Questions

Run a multiple regression with profit as the dependent variable and the other variables that you created (firm size, capital intensity, etc.) as controls. Make sure to omit one industry dummy as the reference category. (Hint: Your regression coefficients for the industries will vary depending on which one you leave out as the reference category. Do not try to compare your answer to others' if you are not using the same reference category.)

• Interpret your results. Which variables have a (statistically) significant effect on profit (that is for which variables is the p-value less than 0.05)?
• Do your coefficients have the expected sign? That is, do your results show a negative effect for a variable on profit that you expected to be positive or vice versa?

**note, I've already ran the regression analysis, just need help interpreting the results (bullet points).

A B C D E F G H SUMMARY OUTPUT N P w Regression Statistics 4 Multiple R 0.561759362 5 R Square 0.31557358 6 Adjusted R Square 0.232418969 7 Standard Error 28.10792784 8 Observations 121 9 10 ANOVA 11 of SS MS F Significance F 12 Regression 13 38977.61928 2998.278406 3.795021992 5.2862E-05 13 Residual 107 84535.95002 790.0556077 14 Total 120 123513.5693 15 16 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% 17 Intercept 27.68664776 23.56421727 1.174944512 0.242624975 -19.02666469 74.39996021 -19.02666469 74.39996021 18 Firm Size 0.76385414 2.175549079 0.351108669 0.726197208 -3.548918175 5.076626456 -3.548918175 5.076626456 19 Capital Intensity -0.377553556 0.079481697 -4.750194926 6.34766E-06 -0.535116751 -0.219990361 -0.535116751 -0.219990361 20 Ad Intensity 0.323006534 0.693273398 0.465915085 0.642223324 -1.05132712 1.697340188 -1.05132712 1.697340188 21 Bad debt 1.830684634 0.712610271 2.568984348 0.01157754 0.418017882 3.243351385 0.418017882 3.243351385 22 Inventory mgt 0.185241369 0.285023095 0.649917051 0.517138698 -0.379783674 0.750266412 -0.379783674 0.750266412 23 energy -57.93526645 29.73250508 -1.948549787 0.053967932 -116.8764901 1.005957181 -116.8764901 1.005957181 24 materials 12.00384749 22.22851584 0.540020197 0.590305277 -32.06159265 56.06928764 -32.06159265 56.06928764 25 industrials -6.955785897 12.4561182 -0.558423241 0.577722098 -31.64858748 17.73701569 -31.64858748 17.73701569 26 cons discr -4.208262844 11.08244311 -0.379723388 0.704903377 -26.17791377 17.76138808 -26.17791377 17.76138808 27 cons staples 20.09799598 12.10513999 1.660286126 0.099784785 -3.899032229 44.09502418 -3.899032229 44.09502418 28 healthcare 9.541237125 11.94616583 0.798686144 0.426241656 -14.14064335 33.22311761 -14.14064335 33.22311761 29 financials 31.37422604 23.05556478 1.36080926 0.176433374 -14.33074217 77.07919425 -14.33074217 77.07919425 30 info tech 20.7975231 12.46466007 1.668519076 0.098136476 -3.912211736 45.50725794 -3.912211736 45.50725794