Regression and correlation

You must cite this article if you use its information in other circumstances. An example of citing this article is:
Ronny Gunnarsson. Regression and correlation [in Science Network TV]. Available at: https://science-network.tv/regression-and-correlation/. Accessed November 12, 2019.
Suggested pre-reading What this web page adds
  1. Introduction to statistics
  2. Observations and variables
  3. Inferential statistics
  4. Choosing statistical analysis
  5. Associations and predictions
This web-page provides an introduction to regression and correlation. Reading this will give you an introduction and overview of these concepts necessary to understand more (on following pages going into more details).

Introduction to correlation

(This section is still under construction. Sorry for the inconvenience.)

Introduction to regression

Regression and correlation focuses on how variables correlate. Often one or several variables are defined as dependent meaning that they are believed to alter when the value of other independent variables change. There are different types of regression :

  • Simple regression: One dependent and one independent variable
  • Multivariable regression = multiple regression: More than one independent variable
  • Multivariate regression: More than one dependent variable
  • Multivariate multivariable regression: More than one dependent variable as well as more than one independent variable.

Linear regression

The dependent variable and different types of linear regression

  • Standard linear regression: The dependent variable is measured with an interval or ratio scale.
  • Logistic regression: The dependent variable is almost always binary (there is an exception with ordinal logistic regression where the dependent variable can be measured by an ordinal scale).
  • Cox regression: The dependent variable consists of two variables. One is stating if the event of interest has happened (usually coded as “1”) or not (usually coded as “0”). The other variable states the time an individual has been followed so far irrespective if the event has happened.

Overview of different types of linear regression

Simple regressionMultivariable regression = multiple regression Multivariate regressionMultivariate multivariable regression
Standard linear regressionSimple standard linear regression = Unadjusted standard linear regressionMultivariable standard linear regression = multiple standard linear regression = adjusted standard linear regressionFactor analysisFactor analysis
Logistic regressionSimple logistic regression = unadjusted logistic regressionMultivariable logistic regression = multiple logistic regression = adjusted logistic regressionMultivariate probit regression (bivariate probit regression is a special case with two dependent variables)Multivariate multivariable probit regression
Proportional hazards regression = Cox regressionSimple proportional Hazards regressionMultivariable proportional Hazards regression / multiple proportional hazards regression??

Non-linear regression

(This section is still under construction. Sorry for the inconvenience.)

Further reading

References

1.
Hidalgo B, Goodman M. Multivariate or Multivariable Regression? Am J Public Health [Internet]. 2013 Jan [cited 2019 Mar 1];103(1):39–40. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3518362/
You must cite this article if you use its information in other circumstances. An example of citing this article is:
Ronny Gunnarsson. Regression and correlation [in Science Network TV]. Available at: https://science-network.tv/regression-and-correlation/. Accessed November 12, 2019.

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