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Standard Error in Linear Regression

FRM Exam, Statistics

This lesson is part 6 of 8 in the course Linear Regression

A simple (two-variable) regression has three standard errors: one for each coefficient (slope, intercept) and one for the predicted Y (standard error of regression).

While the population regression function (PRF) is singular, sample regression functions (SRF) are plural. Each sample produces a different SRF. So, the coefficients exhibit dispersion (sampling distribution). The standard error is the measure of this dispersion: it is the standard deviation of the coefficient.

In this video, David from Bionic Turtle talks about the standard error in linear regression.

Previous Lesson

‹ Ordinary Least Squares (OLS)

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ANOVA Table in Regression ›

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In this Course

  • Introduction to Linear Regression
  • Standard Error of Estimate (SEE)
  • Coefficient of Determination (R-Squared)
  • Sample Regression Function (SRF)
  • Ordinary Least Squares (OLS)
  • Standard Error in Linear Regression
  • ANOVA Table in Regression
  • Using LINEST() Function in Excel for Multivariate Regression

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