(How can I solve this problem): I have two responses y1, y2 and 8 independent variables. Im trying to run multiple linear regression on the data using proc reg either on y1 or y2: And I got the following notes: PROC IMPORT DATAFILE="/folders/myfolders/sasuser.v94/ENB2012_data.xlsx" OUT=energy DBMS=XLSX REPLACE; RUN; PROC PRINT DATA=energy; RUN; proc reg data=energy ; model y1= x1 x2 x3 x4 x5 x6 x7 x8 / vif collin; run; The REG Procedure Model: MODEL1 Dependent Variable: Y1 Y1 Number of Observations Read 1296 Number of Observations Used 768 Number of Observations with Missing Values 528 Analysis of Variance Source DF Sum of Squares Mean Square F Value Pr > F Model 7 71546 10221 1187.06 <.0001 Error 760 6543.77041 8.61022 Corrected Total 767 78090 Root MSE 2.93432 R-Square 0.9162 Dependent Mean 22.30720 Adj R-Sq 0.9154 Coeff Var 13.15413 Note:Model is not full rank. Least-squares solutions for the parameters are not unique. Some statistics will be misleading. A reported DF of 0 or B means that the estimate is biased. Note:The following parameters have been set to 0, since the variables are a linear combination of other variables as shown. X4 = 2.97E-8 * Intercept - 1.63E-8 * X1 + 0.5 * X2 - 0.5 * X3 + 342E-12 * X5 Parameter Estimates Variable Label DF Parameter Estimate Standard Error t Value Pr > |t| Variance Inflation Intercept Intercept B 84.01342 19.03361 4.41 <.0001 0 X1 X1 B -64.77343 10.28945 -6.30 <.0001 105.52405 X2 X2 B -0.08729 0.01708 -5.11 <.0001 201.53113 X3 X3 B 0.06081 0.00665 9.15 <.0001 7.49298 X4 X4 0 0 . . . . X5 X5 B 4.16995 0.33799 12.34 <.0001 31.20547 X6 X6 1 -0.02333 0.09470 -0.25 0.8055 1.00000 X7 X7 1 19.93274 0.81399 24.49 <.0001 1.04751 X8 X8 1 0.20378 0.06992 2.91 0.0037 1.0475 Also I tried to make dummy variables, since I have 2 categorical variables as follow and still the same: DATA energy1; SET energy; IF (x6 = 2) THEN d61 = 1; ELSE d61 = 0; IF (x6 = 3) THEN d62 = 1; ELSE d62 = 0; IF (x6 = 4) THEN d63 = 1; ELSE d63 = 0; IF (x6 = 5) THEN d64 = 1; ELSE d64 = 0; IF (x8 = 0) THEN d81 = 1; ELSE d81 = 0; IF (x8 = 1) THEN d82 = 1; ELSE d82 = 0; IF (x8 = 2) THEN d83 = 1; ELSE d83 = 0; IF (x8 = 3) THEN d84 = 1; ELSE d84 = 0; IF (x8 = 4) THEN d85 = 1; ELSE d85 = 0; IF (x8 = 5) THEN d86 = 1; ELSE d86 = 0; run; see the attached data or ( https://archive.ics.uci.edu/ml/machine-learning-databases/00242/ENB2012_data.xlsx )
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