10 Question Quiz on Logistic Regression in R

Quiz 9

Here is a 10 question quiz on logistic regression in R:

Master Logistic Regression Analysis in R!

This quiz will test your skills on:

- Fitting binary logistic models
- Interpreting coefficients
- Validation and diagnostics
- Probability and odds
- Making predictions

See if you can:

- Fit a logistic model and interpret the coefficients
- Calculate odds ratios from logistic coefficients
- Use statistical tests like z-values and Wald chi-square
- Validate model fit and assumptions
- Make probability predictions

Applying these skills will take your applied logistic regression to the next level!

1 / 10

. What is the main R function for fitting binary logistic regression models?

glm() with family=binomial

logreg()

logistic_model()

lm()

2 / 10

How would you interpret an odds ratio of 2.5 for a predictor?

A one unit increase is associated with 2.5 higher odds

A 2.5 unit increase doubles the odds.

The predictor increases odds by 150%.

The predictor increases the odds by 250%

3 / 10

What R function calculates predicted probabilities from a logistic model?

predict()

fitted()

coefficients()

estimate()

4 / 10

What logistic regression assumption involves the predictors being linearly related to the log odds?

Multicollinearity

Linear logits

Independence

Normality

5 / 10

How can you assess the predictive accuracy of a logistic model?

R-squared

Confusion matrix

Residual plots

z-values

6 / 10

What is the interpretation of a significant positive coefficient in logistic regression?

Higher predictor increases odds of outcome

Higher predictor decreases odds of outcome

Positive correlation between predictor and outcome

Predictor has no effect on the outcome

7 / 10

Which R function calculates Wald chi-square values for logistic coefficients?

pvals()

wald.test()

odds_test()

chi_squared()

8 / 10

How would you validate the proportional odds assumption?

Test for interactions with the predictor

Check residual plots

Compare logit coefficients across models

Assess changes in AUC

9 / 10

What is an advantage of logistic over linear regression?

Better fit for non-linear effects

Handles categorical outcomes

Requirement of normality is relaxed

Easier coefficient interpretation

10 / 10

What plot helps identify influential observations in logistic regression?

QQ plot

Cook's distance plot

Residual histogram

Predicted probability plot

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10 Question Quiz on Logistic Regression in R

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