1 EXAMPLE 10. LOG-LINEAR REGRESSION: LUMBER DATA 1 16:27 Thursday, June 28, 2001 Obs Y X1 X2 X3 X4 LNY LNX1 LNX2 LNX3 LNX4 1 174 5.7 34 10 0.409 5.15906 1.74047 3.52636 2.30259 -0.89404 2 745 8.1 68 17 0.501 6.61338 2.09186 4.21951 2.83321 -0.69115 3 814 8.3 70 17 0.445 6.70196 2.11626 4.24850 2.83321 -0.80968 4 408 7.0 54 17 0.442 6.01127 1.94591 3.98898 2.83321 -0.81645 5 226 6.2 37 12 0.353 5.42053 1.82455 3.61092 2.48491 -1.04129 6 1675 11.4 79 27 0.429 7.42357 2.43361 4.36945 3.29584 -0.84630 7 1491 11.6 70 26 0.497 7.30720 2.45101 4.24850 3.25810 -0.69917 8 121 4.5 37 12 0.380 4.79579 1.50408 3.61092 2.48491 -0.96758 9 58 3.5 32 15 0.420 4.06044 1.25276 3.46574 2.70805 -0.86750 10 278 6.2 45 15 0.449 5.62762 1.82455 3.80666 2.70805 -0.80073 11 220 5.7 48 20 0.471 5.39363 1.74047 3.87120 2.99573 -0.75290 12 342 6.0 57 20 0.447 5.83481 1.79176 4.04305 2.99573 -0.80520 13 209 5.6 40 20 0.439 5.34233 1.72277 3.68888 2.99573 -0.82326 14 84 4.0 44 27 0.394 4.43082 1.38629 3.78419 3.29584 -0.93140 15 313 6.7 52 21 0.422 5.74620 1.90211 3.95124 3.04452 -0.86275 16 60 4.0 38 27 0.496 4.09434 1.38629 3.63759 3.29584 -0.70118 17 1692 12.1 74 27 0.476 7.43367 2.49321 4.30407 3.29584 -0.74234 18 74 4.5 37 12 0.382 4.30407 1.50408 3.61092 2.48491 -0.96233 19 515 8.6 60 23 0.502 6.24417 2.15176 4.09434 3.13549 -0.68916 20 766 9.3 63 18 0.458 6.64118 2.23001 4.14313 2.89037 -0.78089 21 345 6.5 57 18 0.474 5.84354 1.87180 4.04305 2.89037 -0.74655 22 210 5.6 46 12 0.413 5.34711 1.72277 3.82864 2.48491 -0.88431 23 100 4.3 41 12 0.382 4.60517 1.45862 3.71357 2.48491 -0.96233 24 122 4.5 42 12 0.457 4.80402 1.50408 3.73767 2.48491 -0.78307 25 539 7.7 64 19 0.478 6.28972 2.04122 4.15888 2.94444 -0.73814 26 815 8.8 70 22 0.496 6.70319 2.17475 4.24850 3.09104 -0.70118 27 194 5.0 53 23 0.485 5.26786 1.60944 3.97029 3.13549 -0.72361 28 280 5.4 61 23 0.488 5.63479 1.68640 4.11087 3.13549 -0.71744 29 296 6.0 56 23 0.435 5.69036 1.79176 4.02535 3.13549 -0.83241 30 462 7.4 52 14 0.474 6.13556 2.00148 3.95124 2.63906 -0.74655 31 200 5.6 48 19 0.441 5.29832 1.72277 3.87120 2.94444 -0.81871 32 229 5.5 50 19 0.506 5.43372 1.70475 3.91202 2.94444 -0.68122 33 125 4.3 50 19 0.410 4.82831 1.45862 3.91202 2.94444 -0.89160 1 EXAMPLE 10. LOG-LINEAR REGRESSION: LUMBER DATA 2 16:27 Thursday, June 28, 2001 The REG Procedure Model: MODEL1 Dependent Variable: Y Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model 4 5795682 1448920 100.21 <.0001 Error 28 404836 14458 Corrected Total 32 6200518 Root MSE 120.24328 R-Square 0.9347 Dependent Mean 429.75758 Adj R-Sq 0.9254 Coeff Var 27.97933 Parameter Estimates Parameter Standard Variable DF Estimate Error t Value Pr > |t| Type I SS Intercept 1 -508.19148 238.09998 -2.13 0.0417 6094822 X1 1 