11.4: Summary
- Page ID
- 7256
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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.
- 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.↩
- 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′.↩