## Understanding Generalized Linear Models – Coursera statistics

Qn1. 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
``````

Qn2. Generalized linear models (GLMs) handled only between-subjects factors.

``````True
False
``````

Qn3. 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
``````

Qn4. Which of the following is not an example of a generalized linear model (GLM)?

``````Poisson regression
Binomial regression
Gamm regression
Ordinal logistic regression
All are GLMs
``````

Qn5. 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
``````

Qn6. Nominal logistic regression is also known as multinomial regression

``````True
False
``````

Qn7. Multinomial regression with the cumulative logit link function is also know as:

``````Nominal logistic regression
Ordinal logistic regression
Poisson regression
Binomial regression
None of the above
``````

Qn8. Poisson regression is often appropriate for analyzing which kind of data?

``````Error rates
Success percentages
Logarithmic distributions
Rare event counts
None of the above
``````

Qn9. Exponential regression is a special case of which generalized linear model (GLM)

``````Poisson regression
Binomial regression
Ordinal logistic regression
Gamm regression
None of the above
``````

Qn10. The generalized linear model (GLM) can be used in place of the linear mode (LM) for between-subjects designs.

``````True
False

``````