170.30760 18.37202 9.27 <.0001 5676934 X2 1 3.66550 3.76583 0.97 0.3387 21543 X3 1 11.26612 5.30548 2.12 0.0427 40324 X4 1 -1292.52966 651.65581 -1.98 0.0572 56881 Parameter Estimates Variable DF Type II SS Intercept 1 65865 X1 1 1242441 X2 1 13698 X3 1 65196 X4 1 56881 1 EXAMPLE 10. LOG-LINEAR REGRESSION: LUMBER DATA 3 16:27 Thursday, June 28, 2001 The REG Procedure Model: MODEL1 Dependent Variable: Y Output Statistics Dep Var Predicted Std Error Std Error Student Obs Y Value Mean Predict Residual Residual Residual 1 174.0000 171.2053 54.8343 2.7947 107.0 0.0261 2 745.0000 664.5205 51.7546 80.4795 108.5 0.742 3 814.0000 778.2947 53.5366 35.7053 107.7 0.332 4 408.0000 502.1244 23.8422 -94.1244 117.9 -0.799 5 226.0000 362.2695 60.8001 -136.2695 103.7 -1.314 6 1675 1473 69.8649 202.4206 97.864 2.068 7 1491 1374 62.4535 116.5067 102.8 1.134 8 121.0000 37.8482 42.3729 83.1518 112.5 0.739 9 58.0000 -168.6897 41.0145 226.6897 113.0 2.006 10 278.0000 301.3090 32.7863 -23.3090 115.7 -0.201 11 220.0000 255.0466 30.5476 -35.0466 116.3 -0.301 12 342.0000 370.1491 32.6328 -28.1491 115.7 -0.243 13 209.0000 250.0528 41.8640 -41.0528 112.7 -0.364 14 84.0000 129.2493 72.4240 -45.2493 95.985 -0.471 15 313.0000 514.6163 31.0072 -201.6163 116.2 -1.735 16 60.0000 -24.5817 74.4779 84.5817 94.401 0.896 17 1692 1513 63.9720 179.2817 101.8 1.761 18 74.0000 35.2632 41.7656 38.7368 112.8 0.344 19 515.0000 786.6545 41.8747 -271.6545 112.7 -2.410 20 766.0000 917.4070 37.1282 -151.4070 114.4 -1.324 21 345.0000 397.8723 31.0137 -52.8723 116.2 -0.455 22 210.0000 215.5226 36.4831 -5.5226 114.6 -0.0482 23 100.0000 15.8637 44.5586 84.1363 111.7 0.753 24 122.0000 -43.3491 44.0616 165.3491 111.9 1.478 25 539.0000 633.9959 34.5306 -94.9959 115.2 -0.825 26 815.0000 853.8601 38.9630 -38.8601 113.8 -0.342 27 194.0000 169.8617 42.5351 24.1383 112.5 0.215 28 280.0000 263.4311 51.6608 16.5689 108.6 0.153 29 296.0000 415.7923 37.9193 -119.7923 114.1 -1.050 30 462.0000 487.7572 43.2078 -25.7572 112.2 -0.230 31 200.0000 265.5256 23.5039 -65.5256 117.9 -0.556 32 229.0000 171.8114 45.9497 57.1886 111.1 0.515 33 125.0000 91.5251 47.8699 33.4749 110.3 0.303 Output Statistics Cook's Obs -2-1 0 1 2 D 1 | | | 0.000 2 | |* | 0.025 3 | | | 0.005 4 | *| | 0.005 5 | **| | 0.119 6 | |**** | 0.436 7 | |** | 0.095 8 | |* | 0.015 9 | |**** | 0.106 1 EXAMPLE 10. LOG-LINEAR REGRESSION: LUMBER DATA 4 16:27 Thursday, June 28, 2001 The REG Procedure Model: MODEL1 Dependent Variable: Y Output Statistics Cook's Obs -2-1 0 1 2 D 10 | | | 0.001 11 | | | 0.001 12 | | | 0.001 13 | | | 0.004 14 | | | 0.025 15 | ***| | 0.043 16 | |* | 0.100 17 | |*** | 0.245 18 | | | 0.003 19 | ****| | 0.160 20 | **| | 0.037 21 | | | 0.003 22 | | | 0.000 23 | |* | 0.018 24 | |** | 0.068 25 | *| | 0.012 26 | | | 0.003 27 | | | 0.001 28 | | | 0.001 29 | **| | 0.024 30 | | | 0.002 31 | *| | 0.002 32 | |* | 0.009 33 | | | 0.003 Sum of Residuals 0 Sum of Squared Residuals 404836 Predicted Residual SS (PRESS) 604543 1 EXAMPLE 10. LOG-LINEAR REGRESSION: LUMBER DATA 5 16:27 Thursday, June 28, 2001 Plot of YRES*YPRED. Legend: A = 1 obs, B = 2 obs, etc. | | 300 + | | | | | A | 200 + A | A | A | | | | A 100 + | AAA A | R | A e | A s | A A A i | A d 0 + AA u | a | AA A A l | A A A | A | A | -100 + A A | A | | A | A | | -200 + A | | | | | A | -300 + | ---+-----------+-----------+-----------+-----------+-- -500 0 500 1000 1500 Predicted Value of Y 1 EXAMPLE 10. LOG-LINEAR REGRESSION: LUMBER DATA 6 16:27 Thursday, June 28, 2001 Plot of YRES*X1. Legend: A = 1 obs, B = 2 obs, etc. | | 300 + | | | | | A | 200 + A | A | A | | | | A 100 + | A AA A | R | A e | A s | A A A i | A d 0 + AA u | a | AAA A l | A A A | A | A | -100 + A A | A | | A | A | | -200 + A | | | | | A | -300 + | --+---------+---------+---------+---------+---------+---------+-- 2 4 6 8 10 12 14 X1 1 EXAMPLE 10. LOG-LINEAR REGRESSION: LUMBER DATA 7 16:27 Thursday, June 28, 2001 Plot of YRES*X2. Legend: A = 1 obs, B = 2 obs, etc. | | 300 + | | | | | A | 200 + A | A | A | | | | A 100 + | A A A A | R | A e | A s | A A A i | A d 0 + A A u | a | A A A A l | A A A | A | A | -100 + A A | A | | A | A | | -200 + A | | | | | A | -300 + | --+-----------+-----------+-----------+-----------+-----------+-- 30 40 50 60 70 80 X2 1 EXAMPLE 10. LOG-LINEAR REGRESSION: LUMBER DATA 8 16:27 Thursday, June 28, 2001 Plot of YRES*X3. Legend: A = 1 obs, B = 2 obs, etc. | | 300 + | | | | | A | 200 + A | A | A | | | | A 100 + | B A A | R | A e | A s | A A A i | A d 0 + A A u | a | A A B l | A A A | A | A | -100 + A A | A | | A | A | | -200 + A | | | | | A | -300 + | ---+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+-- 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 X3 1 EXAMPLE 10. LOG-LINEAR REGRESSION: LUMBER DATA 9 16:27 Thursday, June 28, 2001 Plot of YRES*X4. Legend: A = 1 obs, B = 2 obs, etc. | | 300 + | | | | | A | 200 + A | A | A | | | | A 100 + | AA AA | R | A e | A s | A A A i | A d 0 + AA u | a | AA AA l | A A A | A | A | -100 + A A | A | | A | A | | -200 + A | | | | | A | -300 + | ---+-----------+-----------+-----------+-----------+-- 0.35 0.40 0.45 0.50 0.55 X4 1 EXAMPLE 10. LOG-LINEAR REGRESSION: LUMBER DATA 10 16:27 Thursday, June 28, 2001 The UNIVARIATE Procedure Variable: YRES (Residual) Moments N 33 Sum Weights 33 Mean 0 Sum Observations 0 Std Deviation 112.477286 Variance 12651.1399 Skewness -0.0922857 Kurtosis 0.1982749 Uncorrected SS 404836.475 Corrected SS 404836.475 Coeff Variation . Std Error Mean 19.5797823 Basic Statistical