Anova Introduction

Anova stands for analysis of variance. It is a statistical method used to test the difference between two or more groups. It is used to test general instead of specific differences among means. It is used to test various null hypothesis at the same time.

Respondent Table Format

Here you will know the format of the Anova table. The components of the table and the formula to be used for different type of Anova like one way Anova and Two way Anova are given with numerical examples.

Section 2 : Anova – Randomized Block Design

Randomized Block Design

In this section you will be able to study the impact of extraneous variables on the primary factor. You will also know the techniques to control and represent them in all the groups of the independent variable. An example will be given to make you understand the type of design. You can also find the output divided into three parts for easy explanation.

Running Anova Model in R studio

This section will give you details on how to download R in your computer, open it and run it in your system. This section also gives you detail about the basic comments to be run in R for Anova for single independent variable and multiple independent variable. This is explained with detailed examples for your easy understanding.

Assigning the observations

R is used to produce stem and leaf displays or box plots to plot the results of Anova and also to check the assumptions. In this section you can look at how the output and observations for various types of analysis looks like.

New Menu Example

Here we will see how to calculate Anova using an elaborated example. The step by step process of performing  Anova in R including the sections of reading your data into R, Allowing R to read the variables within the data file, deciding on the Anova Model, running the analysis and the output of the analysis. The comments to be used in R for performing all these functions are explained in this section.

Section 3 : Anova and the Design of Experiments

Introduction to Factorial

Under this design there are more than one factors to be considered for the experiment. The topics covered under this section includes Definition of factor design, the effects of two or more factors, factorial designs, factors arranged in a factorial design and Model Variability that includes Main effects and Interaction.

Learning about Factorial

The main purpose of factorial design is explained in this section. The difference in mean response for the different factors is studied here. The partition of variability is explained.

Continuation of Factorial

The formula used in factorial design will be studied in detail under this section. It includes formula for Sum of squares between, examples for each, output of each example and their interpretation. Graphical display of the main effects and the interaction is also given in this section.

Factorial (R Studio-1)

This section will cover the example of running a factorial Anova in R studio 1. It explains how to run the factorial Anova, run the pod hoc tests, multiple comparisons and contrasts, its interactions and how to make an interaction plot.

Factorial (R Studio-2)

Under this section you will be learning to perform factorial Anova in R Studio 2. The formulas are explained with example. The output are observed and represented in a graphical format.

Section 4 : Variable reduction technique (Factor Analysis)

Introduction to Factor Analysis

Here we will learn What is factor analysis ? why use factor analysis ? What is the key concept of factor analysis ? What are the factor loadings ?

Understanding about Factor Analysis

Under this heading you will be knowing the need for factor analysis. Where factor analysis need to be used, models of factor analysis, assumptions of factor analysis models.

Learning about Factor Analysis

This section covers the principal components and factor analysis. The commands used for principal components and factor analysis in R are explained here. The arguments and values of factor analysis which can be used in R are listed.

Continuation of Factor Analysis

Exploratory factor analysis is explained in this section. Its commands are explained in detail with examples. The ways to determine the number of factors is included here. Confirmatory factor analysis which is a subset of the Structured Equation Modelling is briefed in this section.

Factor Analysis(R Studio practice)

This post gives you an example of running a basic factor analysis in R. The example is provided along with the output and the screen plot in R.

Continuation of Factor Analysis(R Studio practice)

This section lets you learn how to perform Exploratory factor analysis and Confirmatory factor analysis using SEM in R Studio.

Course Duration:-2h 18m