Beispiel zur Multikollinearität WS 98/99 Umdruck 6
Seite - 1 - Worksheet size: 100000 cells
MTB > # Opening worksheet from file: C:\ARBEIT\Word\EDV-Prakt\Statistik\Multi.dat
MTB > # File was last modified on 12/11/96 MTB > print c1-c3
Data Display
Row C1 C2 C3 1 302 14 32 2 338 15 33 3 362 26 35 4 361 23 36 5 422 30 40 6 380 33 41 7 408 33 44 8 447 38 44 9 495 42 47 10 480 46 48 MTB > desc c1-c3
Descriptive Statistics
Variable N Mean Median Tr Mean StDev SE Mean C1 10 399.5 394.0 399.8 62.4 19.7 C2 10 30.00 31.50 30.00 10.69 3.38 C3 10 40.00 40.50 40.00 5.77 1.83 Variable Min Max Q1 Q3
C1 302.0 495.0 355.2 455.3 C2 14.00 46.00 21.00 39.00 C3 32.00 48.00 34.50 44.75 MTB > corr c1-c3
Correlations (Pearson)
C1 C2 C2 0.948
C3 0.947 0.972
Beispiel zur Multikollinearität WS 98/99 Umdruck 6
Seite - 2 - MTB > plot c2*c3
50 40
30 45
35
25
15
C3
C 2
MTB > brief=3 MTB > regr c1 1 c2
Regression Analysis
The regression equation is C1 = 234 + 5.53 C2
Predictor Coef StDev T P Constant 233.65 20.88 11.19 0.000 C2 5.5282 0.6594 8.38 0.000 S = 21.14 R-Sq = 89.8% R-Sq(adj) = 88.5%
Analysis of Variance
Source DF SS MS F P Regression 1 31417 31417 70.29 0.000 Error 8 3576 447
Total 9 34993
Obs C2 C1 Fit StDev Fit Residual St Resid 1 14.0 302.00 311.05 12.49 -9.05 -0.53 2 15.0 338.00 316.58 11.94 21.42 1.23 3 26.0 362.00 377.39 7.19 -15.39 -0.77 4 23.0 361.00 360.80 8.12 0.20 0.01 5 30.0 422.00 399.50 6.69 22.50 1.12 6 33.0 380.00 416.08 6.97 -36.08 -1.81 7 33.0 408.00 416.08 6.97 -8.08 -0.41 8 38.0 447.00 443.73 8.52 3.27 0.17 9 42.0 495.00 465.84 10.36 29.16 1.58
Beispiel zur Multikollinearität WS 98/99 Umdruck 6
Seite - 3 - 10 46.0 480.00 487.95 12.49 -7.95 -0.47
MTB > regr c1 1 c3
Regression Analysis
The regression equation is C1 = - 9.7 + 10.2 C3
Predictor Coef StDev T P Constant -9.70 49.42 -0.20 0.849 C3 10.230 1.224 8.36 0.000 S = 21.20 R-Sq = 89.7% R-Sq(adj) = 88.4%
Analysis of Variance
Source DF SS MS F P Regression 1 31396 31396 69.83 0.000 Error 8 3597 450
Total 9 34993
Obs C3 C1 Fit StDev Fit Residual St Resid 1 32.0 302.00 317.66 11.87 -15.66 -0.89 2 33.0 338.00 327.89 10.88 10.11 0.56 3 35.0 362.00 348.35 9.08 13.65 0.71 4 36.0 361.00 358.58 8.30 2.42 0.12 5 40.0 422.00 399.50 6.71 22.50 1.12 6 41.0 380.00 409.73 6.82 -29.73 -1.48 7 44.0 408.00 440.42 8.30 -32.42 -1.66 8 44.0 447.00 440.42 8.30 6.58 0.34 9 47.0 495.00 471.11 10.88 23.89 1.31 10 48.0 480.00 481.34 11.87 -1.34 -0.08
Beispiel zur Multikollinearität WS 98/99 Umdruck 6
Seite - 4 - MTB > regr c1 2 c2 c3;
SUBC> xpxi m1;
SUBC> mse k1.
Regression Analysis
The regression equation is C1 = 109 + 2.84 C2 + 5.13 C3
Predictor Coef StDev T P Constant 109.4 128.8 0.85 0.424 C2 2.836 2.833 1.00 0.350 C3 5.126 5.244 0.98 0.361 S = 21.20 R-Sq = 91.0% R-Sq(adj) = 88.4%
Analysis of Variance
Source DF SS MS F P Regression 2 31846 15923 35.43 0.000 Error 7 3146 449
Total 9 34993 Source DF Seq SS C2 1 31417 C3 1 429
Obs C2 C1 Fit StDev Fit Residual St Resid 1 14.0 302.00 313.12 12.70 -11.12 -0.66 2 15.0 338.00 321.08 12.83 16.92 1.00 3 26.0 362.00 362.53 16.82 -0.53 -0.04 4 23.0 361.00 359.15 8.32 1.85 0.10 5 30.0 422.00 399.50 6.70 22.50 1.12 6 33.0 380.00 413.13 7.62 -33.13 -1.67 7 33.0 408.00 428.51 14.51 -20.51 -1.33 8 38.0 447.00 442.69 8.61 4.31 0.22 9 42.0 495.00 469.41 11.01 25.59 1.41 10 46.0 480.00 485.88 12.70 -5.88 -0.35
MTB > mult m1 k1 m2 MTB > prin m2
Data Display
Matrix M2
16600.6 337.1 -666.7 337.1 8.0 -14.4 -666.7 -14.4 27.5 MTB >
