6. 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=....

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.

sales	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	0.557631
2431.36	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	0.660739
1248.56	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