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1. ANOVA test in multiple regression model tests the significance of A) The intercept Bo B) All the regression coefficients B's. C) The dependent variable Y. D) Both A and B. 2. If you reject the null hypothesis in the lack of fit (LOF) test, then: A) The linear model is inappropriate for the relation between Y and X. B) The linear model is appropriate for the relation between Y and X. C) The regression coefficient is significant. D) The regression coefficient is insignificant. 3. The basic idea of the generalized least squares method (GLSM) is to: A) Maximize the values of the dependent variable Y. B) Minimize the errors sum of squares. C) Maximize the weighted errors sum of squares. D) Minimize the weighted errors sum of squares.

1. ANOVA test in multiple regression model tests the significance of A) The intercept Bo B) All the regression coefficients B's. C) The dependent variable Y. D) Both A and B. 2. If you reject the null hypothesis in the lack of fit (LOF) test, then: A) The linear model is inappropriate for the relation between Y and X. B) The linear model is appropriate for the relation between Y and X. C) The regression coefficient is significant. D) The regression coefficient is insignificant. 3. The basic idea of the generalized least squares method (GLSM) is to: A) Maximize the values of the dependent variable Y. B) Minimize the errors sum of squares. C) Maximize the weighted errors sum of squares. D) Minimize the weighted errors sum of squares.

A First Course in Probability (10th Edition)
A First Course in Probability (10th Edition)
10th Edition
ISBN:9780134753119
Author:Sheldon Ross
Publisher:Sheldon Ross
Chapter1: Combinatorial Analysis
Section: Chapter Questions
Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...
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1. ANOVA test in multiple regression model tests the significance of A) The intercept Bo. B) All the regression coefficients B's. C) The dependent variable Y. D) Both A and B. 2. If you reject the null hypothesis in the lack of fit (LOF) test, then: A) The linear model is inappropriate for the relation between Y and X. B) The linear model is appropriate for the relation between Y and X. C) The regression coefficient is significant. D) The regression coefficient is insignificant. 3. The basic idea of the generalized least squares method (GLSM) is to: A) Maximize the values of the dependent variable Y. B) Minimize the errors sum of squares. C) Maximize the weighted errors sum of squares. D) Minimize the weighted errors sum of squares. 4. In multiple regression models, there are: A) One independent variable and one dependent variable. B) One independent variable and several dependent variables. C) Several independent variables and one dependent variable. D) Several independent variables and several dependent variables. 5. If R?-0.85 in a multiple regression model, this means that: A) Xi explains 85% of the variation in Y. B) All X's explains 85% of the variation in Y. C) The error explains 15% of the variation in Y. D) Both B and C.
Transcribed Image Text:1. ANOVA test in multiple regression model tests the significance of A) The intercept Bo. B) All the regression coefficients B's. C) The dependent variable Y. D) Both A and B. 2. If you reject the null hypothesis in the lack of fit (LOF) test, then: A) The linear model is inappropriate for the relation between Y and X. B) The linear model is appropriate for the relation between Y and X. C) The regression coefficient is significant. D) The regression coefficient is insignificant. 3. The basic idea of the generalized least squares method (GLSM) is to: A) Maximize the values of the dependent variable Y. B) Minimize the errors sum of squares. C) Maximize the weighted errors sum of squares. D) Minimize the weighted errors sum of squares. 4. In multiple regression models, there are: A) One independent variable and one dependent variable. B) One independent variable and several dependent variables. C) Several independent variables and one dependent variable. D) Several independent variables and several dependent variables. 5. If R?-0.85 in a multiple regression model, this means that: A) Xi explains 85% of the variation in Y. B) All X's explains 85% of the variation in Y. C) The error explains 15% of the variation in Y. D) Both B and C.
First Te 6. To check the linearity or non-linearity of the regression relation we use: A) The residual plot. B) The LOF test. C) The Durbin-Watson test. D) Both A and B. 7. The test used to check heteroscedasticity is called: A) Lack of fit (LOF) test. B) Spearman rank correlation test. C) Durbin-Watson test. D) F- test. 8. Standard residuals are obtained by: A) Multiplying the residuals by the square root of the MSE. B) Adding the residuals to the MSE. C) Dividing the residuals by the square root of the MSE. D) Dividing the residuals by the MSE.
Transcribed Image Text:First Te 6. To check the linearity or non-linearity of the regression relation we use: A) The residual plot. B) The LOF test. C) The Durbin-Watson test. D) Both A and B. 7. The test used to check heteroscedasticity is called: A) Lack of fit (LOF) test. B) Spearman rank correlation test. C) Durbin-Watson test. D) F- test. 8. Standard residuals are obtained by: A) Multiplying the residuals by the square root of the MSE. B) Adding the residuals to the MSE. C) Dividing the residuals by the square root of the MSE. D) Dividing the residuals by the MSE.
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