Measures Location Variability Mean 0.00000 Std Deviation 112.47729 Median -5.52259 Variance 12651 Mode . Range 498.34418 Interquartile Range 133.35181 Tests for Location: Mu0=0 Test -Statistic- -----p Value------ Student's t t 0 Pr > |t| 1.0000 Sign M -0.5 Pr >= |M| 1.0000 Signed Rank S -4.5 Pr >= |S| 0.9374 Tests for Normality Test --Statistic--- -----p Value------ Shapiro-Wilk W 0.986686 Pr < W 0.9495 Kolmogorov-Smirnov D 0.076728 Pr > D >0.1500 Cramer-von Mises W-Sq 0.030132 Pr > W-Sq >0.2500 Anderson-Darling A-Sq 0.193777 Pr > A-Sq >0.2500 Quantiles (Definition 5) Quantile Estimate 100% Max 226.68965 99% 226.68965 95% 202.42061 90% 165.34906 75% Q3 80.47950 50% Median -5.52259 25% Q1 -52.87231 10% -136.26947 5% -201.61626 1% -271.65453 0% Min -271.65453 1 EXAMPLE 10. LOG-LINEAR REGRESSION: LUMBER DATA 11 16:27 Thursday, June 28, 2001 The UNIVARIATE Procedure Variable: YRES (Residual) Extreme Observations ------Lowest----- -----Highest----- Value Obs Value Obs -271.655 19 116.507 7 -201.616 15 165.349 24 -151.407 20 179.282 17 -136.269 5 202.421 6 -119.792 29 226.690 9 Frequency Counts Percents Percents Value Count Cell Cum Value Count Cell Cum -271.65453382 1 3.0 3.0 2.79472042 1 3.0 54.5 -201.61625634 1 3.0 6.1 16.56887713 1 3.0 57.6 -151.40703741 1 3.0 9.1 24.13829387 1 3.0 60.6 -136.26947128 1 3.0 12.1 33.47486405 1 3.0 63.6 -119.79227665 1 3.0 15.2 35.70532958 1 3.0 66.7 -94.99590078 1 3.0 18.2 38.73680977 1 3.0 69.7 -94.12444768 1 3.0 21.2 57.18859050 1 3.0 72.7 -65.52560594 1 3.0 24.2 80.47950190 1 3.0 75.8 -52.87230716 1 3.0 27.3 83.15175046 1 3.0 78.8 -45.24933029 1 3.0 30.3 84.13634714 1 3.0 81.8 -41.05282153 1 3.0 33.3 84.58166892 1 3.0 84.8 -38.86006698 1 3.0 36.4 116.50669401 1 3.0 87.9 -35.04659802 1 3.0 39.4 165.34905567 1 3.0 90.9 -28.14905116 1 3.0 42.4 179.28166641 1 3.0 93.9 -25.75718220 1 3.0 45.5 202.42061447 1 3.0 97.0 -23.30895491 1 3.0 48.5 226.68965048 1 3.0 100.0 -5.52259264 1 3.0 51.5 1 EXAMPLE 10. LOG-LINEAR REGRESSION: LUMBER DATA 12 16:27 Thursday, June 28, 2001 The UNIVARIATE Procedure Variable: YRES (Residual) Stem Leaf # Boxplot 2 03 2 | 1 78 2 | 1 2 1 | 0 68888 5 +-----+ 0 022344 6 | + | -0 4443321 7 *-----* -0 99755 5 +-----+ -1 42 2 | -1 5 1 | -2 0 1 | -2 7 1 0 ----+----+----+----+ Multiply Stem.Leaf by 10**+2 Normal Probability Plot 225+ *+++*+ | * *+++ | ++*++ | +*** * | +***** -25+ ******* | * ***+ | +**++ | +++* | +++++* -275++++ * +----+----+----+----+----+----+----+----+----+----+ -2 -1 0 +1 +2 1 EXAMPLE 10. LOG-LINEAR REGRESSION: LUMBER DATA 13 16:27 Thursday, June 28, 2001 The REG Procedure Model: MODEL1 Dependent Variable: LNY Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model 4 26.30364 6.57591 431.16 <.0001 Error 28 0.42704 0.01525 Corrected Total 