This paper is published in Volume-6, Issue-4, 2020
Area
Computer Science
Author
A. Kalyan Aravind Kumar
Org/Univ
Great Lakes Institute of Management, Chennai, Tamil Nadu, India
Pub. Date
20 August, 2020
Paper ID
V6I4-1394
Publisher
Keywords
Logistic Regression, Linear Discriminant Analysis, Multicollinearity, Fisher Analysis

Citationsacebook

IEEE
A. Kalyan Aravind Kumar. Accelerating sales for Citrix Education Services using Logistic Regression, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
A. Kalyan Aravind Kumar (2020). Accelerating sales for Citrix Education Services using Logistic Regression. International Journal of Advance Research, Ideas and Innovations in Technology, 6(4) www.IJARIIT.com.

MLA
A. Kalyan Aravind Kumar. "Accelerating sales for Citrix Education Services using Logistic Regression." International Journal of Advance Research, Ideas and Innovations in Technology 6.4 (2020). www.IJARIIT.com.

Abstract

This paper aims to establish an efficient model for predicting company sales by leveraging logistic regression's strengths. A real dataset of Citrix used to figure out a significant variable affecting sales acceleration and to find an appropriate metric to measure unstructured information. To build an efficient model, we used two statistical methods; logistic regression and linear discriminant analysis. The classification accuracy of the models compared using Fisher Analysis, ROC curves, and confusion matrix. In regression analysis, it is evident that response and predictors some times may suffer from correlation issues. By definition, Multicollinearity is that two or more predictors are correlated; if this happens, the coefficients' standard error will increase. Increased standard errors mean that the coefficients for some or all independent variables may be significantly different from 0. In other words, Multicollinearity makes some variables statistically insignificant by overinflating the standard errors when they should be significant. In this paper, we concentrate on logistic regression analysis, linear discriminant analysis, Multicollinearity, fisher analysis, and consequences and effects on the reliability of the regression model
Paper PDF