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Metrics for Assessing Linear Regression

Authored by Emily Anne

Computers

University

Metrics for Assessing Linear Regression
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10 questions

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1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What does R^2 measure in a regression model?

The average size of prediction errors.

The proportion of variance in the dependent variable explained by the independent variables.

The total error in the model.

The sensitivity of the model to changes in the training data.

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which metric penalizes the addition of irrelevant predictors in a regression model?

R^2

Adjusted R^2

Mean Absolute Error (MAE)

Mean Squared Error (MSE)

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How is Mean Absolute Error (MAE) calculated?

By squaring the residuals, averaging them, and taking the square root.

By dividing the sum of absolute value of the residuals by the number of data points.

By dividing the total error by the number of predictors.

By dividing the total error by the mean of observed values.

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What does a high standard deviation of residuals indicate?

The model is underfitting the data.

The model's predictions are consistently close to the observed values.

The model’s predictions have large errors on average.

The independent variables are uncorrelated.

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

When is the Adjusted R^2 value likely to decrease?

When a useful predictor is added to the model.

When an irrelevant predictor is added to the model.

When the total sum of squares (SST) decreases.

When the number of observations increases.

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which metric is expressed as a percentage and helps compare models across datasets with different scales?

MSE

MAPE

RMSE

R^2

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Why could RMSE be preferred over MSE?

Because RMSE is easier to calculate by hand.

Because RMSE penalizes large errors less than MSE.

Because RMSE is in the same units as the dependent variable, making it more interpretable.

Because RMSE gives a lower error value than MSE.

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