Two-Way ANOVA Concepts

What are factor levels in a two-way ANOVA?

• Factor levels are the residuals calculated in a two-way ANOVA

• Factor levels are the p-values associated with each factor in a two-way ANOVA

• Factor levels in a two-way ANOVA are the different categories or groups within each factor being analyzed.

• Factor levels are the interaction effects between factors in a two-way ANOVA

Explain the concept of interaction effects in a two-way ANOVA.

• Interaction effects in a two-way ANOVA refer to the impact of a single independent variable on the dependent variable.

• Interaction effects in a two-way ANOVA are only present when the independent variables are perfectly correlated.

• Interaction effects in a two-way ANOVA are only relevant when the dependent variable is categorical.

• Interaction effects in a two-way ANOVA show how the relationship between the independent variables and the dependent variable is not additive but rather influenced by the interaction between the variables.

Define within-group variation in the context of ANOVA.

• Variability of data points within each group being compared.

• Variability of data points across different experiments.

• Variability of data points between groups being compared.

• Variability of data points within a single data point.

Describe between-group variation and its significance in ANOVA.

• Between-group variation only considers extreme values in the data.

• Between-group variation is not relevant in ANOVA analysis.

• Between-group variation is the same as within-group variation in ANOVA.

• Between-group variation is the differences in means between the groups being compared in ANOVA. It is significant as it helps determine if the observed differences between groups are statistically meaningful.

What are the assumptions that need to be met for conducting ANOVA?

• Outliers present in the data

• Linear relationship between variables

• Equal sample sizes

• Independence of observations, Normality of residuals, Homogeneity of variances