One Way ANOVA:

- To begin with ANOVA, please check Box-plot to find any outlier. If

found, please extrapolate that.

- ANOVA: Sum of the weights of the contrasts is always zero.

- Sample size & Variance equal: LSD

- Hochberg’s GT2 test = Population variances are very different

- First Levene’s Homogeneity Test & Shapiro test to check the normality,

then F-Test (ANOVA), then in order to ascertain do Planned

comparison: Contrast & Unplanned : Post-Hoc, then perform

robustness test Welch test.

- Dependable variable: Measurable, Independent variable: Categorical,

Distribution should not contain any outlier.

- If it fails to meet normality, do non-parametric test (Kruskal-Wallis

Test) because all the time it is not possible to take log transform when

it does not have any value i.e. categorical.

Two way ANOVA

- Two independent or categorical variables, we want to check the

impact.

- Steps:

Chart builder: Box-Plot – Dependent: Test Score (Perform for both

the independent variables) to check Outlier.

General Linear Model -> Univariate (Note: for MANOVA use

multivariate) -> Dependent & Independent variables (into fixed

factors because they are prefixed for this example as drug dose

standards are fixed by medical board) -> Plots -> Drug dose into

horizontal & Gender into Separate lines (to check

interdependency or combined effects) -> Add

Options -> Display means -> Descriptive Statistics, homogeneity

(Levene’s Test of Equality of Error Variances) - > Test of

between-subjects test -> Estimated Marginal Means (Note: We

are interested to check the impacts of gender, drug dose and the

interaction effect of gender and drug dose)

In this case partial eta square statistic gives the practical

significance of each term. Larger the value means larger

variation in the data set.

Use of contrast, Post-Hoc test in similar fashion.

Gabriel test (Post-Hoc)