The metric used to determine the significance of the overall model fit is "Both B and C", which refers to R-squared and F-statistic.

Here's why:

1. R-squared: This metric, also known as the coefficient of determination, measures the proportion of the variance in the dependent variable that is predictable from the independent variable(s). It provides a measure of how well observed outcomes are replicated by the model, based on the proportion of total variation of outcomes explained by the model.

2. F-statistic: This is a good indicator of whether there is a relationship between our predictor and the response variable. The F-statistic is used for comparing our model with a baseline model to see if our addition of predictors improves the model. If the overall F-test is significant, we can conclude that R-squared does not equal zero, and the regression model predicts the response variable better than the mean of the response.

So, both R-squared and F-statistic are used to determine the significance of the overall model fit.

Question

The metric used to determine the significance of the overall model fit is "Both B and C", which refers to R-squared and F-statistic.

Here's why:

1. R-squared: This metric, also known as the coefficient of determination, measures the proportion of the variance in the dependent variable that is predictable from the independent variable(s). It provides a measure of how well observed outcomes are replicated by the model, based on the proportion of total variation of outcomes explained by the model.

2. F-statistic: This is a good indicator of whether there is a relationship between our predictor and the response variable. The F-statistic is used for comparing our model with a baseline model to see if our addition of predictors improves the model. If the overall F-test is significant, we can conclude that R-squared does not equal zero, and the regression model predicts the response variable better than the mean of the response.

So, both R-squared and F-statistic are used to determine the significance of the overall model fit.

Knowee AI · Accepted Answer

The metric used to determine the significance of the overall model fit is "Both B and C", which refers to R-squared and F-statistic.

Here's why:

1. R-squared: This metric, also known as the coefficient of determination, measures the proportion of the variance in the dependent variable that is predictable from the independent variable(s). It provides a measure of how well observed outcomes are replicated by the model, based on the proportion of total variation of outcomes explained by the model.

2. F-statistic: This is a good indicator of whether there is a relationship between our predictor and the response variable. The F-statistic is used for comparing our model with a baseline model to see if our addition of predictors improves the model. If the overall F-test is significant, we can conclude that R-squared does not equal zero, and the regression model predicts the response variable better than the mean of the response.

So, both R-squared and F-statistic are used to determine the significance of the overall model fit.

Which metric is used to determine the significance of the overall model fit?T-statisticR-squaredF-statisticBoth B and C

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Which metric is used to determine the significance of the overall model fit?

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