Testing the difference between two sets of data using comparison two linear regression functions

Authors

  • Unchalee Tonggumnead Faculty of Science and Technology, Rajamangala University of Technology Thanyaburi, 39 Moo1, Rangsit-Nakhonnayok Rd. Klong6, Thanyaburi, Pathum Thani 12110Thailand

DOI:

https://doi.org/10.1285/i20705948v7n2p279

Keywords:

regression function, linear relationship, empirical distribution, bootstrap procedure, expectation function, error distribution

Abstract

This study aims to compare two sets of data with each having a linear relationship between the independent and dependent variables. The problem is solved by testing the equality of two regression functions. The test statistics based on empirical distribution : the Kolmogorov-Smirnov and Kuiper type statistics are considered, under the alternative hypotheses comprised of a constant shift and an affine shift. Additionally, the rejection proportion is calculated using the bootstrap method. The  test statistics are also applied to the analysis of two sets of data, the characteristics of which are found to be consistent with the p-value after 1,000 trials of bootstrapping.

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Additional Files

Published

14-10-2014

Issue

Vol. 7 No. 2 (2014): Electronic Journal of Applied Statistical Analysis

Section

Original Paper

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