A. Is the regression model statistically significant? [____] Support your answer with persuasive evidence from the MLR report

B. What does the regression coefficient 9.4313 mean in terms of the situation to GeoAnalytics non-technical management?

C. Explain the statistical meaning of the .0.9361 coefficient of determination. (Hint: it is the ratio of two values that explains something.)

D. Which of the independent variables has the highest probability of not contributing to explaining SelPrice? ____________ Justify your answer with information from the MLR output

E. What does it mean for the AgeYrscoefficient to be negative

F. If the local price index is 7.0, what would you expect a 20-year-old, 1,000-square-foot house with 1 bath, 6 rooms, and 2 bedrooms to sell for?

. 1. Regression. GeoAnalytics is a forecasting firm for real-estate companies. It has developed a multiple regression model for predicting the selling price of a house in various neighborhoods of Amarillo, Texas. The MLR program output for their sample data is given below. Use this output to answer the questions on the next page. The variables used are: LocPrice The local price index Baths The number of bathrooms Space The size of the living space, in thousands of square feet Rooms The number of rooms BedRms The number of bedrooms Age Yrs The age of the house, in years SelPrice The selling price, in thousands of dollars (the dependent variable) 1 I . *** MULTIPLE LINEAR REGRESSION ANALYSIS *** 7 Variables with 28 observations ar.d deperdent variable SelPrice Variable LocPrice Baths Space Rooms Bedroms Age Yes SelPrice *** DESCRIPTIVE STATISTICS *** Mean Variance 7.1861 8.0952 1.2679 0.1756 1.5117 0.3001 6.6786 1.3373 3.2857 0.5079 36.3214 190.8929 38.1571 200.4144 StaDev 2.8452 0.4190 0.5478 1.1564 0.7127 13.8164 14.1568 *** CORRELATION MATRIX *** LocPrice Baths LocPrice 1.0000 0.8777 Baths 0.8777 1.0000 Space 0.8278 0.8940 Rooms 0.7430 0.7575 Bedrms 0.6472 0.7264 Age Yes -0.3615 -0.2009 Sel Price 0.9163 0.9251 Space 0.8278 0.8940 1.0000 0.8405 0.7913 -0.1775 0.9217 Rooms 0.7430 0.7575 0.8405 1.0000 0.9244 0.0113 0.7771 Bedrms 0.6472 0.7264 0.7913 0.9244 1.0000 0.1069 0.7006 Age Yes SelPrice -0.3615 0.9163 -0.2009 0.9251 -0.1775 0.9217 0.0113 0.7771 0.1069 0.7006 1.0000 -0.2993 -0.2993 1.0000 Determinant of dependent variables' submatrix = 0.001038 t-Stat P(It!) Variable Constant LocPrice Baths Space Rooms BedRms Age Yes *** REGRESSION EQUATION *** Coeff Beta Store 1.0851 1.6228 0.3261 0.6959 9.4313 0.2792 5.2164 10.9290 0.4229 3.8963 0.7316 0.0598 2.1712 -1.9642 -0.0989 3.1149 -0.0413 -0.0403 0.0704 2.3319 1.8080 2.8050 0.3370 -0.6306 -0.5873 0.0297 0.0849 0.0106 0.7395 0.5351 0.5633 SelPrice = 1.085096 +1.622806* LocPrice +9.431342*Baths +10.928979*Space 1.964187*Bedrms -0.041317*Agers +0.731607*Rooms - *** ANALYSIS OF VARIANCE *** F-Ratio 51.2937 P(F) 0.0000 Source Regular Residuals Total Dege 6 21 27 Sumsa 5065.5438 345.6447 5411.1886 Mear. Sumsa 844.2573 16.4593 200.4144 Coefficient of determination (R-squared) Standard error of the estimate 0.9361 4.0570