Autocorrel Durbin Watson Statistic 172443 Rho 13779 Variable Coefficient

Autocorrel durbin watson statistic 172443 rho 13779

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| Autocorrel: Durbin-Watson Statistic = 1.72443, Rho = .13779 | +-----------------------------------------------------------------------+ +---------+--------------+----------------+--------+---------+----------+ |Variable | Coefficient | Standard Error |t-ratio |P[|T|>t] | Mean of X| +---------+--------------+----------------+--------+---------+----------+ Constant -3.585879292 1.6459223 -2.179 .0429 le One\post-pre.xl PRE 1.517221644 .93156695E-01 16.287 .0000 18.695652 CLASS1 1.420780437 .90500685 1.570 .1338 .21739130 CLASS2 1.177398543 .78819907 1.494 .1526 .30434783 CLASS3 2.954037461 .76623994 3.855 .0012 .26086957 (Note: E+nn or E-nn means multiply by 10 to + or -nn power.) le One\post-pre.xls le One\post-pre.xls +-----------------------------------------------------------------------+ | Linearly restricted regression | | Ordinary least squares regression Weighting variable = none | | Dep. var. = POST Mean= 26.21739130 , S.D.= 5.384797808 | | Model size: Observations = 23, Parameters = 2, Deg.Fr.= 21 | | Residuals: Sum of squares= 53.19669876 , Std.Dev.= 1.59160 | | Fit: R-squared= .916608, Adjusted R-squared = .91264 | | (Note: Not using OLS. R-squared is not bounded in [0,1] | | Model test: F[ 1, 21] = 230.82, Prob value = .00000 | | Diagnostic: Log-L = -42.2784, Restricted(b=0) Log-L = -70.8467 | | LogAmemiyaPrCrt.= 1.013, Akaike Info. Crt.= 3.850 | | Note, when restrictions are imposed, R-squared can be less than zero. | William. E. Becker Module One, Part Two: Using LIMDEP Sept. 15, 2008: p. 24
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| F[ 3, 18] for the restrictions = 5.1627, Prob = .0095 | | Autocorrel: Durbin-Watson Statistic = 1.12383, Rho = .43808 | +-----------------------------------------------------------------------+ +---------+--------------+----------------+--------+---------+----------+ |Variable | Coefficient | Standard Error |t-ratio |P[|T|>t] | Mean of X| +---------+--------------+----------------+--------+---------+----------+ Constant -2.211829436 1.9004224 -1.164 .2597 le One\post-pre.xl PRE 1.520632737 .10008855 15.193 .0000 18.695652 CLASS1 .0000000000 ........ (Fixed Parameter) ........ .21739130 CLASS2 -.4440892099E-15 ........ (Fixed Parameter) ........ .30434783 CLASS3 -.4440892099E-15 ........ (Fixed Parameter) ........ .26086957 (Note: E+nn or E-nn means multiply by 10 to + or -nn power.) le One\post-pre.xls From the second part of this printout, the appropriate F to test H o : β 3 = β 4 = β 5 = 0 versus H a : at least one Beta is nonzero is F [df1=3,df2=18] = 5.1627, with p -value = 0.0095. Thus, null hypothesis that none of the dummies is significant at 0.05 Type I error level can be rejected in favor of the hypothesis that at least one class is significant, assuming that the effect of pre-course test score on post-course test score is the same in all classes and only the constant is affected by class assignment. STRUCTURAL (CHOW) TEST The above test of the linear restriction β 3 = β 4 = β 5 = 0 (no difference among classes), assumed that the pretest slope coefficient was constant, fixed and unaffected by the class to which a student belonged. A full structural test requires the fitting of four separate regressions to obtain the four residual sum of squares that are added to obtain the unrestricted sum of squares. The restricted sum of squares is obtained from a regression of posttest on pretest with no dummies for the classes; that is, the class to which a student belongs is irrelevant in the manner in which pretests determine the posttest score. The commands to be entered into the Document/text file of LIMDEP are as follows: Restricted Regression Sample; 1-23$ Regress ; Lhs =post; Rhs =one, pre$ Calc ; SSall = Sumsqdev$ William. E. Becker Module One, Part Two: Using LIMDEP Sept. 15, 2008: p. 25
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Unrestricted Regressions Sample; 1-5$ Regress ; Lhs =post; Rhs =one, pre$ Calc ; SS1 = Sumsqdev$ Sample; 6-12$ Regress ; Lhs =post; Rhs =one, pre$ Calc ; SS2 = Sumsqdev$ Sample; 13-18$ Regress ; Lhs =post; Rhs =one, pre$ Calc ; SS3 = Sumsqdev$ Sample; 19-23$ Regress ; Lhs =post; Rhs =one, pre$ Calc ; SS4 = Sumsqdev$ Calc;List ;F=((SSall-(SS1+SS2+SS3+SS4))/3*2) / ((SS1+SS2+SS3+SS4)/(23- 4*2))$ The LIMDEP output is --> RESET --> READ;FILE="C:\Documents and Settings\beckerw\My Documents\WKPAPERS\NCEE -...
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