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Two-Way ANOVA quiz

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  • What does two-way ANOVA analyze?
    Two-way ANOVA analyzes the effects of two factors on a dependent variable and tests for interaction effects between those factors.
  • How is a two-way ANOVA different from a one-way ANOVA?
    Two-way ANOVA compares means across two factors, while one-way ANOVA compares means across only one factor.
  • What is an interaction effect in two-way ANOVA?
    An interaction effect occurs when the impact of one factor on the dependent variable depends on the level of the other factor.
  • What is the first step when analyzing a two-way ANOVA problem?
    The first step is to test for interaction effects between the two factors before testing the effects of each factor individually.
  • What are the null and alternative hypotheses for testing interaction effects?
    The null hypothesis states there is no interaction effect, while the alternative hypothesis states there is an interaction effect.
  • How is the F statistic calculated in two-way ANOVA?
    The F statistic is calculated as the ratio of mean squares for the factor or interaction to the mean square error (MSE).
  • What does a p-value greater than the significance level indicate in two-way ANOVA?
    A p-value greater than the significance level means you fail to reject the null hypothesis, indicating no significant effect.
  • What should you do if there is no interaction effect in two-way ANOVA?
    If there is no interaction effect, you can proceed to test the individual effects of each factor on the dependent variable.
  • What happens if a significant interaction effect is found?
    If a significant interaction effect is found, you cannot reliably test the individual effects of each factor because they interfere with each other.
  • How are hypotheses for individual factors set up in two-way ANOVA?
    The null hypothesis states there is no difference in means for the factor, while the alternative hypothesis states there is a difference.
  • What does rejecting the null hypothesis for a factor in two-way ANOVA mean?
    Rejecting the null hypothesis means there is evidence that the factor has a significant effect on the dependent variable.
  • What is an interaction plot used for in two-way ANOVA?
    An interaction plot visually assesses whether there is an interaction effect by comparing the parallelism of lines representing factor levels.
  • How do you interpret parallel lines in an interaction plot?
    Parallel lines suggest there is no interaction effect between the factors.
  • What does it mean if lines in an interaction plot are not parallel?
    Non-parallel lines indicate an interaction effect, meaning the effect of one factor depends on the level of the other.
  • Which axes are used for factors and the dependent variable in an interaction plot?
    The dependent variable is plotted on the y-axis, one factor is on the x-axis, and the other factor is represented by different lines.