Model: MODEL1Model: MODEL2Model: MODEL3LITER and LOGGNP as predictors of Life expectancy of womenThe REG ProcedureModel: MODEL1Dependent Variable: lifewom/* mreg-country-23jan05.sas directory: C:\baileraj\Classes\Spring '05\programs\regression-examples purpose: multiple regression with different predictor variables*/* libname class "\\Muserver2\USERS\B\BAILERAJ\public.www\classes\sta402\data";options ls=75 nodate formdlim="-";data country; title ‘country data analysis’; infile "\\Casnov5\MST\MSTLab\Baileraj\country.data"; input name $ area popnsize pcturban lang $ liter lifemen lifewom pcGNP; logarea = log10(area); logpopn = log10(popnsize); loggnp = log10(pcGNP); ienglish = (lang="English"); drop area popnsize pcgnp;proc print;run;/* to generate a scatterplot matrix Solutions > Analysis > Interactive Data Analysis - open data set WORK > COUNTRY - select columns (CTRL and click column labels) - Analyze > Scatter Plot (YX) to generate regression fit via this interactive data analysis Analyze > Fit */1LITER and LOGGNP as predictors of Life expectancy of womenThe REG ProcedureModel: MODEL1Dependent Variable: lifewomlifewom4581lifemen2975liter2099pcturba n593logarea-1.00003.9370logpopn-1.00003.0531loggnp2.07924.1601proc reg data=country;title predicting life expectancy of women in different countries; model lifewom = loggnp; output out=new1 p=yhat r=resid; run;proc plot data=new1 hpercent=50 vpercent=75;title residual plots for LIFEWOM = LOGGNP model; plot resid*(yhat liter); run;proc reg data=country;2LITER and LOGGNP as predictors of Life expectancy of womenThe REG ProcedureModel: MODEL1Dependent Variable: lifewomtitle LITER and LOGGNP as predictors of Life expectancy of women; model lifewom = liter; model lifewom = loggnp; model lifewom = liter loggnp;run;proc reg; title LIFEWOM predicted from PCTURBAN LITER LOGAREA LOGPOPN LOGGNP; model lifewom = pcturban liter logarea logpopn loggnp; plot r.*p. nqq.*r.; run;proc reg data=country; title LITER and LOGGNP as predictors of Life expectancy of women; model lifewom = liter loggnp/ tol vif collinoint; output out=new p=yhat r=resid;run;proc univariate data=new plot; id name; var resid; run;proc plot hpercent=50 vpercent=50; plot resid*yhat=ienglish resid*liter=ienglish resid*loggnp=ienglish;run;3LITER and LOGGNP as predictors of Life expectancy of womenThe REG ProcedureModel: MODEL1Dependent Variable: lifewom residual plots for LIFEWOM = LOGGNP model 5 Plot of resid*yhat. A=1, B=2, etc. Plot of resid*liter. A=1, B=2, etc. 15 ˆ A A 15 ˆ A A ‚ ‚ ‚ ‚ ‚ ‚ ‚ A ‚ A 10 ˆ A 10 ˆ A ‚ ‚ ‚ B ‚ A A ‚ A A ‚ A A ‚ AA B ‚ A AA A 5 ˆ BA AA 5 ˆ A AA A AR ‚ A A B R ‚ A A A Ae ‚ A A A A e ‚ B A As ‚ B A A A s ‚ A A A Bi ‚ A A A AAA A i ‚ CB Ad 0 ˆ AA A A A d 0 ˆ A A B Au ‚ A B A A u ‚ A AA Ba ‚ A A A a ‚ A A Al ‚ A B AA A A A l ‚ AA A AA A A A ‚ A B A ‚ AA A A -5 ˆ A A A A A -5 ˆ B A A A ‚ A ‚ A ‚ A ‚ A ‚ A A AA ‚ A A B ‚ A A ‚ A A -10 ˆ -10 ˆ ‚ A B ‚ A B ‚ ‚ ‚ ‚ ‚ ‚ -15 ˆ -15 ˆ Šƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒ Šˆƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒˆ 50 60 70 80 90 0 50 100 Predicted Value of lifewom literNOTE: 1 obs had missing values. NOTE: 2 obs had missing values LITER and LOGGNP as predictors of Life expectancy of women 6 Model: MODEL1 Dependent Variable: lifewom Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model 1 4700.51263 4700.51263 149.13 <.0001 Error 75 2364.00685 31.52009 Corrected Total 76 7064.51948 Root MSE 5.61428 R-Square 0.6654 Dependent Mean 64.68831 Adj R-Sq 0.6609 Coeff Var 8.67896