Are ANOVA And Regression Similar In Terms Of Features?

It is often said that ANOVA and Linear Regression are somewhat similar analysis. However, in order to find out the real reason behind these tools being called similar to each other, there has to be a constructive approach and formation of statements on the basis of well-drawn reference and instances. If you are wondering is there really a similarity between ANOVA and Regression and does it make them complementing, then read through the lines of this informative blog for helpful information.

Similarity:

To begin with, both Regression and ANOVA try to derive the relationship between several dependent and independent variables. In other words, these tools actually try finding out the relationship between continuous outcome and predictor variable. This happens because from the mathematical perspective both of them are similar and other is an essential part of the regular linear model. Hence, it can be derived that Regression and ANOVA represent a similar model.

Difference:

However, it is also to be noted that there are certain points that set them apart. The most crucial difference between Regression and ANOVA lies in the origin of the development and use of the techniques. While Regression was developed mainly for non-experimental research, ANOVA on the other hand was developed for the experimental purpose. In case of ANOVA there is an experimental group and a control group. Talking of flexibility, ANOVA is said to be less flexible than Regression. There is a limited scope of the number of variables in ANOVA, when it comes to an independent variable. However, when it comes to Regression, you can expect all types of variables by using the dummy ones. This, as a result makes the scope of Regression wider as compared to that of ANOVA.

Is there any major difference between Regression and ANOVA?

Some researchers are of the opinion that the interaction is unique or limited, specifically in case of ANOVA. But it is to be noted that it is not exactly the case. It is quite simple to include interactions in Regression too. All you need to do is to attach an interaction term for two variables and come up with a new variable. The new variable you would come out with will be the product of the two variables. Regression deals with various types of outcome variables too. It also offers several options for the extensions along with facilities that ensure easy computing.

Hence, as already discussed, from a mathematical point of view, there is no difference between these two tools and their application. It is just that different academic disciplines use each of the models, as per the requirement and compatibility. For instance, Economics has largely utilized the Regression application, while Psychology has largely used the ANOVA application. Many researchers and other people have termed ANOVA as nothing but a special case of regression model. However, if the aspect of the utilization of these two tools is to be considered, then they will have some differences in term of features. ANOVA design has certain attributes and factors that are parameterized and statistically independent in certain ways.

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