The contribution of an independent variable to a multiple regression model is measured by (b) SSR( Xj | all variables excluding j) and (c) SSR(all variables including j) – SSR(all variables except j). So, the correct answer is (d) Both (b) and (c).

Here's why:

(a) SSR( Xj | all variables including j) is not correct because it measures the sum of squared residuals when Xj is included with all other variables. It doesn't isolate the contribution of Xj.

(b) SSR( Xj | all variables excluding j) is correct because it measures the sum of squared residuals when Xj is considered alone, without the influence of other variables. This gives us an idea of how much Xj contributes to the model.

(c) SSR(all variables including j) – SSR(all variables except j) is also correct because it measures the reduction in the sum of squared residuals when Xj is added to the model. This gives us another way to quantify the contribution of Xj.

So, the contribution of an independent variable to a multiple regression model is best measured by both (b) and (c).

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The contribution of an independent variable to a multiple regression model is measured by (b) SSR( Xj | all variables excluding j) and (c) SSR(all variables including j) – SSR(all variables except j). So, the correct answer is (d) Both (b) and (c).

Here's why:

(a) SSR( Xj | all variables including j) is not correct because it measures the sum of squared residuals when Xj is included with all other variables. It doesn't isolate the contribution of Xj.

(b) SSR( Xj | all variables excluding j) is correct because it measures the sum of squared residuals when Xj is considered alone, without the influence of other variables. This gives us an idea of how much Xj contributes to the model.

(c) SSR(all variables including j) – SSR(all variables except j) is also correct because it measures the reduction in the sum of squared residuals when Xj is added to the model. This gives us another way to quantify the contribution of Xj.

So, the contribution of an independent variable to a multiple regression model is best measured by both (b) and (c).

Knowee AI · Accepted Answer

The contribution of an independent variable to a multiple regression model is measured by (b) SSR( Xj | all variables excluding j) and (c) SSR(all variables including j) – SSR(all variables except j). So, the correct answer is (d) Both (b) and (c).

Here's why:

(a) SSR( Xj | all variables including j) is not correct because it measures the sum of squared residuals when Xj is included with all other variables. It doesn't isolate the contribution of Xj.

(b) SSR( Xj | all variables excluding j) is correct because it measures the sum of squared residuals when Xj is considered alone, without the influence of other variables. This gives us an idea of how much Xj contributes to the model.

(c) SSR(all variables including j) – SSR(all variables except j) is also correct because it measures the reduction in the sum of squared residuals when Xj is added to the model. This gives us another way to quantify the contribution of Xj.

So, the contribution of an independent variable to a multiple regression model is best measured by both (b) and (c).

The contribution of an independent variable to a multiple regression model is measured by (a) SSR( Xj | all variables including j) (b) SSR( Xj | all variables excluding j) (c) SSR(all variables including j) – SSR(all variables except j) (d) Both (b) and (c)

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