Intro

Background

Loading data

Setting up the data

ANOVA command in R

Summary ANOVA Results

Summary of data

Conclusion

Visualize our data

Assumptions

Faceted plot

Summary Model

Post Hoc Test

Intro

What is an ANOVA test?

Reading in Data

Calculating Weight Loss

ANOVA Test Calculations

ANOVA Functions

Linear Model

Conclusion

Aov() function

Summary

What is ANOVA?

Interpreting results

Boxplots & density plots

Null hypothesis and alternative hypothesis

Gapminder Dataset

Dataset setup

Selecting variables for analysis

Summarizing data

Hypothesis testing

Conclusion

Intro

Reading in data

Renaming columns

Data matrix for ANOVA

Replicating the data

Getting weights for each team

Plotting the data

Running ANOVA

Mean squared error (MSE)

Conclusion

Average Rem Sleep

Summary

Pipe Operators

when should we use one-way analysis of variance (ANOVA) in statistics and in research

how to conduct ANOVA in R software using the "aov" command/function

how to access the help menu in R for ANOVA commands

how to create a boxplot in R statistical software

how to view ANOVA table in R using "summary" function

how to ask R for what is stored in an object using the "attributes" function.

how to extract certain attributes from an object in R using the dollar sign ($)

how to conduct multiple comparisons/pair-wise comparisons for the analysis of variance in R using the "TukeyHSD" command

how to produce a visual display for the pair-wise comparisons of the analysis of variance in R programming language using "plot" function

how to produce Kruskal-Wallis one-way analysis of variance using ranks in R using the "kruskal.test" function

when is it appropriate to use Kruskal-Wallis one-way analysis of variance for data in statistics and in research

Packages needed

Drinking motives

Extracting stress condition factor

Linear regression model

Effect size

Bonferroni adjustments

Multi factorial ANOVA

Estimated marginal effects

Interpreting contrasts

White-adjusted ANOVA

Data Frame

Tidy Data

Example of What Not To Do

Three Major Assumptions for Anova

Side-by-Side Box Plots

Normality

Draw a Qq Plot for each of the Different Groups

Date Variable Is Encoded as a Factor

Generate the Model with the Aov

The Summary Output from the Linear Model

Output

The Contrast Function

Variance Inflation Factor

Variance Inflation Factors

Final Visualization

Types of Anova

Factorial Factorial Anova Analysis

Open the Data Set

Library Ggplot2

Conclusion

The Normality Statistics

A Qq Plot

Test of Normality

Levine's Test of Homogeneity

One-Way Anova

Intro

Two-Way ANOVA Hypothesis Tests

Dataset overview

Two-Way ANOVA Diagram

Plotting Data

Two-way ANOVA

Summary() function

Interpreting the ANOVA table

Summary

Intro

Dataset description

Dataset overview

Identifying factors

Visualization of data

Two-way ANOVA model

Interaction effect

Shapiro test for normality

Homogeneous variance

Interpreting output

Getting data

Reading in data

Side-by-side boxplots

ANOVA table in R

Intro

Example

Data overview

Categorical factors

Setting group as a factor

Homogeneity of variances

One-way ANOVA test

Interpreting p-values

Visualizing data

Summary

Intro

Saving your data file

Importing data into R Studio

Loading the data

Importing the data

Loading the AFX package

Viewing ANOVA output

Suppressing the mean square error

Conclusion

Data overview

Importing data

Plotting graphs

Factor for fertilizer

Residual plot

QQ Plot

Histogram of standardized residuals

Rejecting the null hypothesis

Post-hoc tests

Interpreting results