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Statistics vocabulary · Regression and Correlation

Linear regression

Meaning in statistics, with examples

nounCore term

In statistics, linear regression means: A regression method that models the outcome as a straight-line function of one or more predictors, estimating an intercept and one slope per predictor.

In Turkish: doğrusal regresyon, lineer regresyon

How it is used

Simple linear regression has one predictor; multiple linear regression has several. It is usually estimated by ordinary least squares (OLS). Its main assumptions are linearity, independent errors, constant variance of the residuals and roughly normal residuals.

Example sentences

  1. A simple linear regression was used to predict salary from years of experience.

  2. Check the residual plots before you trust a linear regression.

Common collocations

  • simple linear regression
  • fit a linear regression
  • linear regression model
  • ordinary least squares
  • regressionA family of methods for modelling how an outcome variable changes with one or more predictor variables, used both to explain relationships and to make predictions.
  • least squaresA method of fitting a line or model by choosing the parameter values that make the sum of the squared residuals as small as possible.
  • regression coefficientA number in a regression model showing how much the outcome is expected to change when a predictor increases by one unit, with the other predictors held constant.
  • residualThe difference between an observed value and the value a model predicts for that observation; it shows how far the model misses each data point.
  • multiple regressionA regression model with two or more predictor variables, used to estimate the effect of each one while holding the others constant.

More terms: Regression and Correlation

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