PropVal3 Example 1 The SURVEYSELECT Procedure Selection Method Simple Random Sampling Input Data Set PROPVAL Random Number Seed 49143 Sampling Rate 0.5 Sample Size 11 Selection Probability 0.5 Sampling Weight 2 Output Data Set SUBSETS PropVal3 Example 2 subsets dataset Obs Selected y x1 x2 x3 x4 x5 x6 x7 x8 x9 1 1 25.9 4.9176 1.0 3.4720 0.998 1.0 7 4 42 0 2 0 29.5 5.0208 1.0 3.5310 1.500 2.0 7 4 62 0 3 0 27.9 4.5429 1.0 2.2750 1.175 1.0 6 3 40 0 4 1 25.9 4.5573 1.0 4.0500 1.232 1.0 6 3 54 0 5 0 29.9 5.0597 1.0 4.4550 0.988 1.0 6 3 56 0 6 1 30.9 5.8980 1.0 5.8500 1.240 1.0 7 3 51 1 7 0 28.9 5.6039 1.0 9.5200 1.501 0.0 6 3 32 0 8 1 35.9 5.8282 1.0 6.4350 1.225 2.0 6 3 32 0 9 0 31.5 5.3003 1.0 4.9883 1.552 1.0 6 3 30 0 10 1 31.0 6.2712 1.0 5.5200 0.975 1.0 5 2 30 0 11 0 30.0 5.0500 1.0 5.0000 1.020 0.0 5 2 46 1 12 1 36.9 8.2464 1.5 5.1500 1.664 2.0 8 4 50 0 13 0 41.9 6.6969 1.5 6.9020 1.488 1.5 7 3 22 1 14 1 40.5 7.7841 1.5 7.1020 1.376 1.0 6 3 17 0 15 0 43.9 9.0384 1.0 7.8000 1.500 1.5 7 3 23 0 16 0 37.5 5.9894 1.0 5.5200 1.256 2.0 6 3 40 1 17 1 37.9 7.5422 1.5 5.0000 1.690 1.0 6 3 22 0 18 1 44.5 8.7951 1.5 9.8900 1.890 2.0 8 4 50 1 19 0 37.9 6.0831 1.5 6.7265 1.652 1.0 6 3 44 0 20 1 38.9 8.3607 1.5 9.1500 1.777 2.0 8 4 48 1 21 1 36.9 8.1400 1.0 8.0000 1.504 2.0 7 3 3 0 22 0 45.8 9.1416 1.5 7.3262 1.831 1.5 8 4 31 0 PropVal3 Example 3 newpropval dataset Obs Selected y x2 x3 x5 x8 ynew 1 1 25.9 1.0 3.4720 1.0 42 . 2 0 . 1.0 3.5310 2.0 62 29.5 3 0 . 1.0 2.2750 1.0 40 27.9 4 1 25.9 1.0 4.0500 1.0 54 . 5 0 . 1.0 4.4550 1.0 56 29.9 6 1 30.9 1.0 5.8500 1.0 51 . 7 0 . 1.0 9.5200 0.0 32 28.9 8 1 35.9 1.0 6.4350 2.0 32 . 9 0 . 1.0 4.9883 1.0 30 31.5 10 1 31.0 1.0 5.5200 1.0 30 . 11 0 . 1.0 5.0000 0.0 46 30.0 12 1 36.9 1.5 5.1500 2.0 50 . 13 0 . 1.5 6.9020 1.5 22 41.9 14 1 40.5 1.5 7.1020 1.0 17 . 15 0 . 1.0 7.8000 1.5 23 43.9 16 0 . 1.0 5.5200 2.0 40 37.5 17 1 37.9 1.5 5.0000 1.0 22 . 18 1 44.5 1.5 9.8900 2.0 50 . 19 0 . 1.5 6.7265 1.0 44 37.9 20 1 38.9 1.5 9.1500 2.0 48 . 21 1 36.9 1.0 8.0000 2.0 3 . 22 0 . 1.5 7.3262 1.5 31 45.8 PropVal3 Example 4 newpropval dataset The REG Procedure Model: MODEL1 Dependent Variable: y Sale price of house ($1000) Number of Observations Read 22 Number of Observations Used 11 Number of Observations with Missing Values 11 Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model 4 325.94394 81.48598 19.85 0.0013 Error 6 24.63243 4.10540 Corrected Total 10 350.57636 Root MSE 2.02618 R-Square 0.9297 Dependent Mean 35.01818 Adj R-Sq 0.8829 Coeff Var 5.78608 Parameter Estimates Parameter Standard Variable Label DF Estimate Error Intercept Intercept 1 