Logistic Regression

  • Venkat Reddy Konasani
  • Shailendra Kadre
Chapter

Abstract

In previous chapters, we covered correlation and linear regression modeling in detail. If you look to quantify the relationship between two variables, you use the correlation coefficient. For example, you can quantify the relation between salary and expenses using correlation. If you needed to predict a response variable based upon some other item, you could use linear regression modeling, provided the relationship is linear. For example, if you want to predict exactly what a person’s expenses will be when his salary is $10,000, you can use linear regression modeling, provided the expense and salary fit on a straight-line graph. In some cases, this relationship is not actually linear, but you can make it linear by applying some simple mathematical transformations; still, you can use linear regression modeling.

Keywords

Logistic Regression Linear Regression Modeling Linear Regression Model Beta Coefficient Odds Ratio Estimate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Venkat Reddy Konasani 2015

Authors and Affiliations

  • Venkat Reddy Konasani
    • 1
  • Shailendra Kadre
    • 1
  1. 1.APIndia

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