32 26.73068 Root MSE 0.12350 R-Square 0.9840 Dependent Mean 5.65054 Adj R-Sq 0.9817 Coeff Var 2.18558 Parameter Estimates Parameter Standard Variable DF Estimate Error t Value Pr > |t| Type I SS Intercept 1 -2.31855 0.70974 -3.27 0.0029 1053.64272 LNX1 1 2.11331 0.12763 16.56 <.0001 25.68330 LNX2 1 1.19705 0.20345 5.88 <.0001 0.55820 LNX3 1 -0.20327 0.10454 -1.94 0.0619 0.06213 LNX4 1 0.00740 0.30016 0.02 0.9805 0.00000926 Parameter Estimates Variable DF Type II SS Intercept 1 0.16276 LNX1 1 4.18154 LNX2 1 0.52796 LNX3 1 0.05767 LNX4 1 0.00000926 1 EXAMPLE 10. LOG-LINEAR REGRESSION: LUMBER DATA 14 16:27 Thursday, June 28, 2001 The REG Procedure Model: MODEL1 Dependent Variable: LNY Output Statistics Dep Var Predicted Std Error Std Error Student Obs LNY Value Mean Predict Residual Residual Residual 1 5.1591 5.1061 0.0642 0.0529 0.106 0.501 2 6.6134 6.5721 0.0500 0.0413 0.113 0.366 3 6.7020 6.6575 0.0497 0.0445 0.113 0.393 4 6.0113 5.9868 0.0247 0.0245 0.121 0.202 5 5.4205 5.3469 0.0688 0.0736 0.103 0.718 6 7.4236 7.3786 0.0660 0.0449 0.104 0.431 7 7.3072 7.2794 0.0558 0.0278 0.110 0.253 8 4.7958 4.6702 0.0446 0.1256 0.115 1.091 9 4.0604 3.9207 0.0502 0.1398 0.113 1.239 10 5.6276 5.5376 0.0330 0.0900 0.119 0.756 11 5.3936 5.3791 0.0311 0.0146 0.120 0.122 12 5.8348 5.6928 0.0317 0.1420 0.119 1.190 13 5.3423 5.1229 0.0499 0.2194 0.113 1.942 14 4.4308 4.4641 0.0724 -0.0333 0.100 -0.333 15 5.7462 5.8058 0.0348 -0.0596 0.118 -0.503 16 4.0943 4.2903 0.0760 -0.1960 0.0974 -2.013 17 7.4337 7.4271 0.0569 0.006592 0.110 0.0601 18 4.3041 4.6702 0.0439 -0.3662 0.115 -3.172 19 6.2442 6.4874 0.0426 -0.2433 0.116 -2.099 20 6.6412 6.7604 0.0367 -0.1192 0.118 -1.011 21 5.8435 5.8838 0.0308 -0.0403 0.120 -0.337 22 5.3471 5.3936 0.0410 -0.0465 0.116 -0.399 23 4.6052 4.6970 0.0510 -0.0919 0.112 -0.817 24 4.8040 4.8233 0.0524 -0.0193 0.112 -0.172 25 6.2897 6.3696 0.0336 -0.0798 0.119 -0.672 26 6.7032 6.7295 0.0376 -0.0263 0.118 -0.224 27 5.2679 5.1926 0.0430 0.0753 0.116 0.650 28 5.6348 5.5236 0.0500 0.1112 0.113 0.985 29 5.6904 5.6430 0.0366 0.0474 0.118 0.402 30 6.1356 6.0990 0.0440 0.0365 0.115 0.316 31 5.2983 5.3516 0.0241 -0.0533 0.121 -0.440 32 5.4337 5.3634 0.0449 0.0703 0.115 0.611 33 4.8283 4.8417 0.0549 -0.0134 0.111 -0.121 Output Statistics Cook's Obs -2-1 0 1 2 D 1 | |* | 0.019 2 | | | 0.005 3 | | | 0.006 4 | | | 0.000 5 | |* | 0.047 6 | | | 0.015 7 | | | 0.003 8 | |** | 0.036 9 | |** | 0.061 1 EXAMPLE 10. LOG-LINEAR REGRESSION: LUMBER DATA 15 16:27 Thursday, June 28, 2001 The REG Procedure Model: MODEL1 Dependent Variable: LNY Output Statistics Cook's Obs -2-1 0 1 2 D 10 | |* | 0.009 11 | | | 0.000 12 | |** | 0.020 13 | |*** | 0.147 14 | | | 0.012 15 | *| | 0.004 16 | ****| | 0.494 17 | | | 0.000 18 |******| | 