1. In simple linear regression Yi = A) +AXi +6, the t-test can be used to test H
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1. In simple linear regression Yi = A) +AXi +6, the t-test can be used to test Ho : > 0 = 0 versus one-sided alternative H1 : True False 2. In simple linear regression, confidence interval for the mean value of the response variable given a specific value of the predictor is usually wider than the prediction interval for the individual response given the same value of the predictor True False 3. In multiple linear regression Y; A+B1Xil +AXi2 +6, the t-test and partial F-test for Ho : ,-0 vs H1 : 10 are equivalent (i.e., yielding the same p-values) True False 4. In simple linear regression, the line of best fit based on least square principle marimizes the distance between the observed response variable values and the regression line True False 5. In multiple linear regression Yi-Au + Xil + 2Xi2+ Xi3 + Ei, we usually assume that the random error , follows standard normal distribution N(0, 1) in order to perform inference like t or F tests True False 6. In multiple linear regression Y A) + Xil + AXi2 +Xi3 + Ei, if we perform an overall F-test and get p-value 0.015, we will conclude that three predictors X1, X2 and all have significant impact on response variable y under significance level = 0.05 True False 7. If A is a 4 × 5 matrix (ie, matrix with 4 rows and 5 columns), and B is a 4 × 3 matrix, then (B'A)'is a 5 x 3 matrix. True False 8·To test the association between a predictor, which is categorical with 2 levels, and a potential confounder, which is categorical with 3 levels, we should use two-sample t test True False 9. In multiple linear regression model, adjusted R2 increases whenever new predictors are added to the model. True False In simple linear regression Y,-A)+3.Xi+Ei, ( smaller, the prediction for Y given X = zo gets closer to the sample mean of Y 10. 1, 2, , n) as the value of R2 becomes True FalseExplanation / Answer
1) true
2) false (prediction interval is wider)
3) false (this is true when there is only one independnet variable)
4) false (it minimizes the distance)
5) false (even though we assume error follow normal distributionwith mean , but variance need not be 1)
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