4. Make your t-test for the "slope coefficients", that tells whether each of the coefficients bl, b2, b3, etc. is significantly different from zero. That is, you test the null hypothesis of whether bl=0, and then whether b2=0, and then whether b3=0, etc. separately. Each one is done separately. We usually use the 0.05 level of significance (equivalently the 95% level of confidence). 5. Finally you provide your interpretation in "if"' statements. Example from above equation in (1), "If X1 increases by one unit we expect Y to decreases by 4 units", and so on for the rest of the explanatory variables. 6. Make a test of the "predictability power" of your estimated equation. We can use a very "crude or primitive" test. Use the last observed values of X1, X2, X3, etc. in the estimated equation and calculate the estimated Y. Compare the estimated Y with the observed value of Y in your data set. If the difference between the estimated Y and the actually observed Y is equal to or less than 5% of the observed value, it is a good sign of the "predictability power" of your model.Regression Statistics Multiple R 0.994249006 R Square 0.988531085 Adjusted R Square 0.986892669 Standard Error 4.54963875 Observations 41 ANOVA df SS MS F Significance F Regression 5 62443.88026 12488.77605 603.3454605 6.61156E-33 Residual 35 724.4724464 20.69921275 Total 40 63168.3527 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 4.915635463 3.871536423 1.269685966 0.212573882 -2.944001325 12.77527225 -2.944001325 12.77527225 X1 0.011435097 0.00319386 3.58033714 0.001030675 0.004951216 0.017918978 0.004951216 0.017918978 X2 0.064555603 0.063810691 1.011673784 0.31863717 -0.064986986 0.194098191 -0.064986986 0.194098191 X3 0.0363394 0.010774634 3.372680748 0.001829666 0.01446573 0.058213069 0.01446573 0.058213069 X4 0.001894974 0.002773832 -0.683161205 0.499002606 -0.007526153 0.003736204 -0.007526153 0.003736204 X5 0.315394136 0.059812161 5.273077046 7.01853E-06 0.193968992 0.436819279 0.193968992 0.436819279 4.011901601 Equation : Y = a + b1 X1 + b2 X2 + b3 X3 + ....... + e