Award Date

1-1-2007

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Mathematical Sciences

First Committee Member

Rohan Dalpatadu

Number of Pages

34

Abstract

In multiple linear regression involving several predictor variables, finding a suitable non-linear transformation of the predictors might be helpful to present the model in a simple functional form which is linear in the transformed variables. In this thesis, a computer code in C++ is developed to automate the process of finding a suitable transformation for the predictors. This is done by finding the transformation that yields the maximum correlation between the response and the transformed predictor. Several simulated examples are included to illustrate the method. A prime concern in calculating the correlation between two data sets is statistical accuracy. Correlation coefficients reveal the degree of correlation between two data sets. They are valued from -1 to 1. A positive value indicates correlation and negative values indicate anti-correlation.

Keywords

Determination; Function; Linear; Multiple; Predictor; Regression; Transformation; Variables

Controlled Subject

Mathematics

File Format

pdf

File Size

962.56 KB

Degree Grantor

University of Nevada, Las Vegas

Language

English

Permissions

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Identifier

https://doi.org/10.25669/2nuw-5jd6


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