CompensationROA Return Assets
16.58 2.53 -0.15 20917.5
26.92 1.27 0.57 32659.5
11.83 0.91 1.23 64787.5
0.91 2.24 1.28 596
3.44 -1.93 -0.83 1997.5
10.25 0.04 1.21 10217.5
25.09 1.25 1.01 46029.5
8.12 2.01 1.74 2494
14.12 0.88 0.55 58083
2.47 0.49 0.58 6233.5
11.57 0.73 0.17 35439.5
7.61 0.76 0.01 2161
9.33 0.34 3.07 8555.5
15.38 2.46 5.45 3006.5
10.35 0.96 2.35 4309
6.63 0.85 1.79 3165
4.89 0.19 1.23 13736
23.98 0.74 0.99 156813
3.89 2.36 0.11 2030.5
33.99 0.8 1.19 106109.5
11.24 1.47 0.51 19407
3.24 0.47 0.49 18903
14.22 0.41 0.82 37079.5
29.14 0.87 0.91 121144.5
7.01 0.46 2.05 23958.5
21.23 0.48 0.3 916230.5
7.23 1.34 1.12 1956
24.08 1.64 0.43 31542.5
13.43 1.18 0.88 38776
9.94 1.66 0.14 16466
2.63 1.38 1.44 604.5
5.28 1.97 0.63 21790
5.63 -0.11 2 10154.5
6.04 0.52 2.16 12360.5
3.31 0.8 0.24 3405
2.7 0.31 0.79 21967.5
60.73 0.54 1.6 208133
4.51 0.54 0.43 8713
4.77 0.84 2.66 5524
5.28 1.12 0.52 4264.5
2.24 0 -2.08 1029.5
64.63 -0.14 -0.77 23882
13.32 2.11 -0.55 4999.5
8 3.02 1.84 8256.5
6.29 0.93 0.12 5101.5
2.46 0.36 0.88 11905
27.87 0.5 0.72 1375770
2.27 0.61 0.56 10379.524. 21. Estimate a model that regresses Compensation on all the explanatory variables in the dataset. Comment on joint significance of the slope coefficients.
22. Based on the regression result from Problem 21, comment where you saw any statistically significant effect of the three explanatory variables on the Compensation variable.
23. Create ln(Compensation) in your dataframe as a new variable. ln() is the natural log function. Repeat the analyses in Problems, 21 and 22.