Quiz on Interpret Linear Regression

Quiz 7

📈 Interpret Linear Regression Results Like a Pro! 📈

This quiz will test your skills on:

- Interpreting model coefficients
- Understanding summary statistics
- Assessing model fit
- Checking assumptions
- Making valid predictions

See if you can:

- Explain regression coefficients and p-values
- Interpret R-squared, residuals, and F-statistics
- Check model assumptions from regression diagnostics
- Make reasonable predictions from models
- Draw valid inferences from results

Mastering interpretation of outputs will take your applied regression skills to the next level!

1 / 10

If the p-value for a regression coefficient is 0.02, what can you conclude?

There is a 2% probability the coefficient is non-zero.

The predictor is not significantly related to the outcome.

The coefficient cannot be interpreted.

The predictor is significantly related to the outcome.

2 / 10

If the residuals vs fitted values plot shows a random scatter, what does it indicate?

Multicollinearity

The errors have constant variance

The errors are normally distributed.

A nonlinear relationship.

3 / 10

What is the interpretation of an R-squared value of 0.85 for a model?

The predictions explain 85% of the outcome variability.

85% of the features are useful for predictions

The model fits the data 85% of the time.

The coefficient of determination shows a good fit.

4 / 10

What does a small p-value on the F-statistic indicate?

A significant portion of variance is explained.

Multicollinearity between predictors.

Non-normal errors.

A poor model fit.

5 / 10

How would you assess the predictive accuracy of a model on new data?

R-squared

Residual standard error

Validation on a test set

F-statistic

6 / 10

If the confidence interval for a coefficient does not contain 0, how can you interpret it?

The effect is statistically significant.

It has a spurious correlation.

It violates model assumptions.

The coefficient cannot be interpreted.

7 / 10

What are good model diagnostic plots for linear regression assumptions?

Histogram of residuals, scatterplot

Cook's distance plot, leverage plot

QQ plot, scale-location plot

Residual time series plot, scatterplot matrix

8 / 10

What does a Cook's distance plot help identify?

Nonlinear relationships

Multicollinearity

Outliers and influential points

Homoscedasticity

9 / 10

What is the main assumption needed for statistical inference in regression?

No multicollinearity

Normally distributed errors

Homoscedasticity

Independence of observations

10 / 10

How would you determine if a regression model is overfitting the data?

High R-squared, low p-values

Train/test split, cross-validation

Residual analysis

Low R-squared

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Quiz on Interpret Linear Regression

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