Podcast
Questions and Answers
T-statistic tests can be used to compare three or more population means.
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.
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.
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.
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.
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.