0.291 19 | ****| | 0.119 20 | **| | 0.020 21 | | | 0.001 22 | | | 0.004 23 | *| | 0.027 24 | | | 0.001 25 | *| | 0.007 26 | | | 0.001 27 | |* | 0.012 28 | |* | 0.038 29 | | | 0.003 30 | | | 0.003 31 | | | 0.002 32 | |* | 0.011 33 | | | 0.001 Sum of Residuals 0 Sum of Squared Residuals 0.42704 Predicted Residual SS (PRESS) 0.61846 1 EXAMPLE 10. LOG-LINEAR REGRESSION: LUMBER DATA 16 16:27 Thursday, June 28, 2001 Plot of YRES*YPRED. Legend: A = 1 obs, B = 2 obs, etc. | | 0.3 + | | | | | A 0.2 + | | | A | A A | A 0.1 + | A A | B | A A A A | A A A R | A A e 0.0 + A s | B i | A A A d | AA u | A a | A l -0.1 + A | A | | | | -0.2 + A | | | A | | -0.3 + | | | | A | -0.4 + | --+-----------+-----------+-----------+-----------+-----------+-- 3 4 5 6 7 8 Predicted Value of LNY 1 EXAMPLE 10. LOG-LINEAR REGRESSION: LUMBER DATA 17 16:27 Thursday, June 28, 2001 Plot of YRES*LNX1. Legend: A = 1 obs, B = 2 obs, etc. | | 0.3 + | | | | | A 0.2 + | | | A | A A | A 0.1 + | A A | A A | A A A A | A A A R | A A e 0.0 + A s | A A i | A A A d | B u | A a | A l -0.1 + A | A | | | | -0.2 + A | | | A | | -0.3 + | | | | A | -0.4 + | --+-----------+-----------+-----------+-----------+-----------+-- 1.25 1.50 1.75 2.00 2.25 2.50 LNX1 1 EXAMPLE 10. LOG-LINEAR REGRESSION: LUMBER DATA 18 16:27 Thursday, June 28, 2001 Plot of YRES*LNX2. Legend: A = 1 obs, B = 2 obs, etc. | | 0.3 + | | | | | A 0.2 + | | | A | A A | A 0.1 + | A A | A A | A A A A | A A A R | A A e 0.0 + A s | A A i | A A A d | A A u | A a | A l -0.1 + A | A | | | | -0.2 + A | | | A | | -0.3 + | | | | A | -0.4 + | --+-----------+-----------+-----------+-----------+-----------+-- 3.4 3.6 3.8 4.0 4.2 4.4 LNX2 1 EXAMPLE 10. LOG-LINEAR REGRESSION: LUMBER DATA 19 16:27 Thursday, June 28, 2001 Plot of YRES*LNX3. Legend: A = 1 obs, B = 2 obs, etc. | | 0.3 + | | | | | A 0.2 + | | | A | A A | A 0.1 + | A A | A A | A A A A | A A A R | A A e 0.0 + A s | A A i | A A A d | A A u | A a | A l -0.1 + A | A | | | | -0.2 + A | | | A | | -0.3 + | | | | A | -0.4 + | --+---------+---------+---------+---------+---------+---------+-- 2.2 2.4 2.6 2.8 3.0 3.2 3.4 LNX3 1 EXAMPLE 10. LOG-LINEAR REGRESSION: LUMBER DATA 20 16:27 Thursday, June 28, 2001 Plot of YRES*LNX4. Legend: A = 1 obs, B = 2 obs, etc. | | 0.3 + | | | | | A 0.2 + | | | A | A A | A 0.1 + | A A | A A | A A A A | A AA R | A A e 0.0 + A s | A A i | A A A d | A A u | A a | A l -0.1 + A | A | | | | -0.2 + A | | | A | | -0.3 + | | | | A | -0.4 + | --+-----------+-----------+-----------+-----------+-----------+-- -1.1 -1.0 -0.9 -0.8 -0.7 -0.6 LNX4 1 EXAMPLE 10. LOG-LINEAR REGRESSION: LUMBER DATA 21 16:27 Thursday, June 28, 2001 The UNIVARIATE Procedure Variable: YRES (Residual) Moments N 33 Sum Weights 33 Mean 0 Sum Observations 0 Std Deviation 