Statnote 16: fitting a regression line to data

Anthony Hilton, Richard A. Armstrong

Research output: Contribution to specialist publicationArticle

Abstract

Fitting a linear regression to data provides much more information about the relationship between two variables than a simple correlation test. A goodness of fit test of the line should always be carried out. Hence, ‘r squared’ estimates the strength of the relationship between Y and X, ANOVA whether a statistically significant line is present, and the ‘t’ test whether the slope of the line is significantly different from zero. In addition, it is important to check whether the data fit the assumptions for regression analysis and, if not, whether a transformation of the Y and/or X variables is necessary.
Original languageEnglish
Pages40-42
Number of pages3
Volume2009
Specialist publicationMicrobiologist
Publication statusPublished - Mar 2009

Fingerprint

regression
regression analysis

Keywords

  • linear regression
  • data
  • correlation test

Cite this

Hilton, Anthony ; Armstrong, Richard A. / Statnote 16: fitting a regression line to data. In: Microbiologist. 2009 ; Vol. 2009. pp. 40-42.
@misc{d26c42d847604f8e9f073bcfdf282942,
title = "Statnote 16: fitting a regression line to data",
abstract = "Fitting a linear regression to data provides much more information about the relationship between two variables than a simple correlation test. A goodness of fit test of the line should always be carried out. Hence, ‘r squared’ estimates the strength of the relationship between Y and X, ANOVA whether a statistically significant line is present, and the ‘t’ test whether the slope of the line is significantly different from zero. In addition, it is important to check whether the data fit the assumptions for regression analysis and, if not, whether a transformation of the Y and/or X variables is necessary.",
keywords = "linear regression, data, correlation test",
author = "Anthony Hilton and Armstrong, {Richard A.}",
year = "2009",
month = "3",
language = "English",
volume = "2009",
pages = "40--42",
journal = "Microbiologist",
issn = "1479-2699",

}

Hilton, A & Armstrong, RA 2009, 'Statnote 16: fitting a regression line to data' Microbiologist, vol. 2009, pp. 40-42.

Statnote 16: fitting a regression line to data. / Hilton, Anthony; Armstrong, Richard A.

In: Microbiologist, Vol. 2009, 03.2009, p. 40-42.

Research output: Contribution to specialist publicationArticle

TY - GEN

T1 - Statnote 16: fitting a regression line to data

AU - Hilton, Anthony

AU - Armstrong, Richard A.

PY - 2009/3

Y1 - 2009/3

N2 - Fitting a linear regression to data provides much more information about the relationship between two variables than a simple correlation test. A goodness of fit test of the line should always be carried out. Hence, ‘r squared’ estimates the strength of the relationship between Y and X, ANOVA whether a statistically significant line is present, and the ‘t’ test whether the slope of the line is significantly different from zero. In addition, it is important to check whether the data fit the assumptions for regression analysis and, if not, whether a transformation of the Y and/or X variables is necessary.

AB - Fitting a linear regression to data provides much more information about the relationship between two variables than a simple correlation test. A goodness of fit test of the line should always be carried out. Hence, ‘r squared’ estimates the strength of the relationship between Y and X, ANOVA whether a statistically significant line is present, and the ‘t’ test whether the slope of the line is significantly different from zero. In addition, it is important to check whether the data fit the assumptions for regression analysis and, if not, whether a transformation of the Y and/or X variables is necessary.

KW - linear regression

KW - data

KW - correlation test

UR - http://issuu.com/societyforappliedmicrobiology/docs/mar09_micro

M3 - Article

VL - 2009

SP - 40

EP - 42

JO - Microbiologist

JF - Microbiologist

SN - 1479-2699

ER -