Methods of Correlation and Regression Analysis, Linear and Curvilinear

By Mordecai Ezekiel; Karl A. Fox | Go to book overview

CHAPTER 12
Measuring accuracy of estimate and degree of correlation for multiple linear regressions

Standard Error of Estimate. After working out equations by which values of one variable may be estimated from those for two or more independent variables, it is frequently desirable to have some measure of how closely such estimates agree with the actual values and of how closely the variation in the dependent variable is associated with the variation in the several independent variables. Attention has been called in the preceding chapters to the computation of the residuals, z, when the value of a variable is estimated from that of several others. Where the estimate is based on several independent variables the standard deviation of these residuals serves as a measure of the closeness with which the original values may be estimated or reproduced just as well as where the estimate is based on a single variable. Continuing the same terminology as before, this standard deviation is still called the "standard error of estimate." Thus for the regression equation for estimating income from known numbers of acres, cows, and men, the standard error of estimate is designated S1.234. The subscripts 1.234 indicate that that is the standard error for variable X1 when estimated from the independent variables X2, X3, and X4.

Where the size of the sample is small in proportion to the number of variables involved, the standard deviation of the residuals for the cases included in the sample tends to have a downward bias. That is, it tends to be smaller than the standard error which would be observed if the same constant were computed from large samples drawn from the same universe.

For that reason it is necessary to adjust the square of the observed standard deviation of the residuals, sz, before it will give an unbiased

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