Enter T for true or F for false.In multiple regression as in simple linear regression we use ANOVA to test individual partial regression coefficients for their significance in explaining the data. _____ Multicollinearity is a condition where two or more of the independent variables in the data are correlated. One of the ways this can show up in data analysis is in stepwise regression where addition of another explanatory variable causes partial regression coefficients of variables already in the regression to change substantially. _____ The prediction interval in multiple regression is a confidence interval around a specific value of y. _____ The confidence interval in multiple regression is centered on the regression-predicted value of y. _____ If a histogram of residual values is symmetric and bell shaped, this is evidence that the regression is reasonable. _____

Note: Hi there! Thank you for posting the question. As you have posted multiple questions, as per the policy, we have solved only the first question for you. If you need any specific question to be answered, please re-submit the question separately or by specifying the question number or name.Given information: The information about regression model is given.Explanation: Multiple linear regression model: A multiple linear regression model is given as y = b0 + b1x1 + b2x2 + …+ bkxk where y is the predicted value of response variable, and x1, x2,…, xk are the k predictor variables. The quantities b1, b2,…, bk are the estimated slopes corresponding to x1, x2,…, xk respectively and b0 is the estimated intercept of the line, from the sample data. A multiple regression equation describes the combined effect of all the predictors in the model. Even when the effect of a particular predictor is being studied from a multiple regression equation, it assumes that the effects of all the other predictors are accounted for. Simple linear regression model: A simple linear regression model is given as y = b0 + bx, where y is the predicted value of response variable, and x be the predictor variable. The quantity b is the estimated slope corresponding to x and b0 is the estimated intercept of the line, from the sample data. A simple regression equation describes the effect of predictor variable on the response variable in the model. In any regression model, ANOVA table is used to test for the overall significance of the model. It does not indicate the significance of partial regression coefficients. The given statement is: In multiple regression as in simple linear regression we use ANOVA to test individual partial regression coefficients for their significance in explaining the data. Thus, the given statement is False (F).