The purpose of the adjusted multiple coefficient of determination in multiple li
ID: 3224534 • Letter: T
Question
The purpose of the adjusted multiple coefficient of determination in multiple linear regression is to a. adjust predictions b. adjust R^2 for impact of additional error in estimation c. adjust slope of R^2 d. adjust R^2 for impact of additional independent variables The purpose of the t test in multiple regression is to test for a. independence of variables b. statistical significance of independent variables c. multicollinearity d. statistical significance of the overall model Suppose an estimated regression equation is given by y = 210+ 3x_1 - 15x_2 where y is measured in thousands of dollars, x_1 is measured in thousands of dollars, and x_2 is measured in years. a. If x_1 is held constant and x_2 is increased by 2 years, what will be in y? Indicate units in your answer. b. If x_2 is held constant and x_1 is increased by $500, what will be the exact change in y? Indicate units in your answer. c. To the nearest hundredth, what exactly is y when x_1 = $2,000 and x_2 = 10 years? Indicate units in your answer. The primary purpose of residual analysis is to assess a. stability of error terms b. statistical significance of independent variables c. goodness of model fit d. statistical significance of the overall model In multiple regression involving 3 independent variables, the basic relationship involving SSR, SSE, and SST is a. 3SSR + 3SSE = SST b. undefined c. SSR + SSE = SST/3 d. SSR + SSE = SSTExplanation / Answer
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As per Chegg Guidelines,only the first question is answered. Please post the questions separately to get the answers of the rest.
21, option D is the answer.
Since,the adjusted R2 will penalize you for adding independent variables (K in the equation) that do not fit the model. In regression analysis, it can be tempting to add more variables to the data as you think of them. Some of those variables will be significant, but you can’t be sure that significance is just by chance. The adjusted R2 will compensate for this by that penalizing you for those extra variables.
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