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Two-Way ANOVA definitions

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  • Two-Way ANOVA
    Statistical method comparing three or more means across two factors to assess their individual and combined effects on a dependent variable.
  • Factor
    Categorical variable whose levels are used to group data and assess their influence on the measured outcome.
  • Dependent Variable
    Measured outcome in an experiment, influenced by the levels of the factors under study.
  • Interaction Effect
    Situation where the impact of one factor on the outcome depends on the level of another factor.
  • Null Hypothesis
    Assumption stating no effect or no interaction exists between factors or on the dependent variable.
  • Alternative Hypothesis
    Statement proposing that an effect or interaction exists between factors or on the dependent variable.
  • F Statistic
    Ratio of mean squares used to compare group variances and test hypotheses in ANOVA.
  • P-Value
    Probability measure indicating the likelihood of observing the data if the null hypothesis is true.
  • Alpha Level
    Threshold probability used to decide whether to reject the null hypothesis, commonly set at 0.05.
  • Mean Square
    Average of squared deviations, used in ANOVA calculations to partition variance among sources.
  • Error
    Variation in the dependent variable not explained by the factors or their interaction.
  • Interaction Plot
    Graphical tool displaying means for combinations of factor levels to visually assess interaction effects.
  • Parallel Lines
    Pattern in an interaction plot indicating independent effects of factors without interaction.
  • Non-Parallel Lines
    Pattern in an interaction plot suggesting the presence of interaction effects between factors.
  • Significance Level
    Chosen probability cutoff for determining statistical significance in hypothesis testing.