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Thus, all three of these measures, SST, variance, and standard deviation, are univariate measures of the variability in y alone (although, of course, we could nd the variance and standard deviation of the predictor as well). Would we expect SST to be larger or smaller than SSE Using the calculations shown in Table 2.4, we have SST = 228, which is much larger than SSE = 12. We now have something to compare SSE against. Since SSE is so much smaller than SST, this indicates that using the predictor information in the regression results in much tighter estimates overall than ignoring the predictor information. These sums of squares measure errors in prediction, so that smaller is better. In other words, using the regression improves our estimates of the distance traveled. Next, what we would like is a measure of how much the estimated regression equation improves the estimates. Once again examine Figure 2.2. For hiker 10, the estimation error when using the regression is y y = 25 24 = 1, and the estimation error when ignoring the time information is y y = 25 16 = 9. Therefore, the amount of improvement (reduction in estimation error) is y y = 24 16 = 8. Once again, we may proceed to construct a sum of squares statistic based on y y. Such a statistic is known as SSR, the sum of squares regression, a measure of

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, 463 Faraone, S V, 337 Farquhar, J W, 106, 107, 327, 328 Farrell, A D, 195 Faust, D, 250, 253, 312 Faust, J, 340 Fava, G A, 307 Fava, J, 326 Feather, B W, 67 Feighner, J P, 315 Fein, D, 218, 220, 230, 231 Feinleib, M, 168 Feldman, J M, 15, 457 Felix, M R J, 106 Fensterheim, H, 109, 457 Ferguson, D B, 380, 381 Ferguson, K E, 446 Fernandez, E, 172 Fernandez, T, 223 Feske, U, 308 Fiester, A R, 305 Fink, P J, 452 Finkel, M J, 434 Finkelhor, D, 355 Finney, J W, 291, 329 Fiore, T A, 338 First, M B, 193, 195, 578 Fisch, R, 64 Fischbach, G D, 152, 231 Fisher, D.

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(f) The alternative hypothesis in the F-test for the overall regression asserts that the regression coef cients all differ from zero. (g) The standard error of the estimate is a valid measure of the usefulness of the regression, without reference to an inferential model (i.e., the assumptions need not be relevant). (h) If we were to use only the categorical variables as predictors, we would have to use analysis of variance and could not use linear regression. (i) For use in regression, a categorical variable with k categories must be transformed into a set of k indicator variables. (j) The rst sequential sum of squares is exactly the value for SSR from the simple linear regression of the response on the rst predictor. (k) The variance in ation factor has a minimum of zero, but no upper limit. (l) A variable that has been entered into the model early in the forward selection process will remain signi cant once other variables have been entered into the model. (m) The variable selection criteria for choosing the best model account for the multicollinearity among the predictors. (n) The variance in ation factors for principal components using varimax rotation always equal 1.0.

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, 24 Fisher, H, 152 Fishman, H C, 132 Fitzgibbon, M L, 170 Flay, B R, 293 Fleck, S, 26, 153, 437 Fleisher, M, 480 Fletcher, J, 340 Floyd, M, 349.

lated at some stage (91 out of 92), and these regulations were spread over the whole length of the run-ups An intertrial analysis was used by Montagne et al (2000) to show that the amount of adjustment produced was a function of the point at which regulation was initiated A linear relationship between stride number and amount of adjustment putatively showed that perception and action were closely coupled In the study of cricket bowlers, few correlations were found between stride number and amount of adjustment However, the inconsistent starting points of the bowlers, and initial high levels of variability, did not prevent them from achieving remarkably low levels of variability at the bound stride, consistent with findings in previous studies of long jumpers.

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