STAT 512 Midterm Exam March 1 2007 Name: Show all your work (you can use this page). All questions are for 5 points (90 total). Good luck ! 1. You use 27 observations to estimate parameters of the simple linear regression model. The averages of your X and Y values are equal to: X =2 and Y =10. The estimate of the intercept, b0 , is equal to 4, and its estimated standard deviation, s(b0), is equal to 1. The estimate of the standard deviation of the error term is equal to 3. a) Construct 95% confidence interval for β0. b0 t (0.975,25) s(b0 ) 4 2.06 [1.94,6.06 b) Calculate b1. (Hint – see the formula for b0). b1 Y b0 10 4 3 X 2 c) Calculate SSX ( X i X ) 2 (Hint – see the formula for s2(b0)). 4 1 s 2 (b0 ) 9 27 SSX SSX 54 d) Find s(b1). 9 1 54 6 s (b1 ) 0.41 s 2 (b1 ) e) Construct confidence intervals for β0 and β1 so as to have at least 95% confidence that both these parameters are included in the respective intervals. 0.05 t 1 ,25 t (0.9875,25) 2.393 4 4 2.393 [1.607,6.393] 3 0.41 2.393 [2.02,3.98] 2. You use 23 observations to build a multiple regression model Yi 0 1 X i1 2 X i 2 i , where ξi ~ N(0,σ2). Your estimates are equal to: b0=3, b1=4, b2=3, MSE=5. a) Estimate the mean value of Y for X1=2 and X2=1 (give a point estimate). ˆ 14 b) The estimated standard deviation of the estimate of mean you obtained in point (a), s( ˆ ) , is equal to 2. Find the corresponding standard error of the prediction of Y . s ( pred ) 5 4 3 c) Find the prediction interval for Y at X1=2 and X2=1. t(0.975,20)=2.086 PI : 14±2.086∙3=[7.742, 20.258] 3. Here is the table of type I and type II sums of squares for three explanatory variables used for the multiple regression model. variable X1 X2 X3 Type I SS 200 50 20 Type II SS 60 20 20 n= 24 and SST = 570 . a) Give the estimate of the variance of the error term. SSM=270, SSE=570-270=300, MSE=300/20=15 b) Test the hypothesis that the response variable is not associated with any of the explanatory variables. F=MSM/MSE, MSM=270/3=90, F=90/15=6 F(0.95,3,20)=3.1 6>3.1 – reject H0 Conclusion – at least one of my X’s is a useful predictor c) Give the value of R2 for the full model. R2=270/570≈0.474 d) Test the hypothesis that β1=0 in the full model. F=60/15=4, F(0.95,1,20)=4.35 Do not reject H0. X1 is not a useful predictor in the full model. e) Test the hypothesis that β1=0 in the simple linear regression of Y on X1. SSM=MSM=200, SSE=370, MSE=370/22, F=MSM/MSE≈11.89 F(0.95,1,22)<4.35 Reject H0 X1 is significantly correlated with Y. 4. We study the relation between degree of brand liking (Y) and moisture component (X1) and sweetness (X2) of the product. Below you can find the results of SAS analysis. Analysis of Variance Source DF Sum of Squares Mean Square Model Error Corrected Total 2 13 15 1872.70000 94.30000 1967.00000 936.35000 7.25385 Root MSE Dependent Mean Coeff Var 2.69330 81.75000 3.29455 R-Square Adj R-Sq F Value Pr > F 129.08 <.0001 0.9521 0.9447 Parameter Estimates Variable Intercept x1 x2 DF Parameter Estimate Standard Error t Value Pr > |t| 1 1 1 37.65000 4.42500 4.37500 2.99610 0.30112 0.67332 12.57 14.70 6.50 <.0001 <.0001 <.0001 a) Give the result of the overall ANOVA test verifying if the response variable is related to any of the explanatory variables (give the value of the corresponding test statistic, the p-value and the conclusion). F=129.08, p-value<0.0001, Reject H0, at least one of our X’s is a useful predictor b) Write the fitted regression equation. Yˆ 37.65 4.425x1 4.375x2 c) Give the estimate of the standard deviation of the error term. 2.6933 d) Give the result of the test for significance of X2 in the full model (give the value of the corresponding test statistic, the p-value and the conclusion). t=6.5, pvalue<0.0001, reject H0, X2 is a significant predictor in a full model 5) What can you say about the distribution of the data demonstrated on the attached qqplot ? The distribution has (much) heavier (longer) tails than the normal distribution. 500 400 300 200 x 100 0 -100 -200 -300 -4 -3 -2 -1 0 Normal Quantiles 1 2 3 4