0.11552084 Variance 0.01334507 Skewness -1.1345633 Kurtosis 2.40938387 Uncorrected SS 0.42704209 Corrected SS 0.42704209 Coeff Variation . Std Error Mean 0.0201096 Basic Statistical Measures Location Variability Mean 0.000000 Std Deviation 0.11552 Median 0.024470 Variance 0.01335 Mode . Range 0.58560 Interquartile Range 0.11679 Tests for Location: Mu0=0 Test -Statistic- -----p Value------ Student's t t 0 Pr > |t| 1.0000 Sign M 2.5 Pr >= |M| 0.4869 Signed Rank S 34.5 Pr >= |S| 0.5458 Tests for Normality Test --Statistic--- -----p Value------ Shapiro-Wilk W 0.929139 Pr < W 0.0331 Kolmogorov-Smirnov D 0.12128 Pr > D >0.1500 Cramer-von Mises W-Sq 0.114496 Pr > W-Sq 0.0722 Anderson-Darling A-Sq 0.727768 Pr > A-Sq 0.0526 Quantiles (Definition 5) Quantile Estimate 100% Max 0.2194335 99% 0.2194335 95% 0.1420131 90% 0.1256005 75% Q3 0.0703095 50% Median 0.0244704 25% Q1 -0.0464807 10% -0.1191835 5% -0.2432753 1% -0.3661637 0% Min -0.3661637 1 EXAMPLE 10. LOG-LINEAR REGRESSION: LUMBER DATA 22 16:27 Thursday, June 28, 2001 The UNIVARIATE Procedure Variable: YRES (Residual) Extreme Observations -------Lowest------ ------Highest----- Value Obs Value Obs -0.3661637 18 0.111225 28 -0.2432753 19 0.125601 8 -0.1959865 16 0.139766 9 -0.1191835 20 0.142013 12 -0.0918646 23 0.219434 13 Frequency Counts Percents Percents Value Count Cell Cum Value Count Cell Cum -0.36616372800 1 3.0 3.0 0.02783874937 1 3.0 54.5 -0.24327527629 1 3.0 6.1 0.03652521203 1 3.0 57.6 -0.19598651408 1 3.0 9.1 0.04126789143 1 3.0 60.6 -0.11918345425 1 3.0 12.1 0.04447468627 1 3.0 63.6 -0.09186460308 1 3.0 15.2 0.04493282574 1 3.0 66.7 -0.07984770137 1 3.0 18.2 0.04736028310 1 3.0 69.7 -0.05955218465 1 3.0 21.2 0.05291762072 1 3.0 72.7 -0.05329102669 1 3.0 24.2 0.07030945213 1 3.0 75.8 -0.04648073913 1 3.0 27.3 0.07363510916 1 3.0 78.8 -0.04025870908 1 3.0 30.3 0.07526455251 1 3.0 81.8 -0.03330274473 1 3.0 33.3 0.08998581265 1 3.0 84.8 -0.02631050417 1 3.0 36.4 0.11122498657 1 3.0 87.9 -0.01926139556 1 3.0 39.4 0.12560054967 1 3.0 90.9 -0.01338843696 1 3.0 42.4 0.13976588743 1 3.0 93.9 0.00659193889 1 3.0 45.5 0.14201312082 1 3.0 97.0 0.01455435765 1 3.0 48.5 0.21943354630 1 3.0 100.0 0.02447043563 1 3.0 51.5 1 EXAMPLE 10. LOG-LINEAR REGRESSION: LUMBER DATA 23 16:27 Thursday, June 28, 2001 The UNIVARIATE Procedure Variable: YRES (Residual) Stem Leaf # Boxplot 2 2 1 | 1 | 1 1344 4 | 0 557789 6 +-----+ 0 11234444 8 *--+--* -0 43321 5 | | -0 98655 5 +-----+ -1 2 1 | -1 | -2 40 2 0 -2 -3 -3 7 1 0 ----+----+----+----+ Multiply Stem.Leaf by 10**-1 Normal Probability Plot 0.225+ +++* | +++++ | ++** * * | +*** * | ******** | *****+ -0.075+ ** **++ | *+++ | +++*+ | ++++* |++++ | -0.375+ * +----+----+----+----+----+----+----+----+----+----+ -2 -1 0 +1 +2