Bedrooms9.0002.0845433.0826560.007581Bathrooms13.0001.0000003.0000000.022561Fill the shaded cells above.The R-Square value for the regression is equal to: __________________It means that ______________________________________________________________________________________________________________________________________________The Standard Error is equal to _____________________Is the Regression significant at the 5% level? _____________At 1% ? ______________The F value for the regression is ______________The coefficient -1.00 for age means that _________________________________________________________________________________________________________________Based on the regression above, the predicted value (in thousands of dollars) for a new home (age=0) that is 2000 sq. ft in area, with 3 bedrooms and 3 bathrooms is __________________The Margin of Error for the 95% prediction interval for your prediction above is approximately ____________________________You are testing the relationship between some X and Y for three different materials, A, B, and C. You wish to account for the material used in your regression analysis. Define the dummy variable values for a few observations of material type shown below: YXMaterialDummy ADummy B207A186B155B144C185C176A115A ................
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