(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 )
