Estimation and Hypothesis Testing

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Boundless StatisticsEstimation and Hypothesis TestingTwo-Way ANOVATwo-Way ANOVATwo-way ANOVA examines the influence of different categorical independent variables on one dependent variable.Learning ObjectivesDistinguish the two-way ANOVA from the one-way ANOVA and point out the assumptions necessary to perform the test.Key TakeawaysKey PointsThe two-way ANOVA is used when there is more than one independent variable and multiple observations for each independent variable.The two-way ANOVA can not only determine the main effect of contributions of each independent variable but also identifies if there is asignificant interaction effect between the independent variables.Another term for the two-way ANOVA is a factorial ANOVA, which has fully replicated measures on two or more crossed factors.In a factorial design multiple independent effects are tested simultaneously.Key Termstwo-way ANOVA:an extension of the one-way ANOVA test that examines the influence of different categorical independent variables on onedependent variableorthogonal:statistically independent, with reference t o variateshomoscedasticif all random variables in a sequence or vector have the same finite varianceThe two-way analysis of variance {ANOVA) test is an extension of the one-way ANOVA test that examines the influence of different categoricalindependent variables on one dependent variable. While the one-way ANOVA measures the significant effect of one independent variable (IV),the two-way ANOVA is used when there is more than one IV and multiple observations for each IV. The two-way ANOVA can not only determinethe main effect of contributions of each IV but also identifies if there is a significant interaction effect between the IVs.Assumptions of the Two-Way ANOVAAs with other parametric tests, we make the following assumptions when using two-way ANOVA:The populations from which the samples are obtained must be normally distributed.Sampling is done correctly. Observations for within and between groups must be independent.The variances among populations must be equal (homoscedastic).Data are interval or nominal.

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