2.  Input parameters 1, 2, …, n  Values of each denoted X 1, X 2, X n  For each setting of X 1, X 2, X n observe a Y  Each set (X 1, X 2, X n,Y) is one observation  As we vary the X-values, Y changes in a linear (scaled proportional) manner  Some of the X’s don’t matter much, some are key

3.  Assumptions   is independent from sample to sample   is independent of the X’s   ~N(0,  2 )  So we will examine the “noise”

4.  Troops patrol their assigned area  Discover targets for destruction from the air  Call for CAS  May need an aircraft with laser- designation-capable weapons  May have a time deadline  Have a distance from the FARP to the target  Effects measured on 1..100 scale

7. Regression Statistics Multiple R0.985 R Square0.97 Adjusted R Square0.97 Standard Error4.744 Observations100 ANOVA dfSSMSF Significanc e F Regression171324 31692E-76 Residual98220622.51 Total9973530 Coefficient s Standard Errort StatP-valueLower 95%Upper 95% Intercept10.740.6616.281E-299.4312.05 X Variable 10.5510.0156.292E-760.5320.571 Y= 10.7 +.55 EXP Test for b = 0

8. } ERROR

10.  Look at all of the independent variables  Builds the complex multidimensional function in n-space

11. Regression Statistics Multiple R0.999985 R Square0.99997 Adjusted R Square0.999969 Standard Error0.157982 Observations100 ANOVA dfSSMSFSignificance F Regression379706.992656910645296.9E-217 Residual962.3960110.024958 Total9979709.39 CoefficientsStandard Errort StatP-valueLower 95%Upper 95% Intercept0.3927090.0413949.4872061.88E-150.3105430.474874 X Variable 10.8128930.03187225.505331.52E-440.7496290.876158 X Variable 20.1856550.000587316.32721.1E-1460.184490.18682 X Variable 30.5351990.0003031768.0232E-2180.5345980.5358 Y=.39 +.81 LAZ +.19 DIST +.54 EXP

12.  Residuals that depend on one of the X’s  Residuals that have different variance at different values of an X