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# 605 Class Note for STAT 462 at PSU

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KARMA
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This 9 page Class Notes was uploaded by an elite notetaker on Friday February 6, 2015. The Class Notes belongs to a course at Pennsylvania State University taught by a professor in Fall. Since its upload, it has received 21 views.

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Date Created: 02/06/15
EXAMPLES FOR THE GENERAL LINEAR MODEL F Chiaromonte Salarymtw Minitab Student 12 subdirectory Study faculty salary in 1991 as a function of various predictor variables year 1991 StartYr Model 1 linear form in years and beginning salary The regression equation is 1991 10477 1922 years 142 Begin Predictor Coef SE Coef T P Constant 10477 1774 591 0000 years 192215 4987 3855 0000 Begin 142001 008058 1762 0000 S 192227 R Sq 935 R Sqadj 934 Analysis of Variance Source DF SS MS F P Regression 2 8934313979 4467156989 120893 0000 Residual Error 168 620781226 3695126 Total 170 9555095205 F Chiaromonte Scatterplot of RESI1 vs years Begin FITS1 years Begn 10000 5000 0 RBII U1 8 G 8 10000 O 5000 0 in I 5000 39 50000 I 30000 40000 I u I 00 10000 15000 20000 25 5000 000 Firs Reg ress Lowss 999 Probability Plot of RESI1 Normal 95 CI F Chiaromonte Percent 8 Man StDev N AD PValue 146796E12 1911 171 1781 lt0005 I 5000 I I 0 5000 RESI1 I 10000 Model 2 full 2nd order polynomial in years and beginning salary The regression equation is 1991 22975 2940years 183Begin 324yearsA2 0000012Begin22 00150yearsBegin Predictor Coef SE Coef T P Constant 22975 12140 189 0060 years 29396 6961 422 0000 Begin 1834 1015 181 0073 yearsA2 3238 1072 302 0003 Not all second order terms are Begin22 000001208 000002184 055 0581 significant may want to drop yearsBegin 001498 002752 054 0587 some 8 150725 R Sq 961 R Sqadj 960 Analysis of Variance Source DF SS MS F P Regression 5 9180248879 1836049776 80819 0000 Residua1 Error 165 374846326 2271796 Tota1 170 9555095205 F Chiaromonte 4 Scatterplot of RESIZ vs years Begin FITSZ years Begin H s Regrss 10000 LOWE O O 5000 O o 9 o O I o I 0 I o 0 o O U o O O CI 5000 a 5 10 15 20 255000 10000 15000 20000 25000 FITSZ 0 10000 I Probability Plot of RESIZ 0 5000 Normal 95oCI 0 999 39 o 39 Mean 8212046E12 039 StDev 1485 o 9939 N 171 5000 o 95 AD 6653 20000 30000 40000 50000 90 P39Va39ue 039005 so u 70 5 60 E 5039 o 4039 n 30 20 1o 5 1 o o I I I I I I I I 5000 2500 0 2500 5000 7500 10000 12500 RESIZ F Chiaromonte Model 3 2nd order but dropping the square of Begin The regression equation is 1991 16663 2606 years 128 Begin 278 yearsA2 00290 yearsBegin Predictor Coef SE Coef T P Constant l6663 4125 404 0000 years 26064 3475 750 0 000 NOW all terms are highly Begin 12814 01783 719 0000 yearsA2 27813 6825 408 0000 explained variability in yearsBegil 1 002902 001058 274 0007 s 150409 R Sq 961 R Sqadj 96 Analysis of Variance Source DF SS MS P Regression 4 9179554307 2294888577 101441 0000 Residual Error 166 375540898 2262295 Total 170 9555095205 F Chiaromonte Scatterplot of RESI3 vs years Begin FITS3 years Begin Fis Regress 39 10000 Lowss o o 39 5000 o o o o 3 I O a 39 0 39 o 0 0 o 5000 a 5 10 15 20 255000 10000 15000 20000 25000 FITS3 0 10000 Probability Plot of RESI3 5000 5000 I 20000 30000 I 40000 I 50000 F Chiaromonte Percent 999 Normal 95 CI 95 90 a 60 50 40 30 20 10 5 01 Man StDev N AD PValue 610585E12 1486 171 699 lt0005 I I 5000 2500 I 0 I I 2500 5000 RESB I I I 7500 10000 12500 C 0 if female 1 if male Model 4 linear form in years which can differ between males and females The regression equation is 1991 18826 1145 years Predictor Coef Constant 18826 years 114460 C 2330 yearsC 2554 S 312715 R Sq Ana1ysis of Variance Source DF Regression 3 Residua1 Error 167 Tota1 170 F Chiaromonte Scatterplot of 1991 vs years 55000 39 50000 45000 40000 1991s 35000 30000 25000 20000 10 15 20 years 25 2330 C 255 yearsC SE Coef T P 1159 1624 0000 6489 1764 0000 1625 143 0153 8577 030 0766 829 R Sqadj 826 SS MS F 7921992122 2640664041 27003 1633103083 9779060 9555095205 The difference in intercept is mildly significant the one in slope is not P 0000 Scatterplot of RESI4 vs years FITS4 20000 30000 40000 50000 years FITS4 Hm 15000 Regress Lowess 10000 Boxplot of RESI4 o 0 15000 lt m o o E 5000 O r w 0 a 39I392 o 0 0 o oo 0 o O i I o a 5000 0 0 g 0 a o 5000 0 0 0 539 1390 1395 2390 2395 5000 6 Probability Plot of RESI4 C Normal 95 CI 999 Mean 574418E12 StDev 3099 9939 N 171 039 AD 1896 9539 PValue ltooos 9o 80 E 70 60 50 40 n 30 20 1o 5 1 o 01 u F Chiaromonte 10000 5000 0 5000 10000 15000 I RESI4

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