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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.
  • 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 null hypothesis when testing for interaction effects in two-way ANOVA?
    The null hypothesis states that there is no interaction between the two factors.
  • What statistical values are used to determine significance in two-way ANOVA?
    F-statistics and p-values are used to determine significance at a chosen alpha level.
  • What does it mean if the p-value for interaction is greater than the alpha level?
    If the p-value is greater than alpha, you fail to reject the null hypothesis, indicating no evidence of interaction.
  • What is the dependent variable in a two-way ANOVA experiment?
    The dependent variable is the outcome being measured, such as plant growth or exam scores.
  • What is the first test you should perform in a two-way ANOVA?
    You should first test for interaction effects before testing the effects of each individual factor.
  • What happens if there is a significant interaction effect in two-way ANOVA?
    If there is a significant interaction, you cannot proceed with testing the individual effects of each factor independently.
  • How is the F-statistic for interaction calculated in two-way ANOVA?
    The F-statistic for interaction is calculated as the ratio of mean squares due to interaction to mean squares due to error.
  • What is the purpose of an interaction plot in two-way ANOVA?
    An interaction plot visually assesses whether there is an interaction effect by comparing the parallelism of lines representing factor levels.
  • What does it indicate if lines in an interaction plot are parallel?
    Parallel lines suggest that the factors are independent and there is no interaction effect.
  • What does it indicate if lines in an interaction plot are not parallel?
    Non-parallel lines indicate that there is an interaction effect between the factors.
  • How do you test the effect of a single factor in two-way ANOVA after finding no interaction?
    You use an F-statistic to compare the mean squares for that factor to the mean squares due to error, similar to one-way ANOVA.
  • What is the null hypothesis when testing the effect of a single factor in two-way ANOVA?
    The null hypothesis states that there is no difference in means due to that factor.
  • What should you conclude if the p-value for a factor is less than the alpha level in two-way ANOVA?
    If the p-value is less than alpha, you reject the null hypothesis and conclude there is evidence of a difference in means due to that factor.