12.28667 3.47670 x2 Number of baths 1 12.32734 2.75759 x3 Lot size (1000 sq ft) 1 1.30471 0.45958 x5 Number of garage stalls 1 1.67154 1.64505 x8 Age of home (years) 1 -0.08509 0.03929 Parameter Estimates Variable Label DF t Value Pr > |t| Intercept Intercept 1 3.53 0.0123 x2 Number of baths 1 4.47 0.0042 x3 Lot size (1000 sq ft) 1 2.84 0.0296 x5 Number of garage stalls 1 1.02 0.3488 x8 Age of home (years) 1 -2.17 0.0735 PropVal3 Example 5 out2 dataset Obs Selected y x2 x3 x5 x8 ynew predict resid matchcol 1 1 25.9 1.0 3.4720 1.0 42 . 27.2418 -1.34183 1 2 0 . 1.0 3.5310 2.0 62 29.5 27.2886 . 1 3 0 . 1.0 2.2750 1.0 40 27.9 25.8503 . 1 4 1 25.9 1.0 4.0500 1.0 54 . 26.9749 -1.07491 1 5 0 . 1.0 4.4550 1.0 56 29.9 27.3331 . 1 6 1 30.9 1.0 5.8500 1.0 51 . 29.5787 1.32135 1 7 0 . 1.0 9.5200 0.0 32 28.9 34.3121 . 1 8 1 35.9 1.0 6.4350 2.0 32 . 33.6301 2.26988 1 9 0 . 1.0 4.9883 1.0 30 31.5 30.2412 . 1 10 1 31.0 1.0 5.5200 1.0 30 . 30.9349 0.06506 1 11 0 . 1.0 5.0000 0.0 46 30.0 27.2235 . 1 12 1 36.9 1.5 5.1500 2.0 50 . 36.5857 0.31435 1 13 0 . 1.5 6.9020 1.5 22 41.9 40.4182 . 1 14 1 40.5 1.5 7.1020 1.0 17 . 40.2688 0.23119 1 15 0 . 1.0 7.8000 1.5 23 43.9 35.3411 . 1 16 0 . 1.0 5.5200 2.0 40 37.5 31.7556 . 1 17 1 37.9 1.5 5.0000 1.0 22 . 37.1009 0.79914 1 18 1 44.5 1.5 9.8900 2.0 50 . 42.7700 1.73000 1 19 0 . 1.5 6.7265 1.0 44 37.9 37.4815 . 1 20 1 38.9 1.5 9.1500 2.0 48 . 41.9747 -3.07468 1 21 1 36.9 1.0 8.0000 2.0 3 . 38.1395 -1.23954 1 22 0 . 1.5 7.3262 1.5 31 45.8 40.2059 . 1 PropVal3 Example 6 out2 dataset The MEANS Procedure Analysis Variable : y Sale price of house ($1000) N Mean ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ 11 35.0181818 ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ PropVal3 Example 7 out2 dataset Obs _TYPE_ _FREQ_ ybar matchcol 1 0 22 35.0182 1 PropVal3 Example 8 sse dataset Obs ynew predict ybar r2 d2 1 29.5 27.2886 35.0182 4.8903 30.450 2 27.9 25.8503 35.0182 4.2014 50.669 3 29.9 27.3331 35.0182 6.5888 26.196 4 28.9 34.3121 35.0182 29.2906 37.432 5 31.5 30.2412 35.0182 1.5845 12.378 6 30.0 27.2235 35.0182 7.7087 25.182 7 41.9 40.4182 35.0182 2.1957 47.359 8 43.9 35.3411 35.0182 73.2552 78.887 9 37.5 31.7556 35.0182 32.9980 6.159 10 37.9 37.4815 35.0182 0.1751 8.305 11 45.8 40.2059 35.0182 31.2943 116.248 PropVal3 Example 9 Compute SSE The MEANS Procedure Variable Sum ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ r2 194.1827272 d2 439.2645455 ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ PropVal3 Example 10 Compute SSE Obs _TYPE_ _FREQ_ r2_Sum d2_Sum 1 0 11 194.183 439.265 PropVal3 Example 11 Compute SSE Obs r2predict 1 0.55794