Understanding Generalized Linear Models :Designing, Running, and Analyzing Experiments(Interaction Design Specialization) Answers 2026

Question 1

What do generalized linear models (GLMs) generalize?

✅ The linear model, which encompasses the ANOVA
❌ The linear model, which is a subset of the ANOVA
❌ The general model, which supersedes the ANOVA
❌ The general model, which is a subset of the ANOVA
❌ None of the above

Explanation:
GLMs extend the linear model (LM), which already includes ANOVA as a special case. GLMs allow non-normal response distributions and link functions.

Question 2

Generalized linear models (GLMs) handle only between-subjects factors.

❌ False
✅ True

Explanation:
GLMs can handle between-subjects, within-subjects, and mixed designs (with appropriate extensions).

Question 3

Poisson regression is an example of a generalized linear model (GLM) with a Poisson distribution for the response and a log link function.

✅ True
❌ False

Explanation:
Poisson regression is a classic GLM using a Poisson distribution and a log link.

Question 4

Which of the following is not an example of a generalized linear model (GLM)?

❌ Poisson regression
❌ Binomial regression
❌ Gamma regression
❌ Ordinal logistic regression
✅ All are GLMs.

Explanation:
All listed models fall under the GLM framework.

Question 5

The link function in a generalized linear model (GLM) most precisely relates what to what?

❌ Factors to each of the responses
✅ Factors to the mean of the response
❌ Factors to the distribution of the response
❌ Factors to the error in the response
❌ None of the above

Explanation:
The link function connects the linear predictor (factors) to the mean of the response variable.

Question 6

Nominal logistic regression can also be known as multinomial regression.

✅ True
❌ False

Explanation:
Nominal logistic regression is another name for multinomial logistic regression.

Question 7

Multinomial regression with the cumulative logit link function is also known as:

❌ Nominal logistic regression
✅ Ordinal logistic regression
❌ Poisson regression
❌ Binomial regression
❌ None of the above

Explanation:
Using a cumulative logit link implies ordered categories, which defines ordinal logistic regression.

Question 8

Poisson regression is often appropriate for analyzing which kind of data?

❌ Error rates
❌ Success percentages
❌ Logarithmic distributions
✅ Count data
❌ None of the above

Explanation:
Poisson regression is designed for count data (e.g., number of events).

Question 9

Exponential regression is a special case of which generalized linear model (GLM)?

❌ Poisson regression
❌ Binomial regression
❌ Ordinal logistic regression
✅ Gamma regression
❌ None of the above

Explanation:
An exponential distribution is a special case of the Gamma distribution, so exponential regression falls under Gamma GLMs.

Question 10

The generalized linear model (GLM) can be used in place of the linear model (LM) for between-subjects designs.

✅ True
❌ False

Explanation:
GLMs generalize LMs, so they can always be used where LMs apply, including between-subjects designs.

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