Can you help me to do the regression analysis of this result, please?

BACKGROUND Data Frame: Response Variable: 06_1 Predictor Variable 1: 03_1 Predictor Variable 2: 03_2 Predictor Variable 3: 03 3 Number of cases (rows) of data: 27 Number of cases retained for analysis: 26 BASIC ANALYSIS 9.14 - Estimated Model for Q6_1 Estimate Sed Err t-value p-value Lower 95% Upper 95% (Intercept) 4.519 1.113 4.060 0.091 2. 211 6.828 40 55 70 LLLLLL 09 1 651 03 1 8. 092 . 228 9.402 9.691 0. 381 0.564 03 2 9. 112 9.150 9.742 9. 466 e.208 0.423 03 2 0 241 0. 128 1.982 0.873 -0.025 0.507 03 2 -0.11 -- Model Fit Standard deviation of 06_1: 1.050 LLLLLL 013 Standard deviation of residuals: 1.017 for 22 degrees of freedom 95% range of residual variation: 4.220 = 2 * (2.074 * 1.017) 70 -squared: 0.168 Adjusted R-squared: 0.055 PRESS R-squared: -0.155 Collinearity Tolerance VIF Q3_1 0.739 1. 352 Q3_ 3.738 1.355 03 3 0.987 1. 014 Null hypothesis of all @ population slope coefficients: F-statistic: 1.485 df: 3 and 22 p-value: 6.246 -- Best Subset Regression Models -- Analysis of Variance 03 1 Q3 2 03 3 adir2 X'S 1 3.089 Sum 5q Mean Sq F-value p-value Q3_1 0.549 0. 549 0.530 3.474 DUPO 0.073 NHHWNAN Q3_2 3. 39 3. 395 0. 382 3.543 3.055 03 3 3.668 3. 668 3.543 9.073 0.010 0.021 Mode] 3 4.611 1.537 1. 485 0. 246 0.049 Residuals 22 22.773 1. 035 06_1 27. 385 1. 095 [based on Thomas Lumley's leaps function from the leaps package] K-FOLD CROSS-VALIDATION RESIDUALS AND INFLUENCE RELATIONS AMONG THE VARIABLES Data, Fitted, Residual, Studentized Residual, Offits, Cook's Distance [sorted by Cook's Distance] [res_rows = 20, out of 26 rows of data, or do res_rows="all"] = = Correlation Matrix - - --- Q6_1 0 Q3 1 03 2 Q3_3 Q3_1 03_2 Q3 3 06_1 fitted resid rstant diffits cooks 26 1 1.00 0.14 6 0.18 0.34 27 4.000 5.000 3.000 4.000 6.167 -2.167 -2.564 -1.012 0.204 Q3 1 0.14 1.00 0.51 -0.09 22 5.000 2.000 7.000 6.000 6.890 -0.890 -1.118 -0. 879 0.191 Q3_2 0.18 0.51 1.00 -0.11 8 4.000 7.080 2.000 7.080 6.149 0.851 1.058 8 0.814 0.165 Q3_3 0.34 -0.09 -0.11 1.00 23 4.000 6.000 2.000 5.000 6.038 -1.038- -1.179 -0.660 0.107 24 4.006 3.000 2.060 4.000 5.703 -1.703 -1.862 -0.625 0.088