Test your knowledge of the material on loglinear models in the following quiz.

**What is a loglinear model for frequency tables in categorical data analysis?**

- A linear regression model for categorical data
- A logistic regression model for categorical data
- A statistical model for analyzing the relationship between categorical variables
- A model for predicting binary outcomes from categorical predictors

**How do you test for goodness of fit of a loglinear model for a two-way frequency table?**

- By using a chi-squared test
- By using a likelihood ratio test
- By using a Cochran-Mantel-Haenszel test
- By using Cohenâ€™s kappa.

**What is the main difference between a**`glm()`

loglinear model and the chi-squared test for independence in two-way frequency tables?

`glm()`

models allow for different types of associations among variables while chi-squared test only tests for independence`glm()`

models allow for non-normal data while chi-squared test do not`glm()`

models apply in small sample size while chi-squared test require large sample size`glm()`

models allow for estimation of parameters for interactions among variables and also for non-normal data while chi-squared test only tests for independence

**How does a loglinear model differ from a logit model?**

- Loglinear models are linear while logit models are non-linear
- Logit models handle only binary outcomes while loglinear models handle more than 2 outcomes
- Loglinear models are used for continuous data while logit models are used for categorical data
- Logit models estimate probabilities while loglinear models estimate frequencies

**What is the difference between a loglinear model fit by**`loglm()`

and one fit using`glm()`

?

`loglm()`

requires large samples while`glm()`

can be used for smaller samples`loglm()`

only estimates fitted values while`glm()`

estimates parameters`loglm()`

uses an interative process while`glm()`

provides an exact solution`loglm()`

gives the Pearson chi-square test while`glm()`

gives the likelihood ratio test

**What are the advantages of a loglinear model fit using**`glm()`

over one fit using`loglm()`

`glm()`

estimates parameters while`loglm()`

estimates only fitted values`glm()`

can handle ordinal and quantitative predictors while`loglm()`

cannot`glm()`

can accommodate over-dispersion while`loglm()`

cannot- All of the above

**How can you extend a loglinear model for frequency tables to handle multi-way contingency tables?**

- By adding association terms to the model
- By fitting separate loglinear models for each combination of predictors
- By using a multivariate linear regression model
- By using a multinomial logistic regression model

**For a three-way frequency table what is the interpretation of the model symbolized by**`[A B] [A C]`

- A and B are conditionally independent given C
- B and C are conditionally independent given A
- A and B are mutually independent
- The association between A and B is the same for all levels of C