ACT-SCI-654-HW-10.pdf - ACT SCI 654 Homework#10 1 ACT SCI 654 HW 10 Frees 5.4 1 a dat<-read.csv"C\Users\22916\OneDrive\Documents\data\TermLife.csv Term<

ACT-SCI-654-HW-10.pdf - ACT SCI 654 Homework#10 1 ACT SCI...

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ACT SCI 654 Homework #10 November 15, 2019 1
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ACT SCI 654 HW 10 Frees 5.4 1 a dat<- read.csv ( "C:\\Users\\22916\\OneDrive\\Documents\\data\\TermLife.csv" ) Term<- dat[ which (dat $ FACE > 0 ), c ( "FACE" , "EDUCATION" , "INCOME" , "NUMHH" , "MARSTAT" , "GENDER" , "AGE" )] Term $ LOGFACE<- log (Term $ FACE) Term $ LOGINCOME<- log (Term $ INCOME) Term $ GENDER<- factor (Term $ GENDER, labels = c ( "Female" , "Male" )) Term $ Marital <- factor (Term $ MARSTAT, labels = c ( "Single" , "Married" , "LivingTog" )) model1<- lm (LOGFACE ~ LOGINCOME + EDUCATION + NUMHH + MARSTAT + AGE + GENDER, data = Term) summary (model1) ## ## Call: ## lm(formula = LOGFACE ~ LOGINCOME + EDUCATION + NUMHH + MARSTAT + ## AGE + GENDER, data = Term) ## ## Residuals: ## Min 1Q Median 3Q Max ## -5.7719 -0.8990 0.0914 0.8407 4.6519 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 2.527493 0.907585 2.785 0.00574 ** ## LOGINCOME 0.450635 0.078786 5.720 2.84e-08 *** ## EDUCATION 0.218198 0.038729 5.634 4.45e-08 *** ## NUMHH 0.263542 0.072279 3.646 0.00032 *** ## MARSTAT -0.163366 0.268683 -0.608 0.54369 ## AGE -0.003529 0.007928 -0.445 0.65656 ## GENDERMale 0.917195 0.352626 2.601 0.00981 ** ## --- ## Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 ## ## Residual standard error: 1.511 on 268 degrees of freedom ## Multiple R-squared: 0.3616, Adjusted R-squared: 0.3474 ## F-statistic: 25.3 on 6 and 268 DF, p-value: < 2.2e-16 LOGINCOME, EDUCATION, NUMHH and GENDER are statistically siginificant at 5% siginificance level, MARSTAT and AGE are not statistically siginificant. Multiple R-squared is 0.3616 and adjusted R-squared is 0.3474. b library (car) ## Loading required package: carData ## ## Attaching package: car ## The following object is masked from package:psych : ## 2
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ACT SCI 654 HW 10 ## logit ## The following object is masked from package:dplyr : ## ## recode vif (model1) ## LOGINCOME EDUCATION NUMHH MARSTAT AGE GENDER ## 1.248831 1.169355 1.396531 1.863770 1.140301 1.743312 vcov (model1) ## (Intercept) LOGINCOME EDUCATION NUMHH ## (Intercept) 0.823711178 -4.429478e-02 -1.023236e-02 -0.0122496610 ## LOGINCOME -0.044294779 6.207179e-03 -1.122578e-03 -0.0008869791 ## EDUCATION -0.010232359 -1.122578e-03 1.499961e-03 0.0002181075 ## NUMHH -0.012249661 -8.869791e-04 2.181075e-04 0.0052242181 ## MARSTAT 0.008683968 4.349741e-05 -1.291455e-05 -0.0058565346 ## AGE -0.002686168 -4.177959e-05 -1.491319e-05 0.0001911944 ## GENDERMale -0.017796273 -4.642548e-03 1.203593e-03 -0.0009041181 ## MARSTAT AGE GENDERMale ## (Intercept) 8.683968e-03 -2.686168e-03 -0.0177962732 ## LOGINCOME 4.349741e-05 -4.177959e-05 -0.0046425484 ## EDUCATION -1.291455e-05 -1.491319e-05 0.0012035930 ## NUMHH -5.856535e-03 1.911944e-04 -0.0009041181 ## MARSTAT 7.219058e-02 -8.463202e-05 -0.0548271259 ## AGE -8.463202e-05 6.286075e-05 -0.0001548341 ## GENDERMale -5.482713e-02 -1.548341e-04 0.1243450002 Because VIF for all variables are less than 10, there are not severe collinearity between these variables. From
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