11.4: Summary

Quantitative Research Methods for Political Science, Public Policy and Public Administration · LibreTexts

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    From LibreTexts, under CC BY 4.0, as the source states it; its equations are written in TeX, which the PDF shows typeset. This page at LibreTexts.

    Whew! Now, using matrix algebra and calculus, you have derived the squared-error minimizing formula for multiple regression. Not only that, you can use the matrix form, in R, to calculate the estimated slope and intercept coefficients, predict YY, and even calculate the regression residuals. We’re on our way to true Geekdome!

    Next stop: the key assumptions necessary for OLS to provide the best, unbiased, linear estimates (BLUE) and the basis for statistical controls using multiple independent variables in regression models.

    1. It is useful to keep in mind the difference between “multiple regression” and “multivariate regression”. The latter predicts 2 or more dependent variables using an independent variable.↩
    2. The use of “prime” in matrix algebra should not be confused with the use of prime" in the expression of a derivative, as in X′X′.↩

    This page titled 11.4: Summary was last modified on Mon, 08 Jan 2024 03:13:05 GMT and is shared under a CC BY 4.0 license and was authored, remixed, and/or curated by Jenkins-Smith et al. (University of Oklahoma Libraries) via source content that was edited to the style and standards of the LibreTexts platform.