Question: Model 1: Multicollinearity Model 2: Multicollinearity 7 Correlations Model Summary carbmax Far nicotine weight Adjusted R Sod. Error off Pearson Comelation carbmox 1080 457 926

Far nicotine weight Adjusted R Sod. Error off Pearson Comelation carbmox 1080

Model 1: Multicollinearity Model 2: Multicollinearity 7 Correlations Model Summary carbmax Far nicotine weight Adjusted R Sod. Error off Pearson Comelation carbmox 1080 457 926 464 Model R Square square the Evemate 957 1 000 .977 .491 .957 1.3967 1.000 .400 . Predictors: IConstant, tar weight 404 491 1,00 carbmax 0OO .010 car .000 .ODE ANOVA" nicofirst Sum of weight 1010 Turnby Mean Square Regression 494.281 494 281 253.170 Residual 44.863 23 1 951 Model Summary Towl 459.150 Adjusted & Sod. Eror of a. Dependent Variable: carbmox R Square Squaint b. Predictors (Command, Er 919 1.4457 L. Predictors: Koastand, weight, sir, nicotine Coefficients ANOVA" Umbandardiced Sundardiane Coefficients Collinearicy Semantics Sum of Model Mean Square Model Beta Tolerance VIE Repression 495.254 145.084 78984 Conwant 2.743 675 4.063 Residual 43.689 21 2.080 .050 15.918 1.000 1.000 Tou 534.150 a. Dependent Variable: carbmow in. Dependent Variable: carbmox b. Predictors: (Contant. weight tar, nicotine Coefficients I'mstandardized Kandardined Cellinearity Service Model Tolerance VIF iConstant 3.202 925 .145 tar 1.151 1974 046 2143 1.101 046 21.10 .130 3.485 034 1.3:34 L. Dependent Variable: carbmon

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1. Provide new information that contains more than 59 items, depending on the recipients capacity.

3. Give away need to know information and you will get back information that you find useful.

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