A manager wishes to study the relation between sales (Y), advertising spending (X1, in $), firm size (X2), and financial leverage (X3).

a.Write out the regression equation, with specific intercept and slope estimates.Remember that the regression is already estimated for you so you only need to write the equation of the estimated model. For example: Y=....

b.For the first row of actual data from the excel file, use the equation in (a) to "predict" the value of Y. For this row, also compute the "residual" (actual value minus the predicted value).Note: Only use the first raw of data and not the whole data set.

c.Evaluate the statistical significance of each of the three slope estimates. This can be done in a very summary way.Use a significance level of 0.05. Note: You can either use the p-value or t-statistics to determine whether the coefficients are significant or not.

d.Use the R-square and F-test to evaluate the overall reliability of the regression.

SUMMARY OUTPUT Regression Statistics Multiple R 0.984822851 R Square 0.969876048 Adjusted R Square 0.968262264 Standard Error 1430.876622 Observations 60 ANOVA df SS MS F Significance F Regression 3 3691447394 1230482465 600.9952684 1.53076E-42 Residual 56 114654842.8 2047407.907 Total 59 3806102237 Betas Standard Error t Stat P-value Lower 95% Upper 95% Intercept 1504.507947 1921.548858 0.782966273 0.436945613 -2344.816968 5353.832862 advertising 31.7153263 1.731192888 18.3199264 2.55407E-25 28.24733021 35.18332238 size -532.7043998 712.8094108 -0.747330762 0.457990546 -1960.633236 895.2244367 leverage 217.2876113 1036.300256 0.209676308 0.834681271 -1858.671259 2293.246481sales ad size leverage 1198 42.295 2.93864 0.274201 1460.19 46.69 3.07335 0.448616 1584.82 40.788 3.08249 0.417412 1654.46 41.807 3.07808 0.410816 1737.03 40.775 3.08172 0.492455 1750.51 45.708 3.05981 0.47919 1726.8 45.648 3.04499 0.424647 1676.91 49.311 3.03914 0.393131 1652.4 51.266 3.02767 0.383937 1608.91 45.384 3.00597 0.4268 3285.39 135.2 3.28854 0.413237 3707.49 151.3 3.34388 0.542254 3912.85 126 3.33367 0.489694 4151.29 146.1 3.3467 0.515779 4376.9 149.6 3.36512 0.652683 4235.22 133.6 3.34106 0.728657 3620.58 113.2 3.2898 0.668065 2858.5 80.6 3.26767 0.60653 2761.39 80.2 3.1716 0.704352 2826.92 80.4 3.15718 0.784222 293.62 16.115 3.01827 0.609943 328.579 16.6 3.02815 0.707946 727.334 43.5 3.44863 0.268072 1243.28 78.6 3.19325 1.51911 1263.72 79.27 3.16273 0.736702 1822.76 110.849 3.66704 0.216276 3580.83 182.008 3.69683 0.454671 3416.41 169.704 3.6751 0.5576312431.36 106.658 3.63354 0.597461 2505.24 103.147 3.63379 0.535555 318.521 1.02 2.44697 0.528685 308.817 4.046 2.37066 0.433116 322.151 6.209 2.31432 0.300309 324.64 6.676 2.31545 0.199235 320.387 5.8 2.3417 0.188359 317.662 3.8 2.35511 0.17028 292.863 4.6 2.28862 0.160693 244.933 3.1 2.38443 0.334163 348.748 2 2.35797 0.276217 350.097 2.4 2.36364 0.252613 17140.5 709.8 4.40697 0.530584 19064.7 722.6 4.44463 0.489843 20460.2 771.4 4.47696 0.494941 21586.4 787.2 4.46275 0.467392 22786.6 718.3 4.46822 0.480132 23522.4 703.4 4.45426 0.5298 22744.7 650.8 4.48036 0.535684 24074.6 687 4.50481 0.542327 27006 768.6 4.51838 0.5638 27567 787.5 4.54884 0.567813 1041.36 1.7 2.97148 0.448407 1110.29 11.9 3.03103 0.475617 1306.24 13.1 3.06877 0.550041 1410.23 20.7 3.08985 0.642783 1360.3 19.64 3.10447 0.6607391248.56 19.92 3.05084 0.629632 1194.8 19.4 3.02695 0.494281 1265.16 15 3.07446 0.501516 1325.84 12.4 3.0695 0.508985 1251.49 20.1 3.01836 0.50456