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Home » Courses » **R Studio Anova Techniques Course**

This is a online course is to gain fundamental understanding of R-Studio Annova Techniques. The aim is to learn about data science driven hypothesis testing and subsequent decision making.. The tutorials will help you learn about One-way Anova, Ratings, Anova - Randomized Block Design, Anova and the Design of Experiments and Variable reduction technique(Factor Analysis).

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R Studio Anova Techniques Course is an online training which will help you to have a basic understanding of R-Studio ANOVA techniques. It will help you to do Analysis of Variance test also known as Anova in the statistical software R. Anova is an quick and easy way to test the differences between two or more means. It is accessible and applicable to the people even out of the statistics field with ease. Before Anova was introduced t-test and z-test were used. But these tests cannot be used where there are more than two groups. So Anova was introduced.

R is a free open source software in the field of statistics. It has all the text commands written in the R language. This introductory level course requires no expert knowledge in R. It will help you to know the basic commands of R to perform Anova and will make you an expert in performing Anova using R at the end of this course.

In R Anova is used to differentiate between several samples. R offers a function to perform Anova using the command aov (model, data). There are four basic assumptions of Anova. They are

- the expected values of the errors are zero
- the variances of all errors are equal to each other
- the errors are independent
- they are normally distributed

R supplies a variety of ways to perform Anova. It also offers a lot of functions and each function has its own advantages and disadvantages. The statistical process is derived from the estimates of the population variances through different approaches. One approach is based on the variance of the sample means and the second approach is based on the variance of the sample variances. Using R you can test the null hypothesis based on the various response data from different treatments.

By the end of this course you will be able to

- Find out which tests to be used to answer different type of questions
- Know the difference between one way Anova, two way Anova and factorial Anova
- Perform the Anova calculation using R
- Explain and interpret the output in R
- Determine the power and effect size for Anova
- Find out the assumptions of Anova

Performing Anova in R requires you to have an basic understanding of the text based computer languages. If you have no experience with text based software then you can try it using the JMP statistics software.

You should be able to have easy access to the computer and a data set to analyze.

- Anova is used by students who pursue their engineering course and also by students of other courses during their project or research work.
- Professionals from IT industry is also using Anova in their project.So this course will be very useful to them.
- And finally anyone who is interested in learning Anova can undertake this course.
- At the end of this course you will become an expert in performing Anova using R.

**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.

**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.

**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.

**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.

**Why should I use R when I am already familiar with other data analysis package ?**

One main reason behind learning R is that it is a very powerful programming language that can conduct a wide range of analysis. Thus for many projects all the type of analysis can be done with this single program. Once the data is entered into R it will be available for use until the R program is closed. Users of R can also submit their own packages to R server and anyone can use it. Because of this reason R has a large variety of packages that will let you do everything in analysis. And finally R is a free open source programming language which can be used in various operating systems. Its syntax and packages can be transferred from one system to another and therefore it will help you in your research to a great extent.

**Which is easier to use SPSS or R for performing Anova ?**

Of course using SPSS or excel to perform Anova is easy than using R. But using R to perform Anova gives you a better output. R has GUI which makes it easy to use. R is now widely accepted by many of the researchers and scientists because it contains all the statistical tools in the form of its packages. It is free. It is a multiplatform program which ensures easy collaboration and its open source ensures the reproducibility of the analysis. R can take you anywhere you want to go if you learn to use it properly.

**What are the advantages of R over SPSS or SAS ?**

R is a open source programming language with great data manipulation, built in statistics and graphical functionalities. In SPSS the input is entered mostly in the form of table and the other form of providing the input is probably too hard. But in R it is possible to provide various forms of input. In SPSS it is difficult to find the graph type for the output but in R it has a separate package to decide on that. In R you can integrate your workflow with your reports. For example, you can write a document using R code that will produce plots or tables, run the file and get the output in PDF format. Thus you can do more with your data in R.

**Knowledge gaining at an affordable cost**

If you are a decent programmer it is worth for you to learn R to perform Anova and other statistics in R. If you are a beginner and you wanted to learn on the inner workings of R in performing Anova then this course could be a great start for you. You can improve your understanding from this course at an affordable cost.

**Richie**

As a data analyst I usually spend a lot of hours working with R. I was attempting to understand R to perform Anova but I could not. Then I found this great source to learn about R studio to perform Anova. This is one of the best courses that I have come across to learn Anova in R. This R Studio Anova Techniques Course helped me to learn the basics of R correct and get started with my data sets for performing Anova.

**Harlan Greg**

This is probably a course that suits for someone who needs to learn Anova using R studio. It explains all the basics with more hands on practice. It begins with a introduction to R and Anova and slowly enters into the basics of performing the Anova calculation using R. The examples were easy to understand. This course has now made me work with Anova in R like an expert.

**Jeromy**

Thank you very much for putting the R Studio Anova Techniques Course on site. I am in the process of learning R and Anova in R. I was unable to find information on the net regarding this and finally ended up in this great course. It was of great help to me to understand R and conduct Anova in R with ease. Love this tutorial.

Where do our learners come from? |

Professionals from around the world have benefited from eduCBA’s R Studio Anova Techniques courses. Some of the top places that our learners come from include New York, Dubai, San Francisco, Bay Area, New Jersey, Houston, Seattle, Toronto, London, Berlin, UAE, Chicago, UK, Hong Kong, Singapore, Australia, New Zealand, India, Bangalore, New Delhi, Mumbai, Pune, Kolkata, Hyderabad and Gurgaon among many. |

1 | Anova Introduction |

2 | Respondant Table Format |

3 | Randomized Block Design |

4 | Running Anova Model in R studio |

5 | Assigning the Observations |

6 | New Menu Example |

7 | Introduction to Factorial |

8 | Learning about Factorial |

9 | Continuation of Factorial |

10 | Further Continuation of Factorial |

11 | Factorial(R Studio-1) |

12 | Factorial(R Studio-2) |

13 | Introduction to Factor Analysis |

14 | Understanding about Factor Analysis |

15 | Learning about Factor Analysis |

16 | Continuation of Factor Analysis |

17 | Factor Analysis(R Studio practice) |

18 | Continuation of Factor Analysis(R Studio practice) |