1.-Interpret the following regression model Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -7.819e+05 7.468e+04 -10.470 < 2e-16 *** Lot.Size -5.359e-01 1.163e-01 -4.610 4.67e-06 *** Square.Feet 1.108e+02 1.109e+01 9.986 < 2e-16 *** Num.Baths 2.985e+04 9.650e+03 3.094 0.00204 ** API.2011 1.226e+03 9.034e+01 13.568 < 2e-16 *** dis_coast -7.706e+00 2.550e+00 -3.022 0.00259 ** dis_fwy 1.617e+01 1.232e+01 1.312 0.18995 dis_down 5.364e+00 3.299e+00 1.626 0.10429 I(dis_fwy * dis_down) -4.414e-04 5.143e-04 -0.858 0.39098 Pool 1.044e+05 2.010e+04 5.194 2.59e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 141400 on 832 degrees of freedom Multiple R-squared: 0.554, Adjusted R-squared: 0.5492 F-statistic: 114.8 on 9 and 832 DF, p-value: < 2.2e-16
here the null hypothesis
H0: Lot.Size=Square.Feet=Num.Bath=API.2011=dis_coast=dis_fwy=dis_down=I(dis_fwy*dis_down)=Pool=0
Ha: atleast one of the is different from zero
since the F-statisitc has p-value is less than typical level of significance alpha=0.05, so we reject H0 and conclude that atleast one of the regression coefficient is different from zero.
Lot.Size, Square.Feet, Num.Bath, API.2011, dis_coast are significant to explain the dependent variables but
dis_fwy , dis_down and I(dis_fwy * dis_down) are not significant, so these three may be removed from the model and re-analyze the data.
1.-Interpret the following regression model Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -7.819e+05 7.468e+04 -10.470 < 2e-16 *** Lot.Si...
2.-Interpret the following regression model Call: lm(formula = Sale.Price ~ Lot.Size + Square.Feet + Num.Baths + API.2011 + dis_coast + I(dis_fwy * dis_down * dis_coast) + Pool, data = Training) Residuals: Min 1Q Median 3Q Max -920838 -84637 -19943 68311 745239 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -7.375e+05 7.138e+04 -10.332 < 2e-16 *** Lot.Size -5.217e-01 1.139e-01 -4.581 5.34e-06 *** Square.Feet 1.124e+02 1.086e+01 10.349 < 2e-16 *** Num.Baths 3.063e+04 9.635e+03 3.179 0.00153 ** API.2011 1.246e+03 8.650e+01 14.405 < 2e-16...
2. 2. After we fit the model, the R commander output is provided below. Coefficients: (Intercept) -5.128e+03 1.103e+02 46.49 2e-16** Estimate std. Brror t value Pr(lt|) TEMP PERT TEM: FERT 1.45se-01 9.692e-03 -15.01 1.06e-12 3.110e+01 1.344e+00 23.13 2e-16* 1.397e+02 3.140e+00 44.51 < 2e-16** TEMPSQ FERTSO -1.334e-01 6.853e-03 19.46 6.46e-15 -1.144e+00 2.741e-02 41.74 <2e-16 signif. codes: 00.001 0.01 0.05 011 Residual standard error: 1.679 on 21 degrees of freedom Multiple R-squared: 0.993, F-statistic: 596.3 on 5 and 21 DF, p-value: 2.2e-16...
only part II is needed Regardless of your answer to (a), you come up with the following multiple regression model. b. Coefficients: Estimate Std. Error t value Pr>lt (Intercept) 72.2285 1.2697 56.89 2e-16 X2 X3 Residual standard error: 7.25 on 191 degrees of freedom Multiple R-squared: 0.494, Adjusted R-squared: 0.489 F-statistic: 93.3 on 2 and 191 DF, p-value: <2e-16 0.4590 0.0524-8.76 1.1e-15 0.4146 0.1290 3.21 0.0015** I) What percentage of the total variation in Life Expectancy can you explain with...