Review of Regression Analysis Models

Review of Regression Analysis Models
Authors : Aviral Gupta, Akshay Sharma, Dr. Amita Goel
Publication Date: 04-08-2017


Author(s):  Aviral Gupta, Akshay Sharma, Dr. Amita Goel

Published in:   International Journal of Engineering Research & Technology

License:  This work is licensed under a Creative Commons Attribution 4.0 International License.

Website: www.ijert.org

Volume/Issue:   Volume. 6 - Issue. 08 , August - 2017

e-ISSN:   2278-0181

 DOI:  http://dx.doi.org/10.17577/IJERTV6IS080060


In statistics and data analysis, we often need to establish a relationship between the various parameters in a data set. This relationship is important for prediction and analysis. Regression Analysis is such a technique. This work mainly focuses on the different Regression Analysis models used nowadays and how they are used in context of different data sets. Picking the right model for analysis is often the most difficult task and therefore, these models are looked upon closely in this research. While a Linear Regression Analysis model is used to fit linear data, a Polynomial Regression Analysis model focuses on a data set representing polynomial relationship between data parameters. Logistic Regression model is used in a scenario where we need a binary type of prediction. When the data set becomes complex, these models may suffer from issues like Underfitting and Overfitting. Ridge and Lasso Regression are considered the best models to deal with this type of situation. Ridge regression is used when data suffers from multicollinearity, that is independent variables are highly correlated. Lasso regression differs from ridge regression in a way that it uses absolute values in the penalty function, instead of squares. Using these models in the right way and with right data set, Data Analysis and Prediction can produce the most accurate results.


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