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 January 23, 2020.

Ronny Gunnarsson. Regression and correlation [in Science Network TV]. Available at: https://science-network.tv/regression-and-correlation/. Accessed January 23, 2020.

Suggested pre-reading |
What this web page adds |
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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 regression | Multivariable regression = multiple regression | Multivariate regression | Multivariate multivariable regression | |
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Standard linear regression | Simple standard linear regression = Unadjusted standard linear regression | Multivariable standard linear regression = multiple standard linear regression = adjusted standard linear regression | Factor analysis | Factor analysis |

Logistic regression | Simple logistic regression = unadjusted logistic regression | Multivariable logistic regression = multiple logistic regression = adjusted logistic regression | Multivariate probit regression (bivariate probit regression is a special case with two dependent variables) | Multivariate multivariable probit regression |

Proportional hazards regression = Cox regression | Simple proportional Hazards regression | Multivariable 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 January 23, 2020.

Ronny Gunnarsson. Regression and correlation [in Science Network TV]. Available at: https://science-network.tv/regression-and-correlation/. Accessed January 23, 2020.