uestion2) Following table presents the results of multiple linear regression mod

Question

uestion2) Following table presents the results of multiple linear regression model using both "beer" and "weight" as regressors. (Minitab->Stat-Regression->Regression) Regression Analysis BAC versus beers, weight Analysis of Variance Source DF Adj S5 Adj MS F-Value P-Value Regresion 2 0.027816 0.01390 128.33 1 0.027114 0.027114 250.19 40.98 beers weight 0.000 0.000 0.000 Error Total 1 0.004441 0.004441 13 0.001409? 15 0.029225 Model Summary R-sq R-sq (adj) PRESS R- (pred) 0.0023358 94.44 92, 01 Coefiicients T-Value P-Value VIE Term Constant beers weight Coef SE Coef 0.0399 0.0624) 0.02270) 3.82 15.82 6.40 0.002 0.000 1.0 0.000 1.0 0.0104.0173, 0.01998 0.00126 0.01725, 0.000363 0.000057 (-0.000485, -0.000240) Regression Equation BAC = 0.0399 + 0.01998 beers - 0.000363 we ight a) Find the missing values in this table b) What are your conclusions? c) Compare this model to the simple linear regression model that only had "beers" as the regressor. You can do this by comparing R2 and Root MSE (S in Minitab) values of these models

Explanation / Answer

MSE = SS_error /df error = 0.001409/13 = 0.00010838

S = sqrt(MSE) =sqrt(SS /df error) = sqrt(0.001409/13)= 0.01041079

R^2 = SSR/SST = 0.027816/0.029225 = 0.9517878

b)

p-value of every variable is 0.000 < 0.05

hence the model is significant

c) As data is not given , you have to do it yourself , take only beer as indeoendent variable

take the value of R^2 and MSE of this model and compare

normally R^2 will lower in this case and S should be higher

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