1.The accompanying computer excel output (please see attachment on Blackboard) provides details of data and analysis on the topic of "firm sales".A manager wishes to study the relation between sales (Y), advertising spending (X1, in $), firm size (X2), and financial leverage (X3).

The output includes a listing of all 60 observations; and, the results of a regression analysis that uses firm sales as the dependent variable, and advertising, firm size, and financial leverage.

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=....

Regression formula y=bx+a

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.

Data provided below (i can also send the excel spreadsheet as well)

sales 1198 1460.19 1584.82 1654.46 1737.03 1750.51 1726.8 1676.91 1652.4 1608.91 3285.39 3707.49 3912.85 4151.29 4376.9 4235.22 3620.58 2858.5 2761.39 2826.92 293.62 328.579 727.334 1243.28 1263.72 1822.76 3580.83 3416.41 2431.36 2505.24 318.521 308.817 322.151 324.64 320.387 317.662 292.863 244.933 348.748 350.097 17140.5 19064.7 20460.2 21586.4 22786.6 23522.4 22744.7 24074.6 27006 27567 1041.36 1110.29 1306.24 1410.23 1360.3 1248.56 1194.8 1265.16 1325.84 1251.49

ad 42.295 46.69 40.788 41.807 40.775 45.708 45.648 49.311 51.266 45.384 135.2 151.3 126 146.1 149.6 133.6 113.2 80.6 80.2 80.4 16.115 16.6 43.5 78.6 79.27 110.849 182.008 169.704 106.658 103.147 1.02 4.046 6.209 6.676 5.8 3.8 4.6 3.1 2 2.4 709.8 722.6 771.4 787.2 718.3 703.4 650.8 687 768.6 787.5 1.7 11.9 13.1 20.7 19.64 19.92 19.4 15 12.4 20.1

size 2.93864 3.07335 3.08249 3.07808 3.08172 3.05981 3.04499 3.03914 3.02767 3.00597 3.28854 3.34388 3.33367 3.3467 3.36512 3.34106 3.2898 3.26767 3.1716 3.15718 3.01827 3.02815 3.44863 3.19325 3.16273 3.66704 3.69683 3.6751 3.63354 3.63379 2.44697 2.37066 2.31432 2.31545 2.3417 2.35511 2.28862 2.38443 2.35797 2.36364 4.40697 4.44463 4.47696 4.46275 4.46822 4.45426 4.48036 4.50481 4.51838 4.54884 2.97148 3.03103 3.06877 3.08985 3.10447 3.05084 3.02695 3.07446 3.0695 3.01836

leverage 0.274201 0.448616 0.417412 0.410816 0.492455 0.47919 0.424647 0.393131 0.383937 0.4268 0.413237 0.542254 0.489694 0.515779 0.652683 0.728657 0.668065 0.60653 0.704352 0.784222 0.609943 0.707946 0.268072 1.51911 0.736702 0.216276 0.454671 0.557631 0.597461 0.535555 0.528685 0.433116 0.300309 0.199235 0.188359 0.17028 0.160693 0.334163 0.276217 0.252613 0.530584 0.489843 0.494941 0.467392 0.480132 0.5298 0.535684 0.542327 0.5638 0.567813 0.448407 0.475617 0.550041 0.642783 0.660739 0.629632 0.494281 0.501516 0.508985 0.50456

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.984822851

R Square 0.969876048

Adjusted R Square 0.968262264

Standard Error 1430.876622

Observations 60

ANOVA

Regression

df 3

SS 3691447394

MS

1230482465

F 600.9952684

Significance F 1.53076E-42

Residual

df 56

SS 114654842.8

MS 2047407.907

Intercept

Betas 1504.507947

Standard Error 1921.548858

t Stat 0.782966273

P-value 0.436945613

Lower 95% -2344.816968

Upper 95% 5353.832862

Advertising

Betas 31.7153263

Standard Error 1.731192888

t Stat 18.3199264

P-value 2.55407E-25

Lower 95% 28.24733021

Upper 95% 35.18332238

Size

Betas -532.7043998

Standard Error 712.8094108

t Stat -0.747330762

P-value 0.457990546

Lower 95% -1960.633236

Upper 95% 895.2244367

Leverage

Betas 217.2876113

Standard Error 1036.300256

t Stat 0.209676308

P-value 0.834681271

Lower 95% -1858.671259

Upper 95% 2293.246481