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Questions and Answers
T-statistic tests can be used to compare three or more population means.
False
ANOVA assumes that the errors are normally distributed and have constant variance.
True
Single-factor analysis of variance is a type of two-factor analysis of variance.
False
ANOVA is used to test the difference between two population means only.
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Single-factor analysis of variance with regression is a type of two-way ANOVA.
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Study Notes
Comparing Means of Populations
- T-statistic tests are limited to comparing the means of two populations (data sets)
- Analysis of Variance (ANOVA) allows for the comparison of two or more population means (data sets)
ANOVA Assumptions
- Errors are normally distributed
- Errors are independent
- Errors have constant variance (σ2)
Types of Single Factor Tests
- Single-factor analysis of variance (one-way)
- Single-factor analysis of variance with regression (effects model)
- Single-factor analysis of variance with regression (means model)
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Description
Test your knowledge on the differences between t-statistic tests and analysis of variance (ANOVA) in comparing two or more population means. Learn about the assumptions of ANOVA and different types of single factor tests.
