I understand the regression equation to predict something( sales in this case) but what do I use in the equation when I am using lagged variables?

Regression Statistics Multiple R 0.998852878 R Square 0.997707072 Adjusted R Square 0.997577632 TOTALNSA-12 Residual Plot D-Factor Residual Plot Standard Error 10.95141499 50 Observations 132 50 ANOVA Residual 9.000 500.00 1000 060 1:60.006 2000.000 0 MS Significance F Regression 6471050 924435.7 7707.902591 2 1445E-160 100 Residual 124 14871.75 119.9335 TOTALNSA-12 100 Total 131 6485921 D-Factor Coefficients andard Em + Stat P-voice Lower 95% Upper 95%lower 95 0Nipper 95.0% Intercept 348.5063218 62.31368 5.592774 1 35738-07 225.1701025 471.8425 225.1701 471.8425 TOTALNSA-12 0.721898195 0.048869 14.77224 4.12473E-29 0.625173641 0.818623 0.625174 0.818623 DSALES Residual Plot D-Sales -12 Residual Plot D-Factor -312. 7824521 62.18857 -5.02958 1 68019E-06 435,8710719 -189.694 -435.871 -189.694 50 50 DSALES 0.930128779 0.012117 76.76147 2 1439E-106 0.906145584 0.954112 0.906146 0.954112 D-Sales -12 0.690194538 0.047624 -14.4924 1 86408E-28 -0.78445665 -0.59593 -0.78446 -0.59593 0 Domestic Auto Prod. 0.174922528 0.066447 -2.6325 0.009553487 0.306440564 -0.0434 -0 30644 -0.0434 Residuals 0.000 5co.080 # 1060 080 1500.060 2000.000 Residual 0.000 500.800 1080.80 1520.000 2000.800 2500.000 Domestic Auto Prod x Total Sale 0.000145728 5.16-05 2.856596 0.005022727 4.4755BE-05 0.000247 4.4BE-05 0.000247 50 50 Crude Oil 0.08590267 0.04014 2.140065 0.03430608 0.006453944 0.165351 0.006454 0.165351 -190 -100 DSALES D-Sales -12 RESIDUAL OUTPUT Domestic Auto Prod. Domestic Auto Prod x Total Crude Oil Residual Plot Observation Predicted TOTALNSA Residuals Residual Plot Sales Residual Plot 1254.770876 1,762124 50 1588.079712 -7.68671 30 50 1311 00356 2.76444 0 0 Residual esidual Residuals 1275.411636 -2.41264 100. 200.0 300.0 400.0 500.0 400000 600000 800000 1080.750902 -17.3429 -100 839.6656372 35.18236 Domestic Auto Prod. .100 Domestic Auto Prod x Total Sales Crude Oil 1338.654207 4.344793 1492 496008 -2.69201 1094.683115 -16.8141 1262 994552 6.118448 1534.828546 -3.71455 1469.617239 -1.90924 1482 18589 -0.97589 1084.614263 1449737 1327.624705 1.130295 545.673366 -7.13937