612542 2237503 0720688 0475512 2917046 614213 2917046 614213 The model

# 612542 2237503 0720688 0475512 2917046 614213 2917046

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1.612542 2.237503 0.720688 0.475512 -2.917046 6.14213 -2.917046 6.14213 The model: Return=b0+b1*DE+b2*IE+b3*Expenses+b4*2STAR+b5*3STAR+b6*4STAR+e in the expenses ratio is estimated to increase the 5-year average return by 5.519%. This coefficient is not zero because its test p value 0.039 < 0.05. b4 is the difference between the avg 5 year return of a '2-star' ranked fund and a '5-star' ranked fund. The 5-STAR ranked fund is our base line and in our estimated model a 2-STAR fund earns 6.62% less avg return than a 5-STAR fund keeping the rest of the variables unchanged. BTW, the prediction equation for a 5-STAR fund is Return = b0+b1*DE+b2*IE+b3*Expenses because 2STAR = 3STAR = 4STAR = 0 when the fund is ranked 5STAR. b 3 is a slope for Expenses. Inetrpretation: Keeping all other variables unchanged, every 1% increase
The model is Salary = b0 + b1Education + b2Experience + epsilonA large firm employing thousands of workers wanted to understand the pay variation in the compasee if its own policies are being followed. A study was conducted where the number of years of education, and the number of years of Experience along with the salery was recorded for 100 manin the company.a. Obtain the regression equation that can estimate salary of a mangerb. What percentqage of the variability in salaries is explained by this model.c. Is this a valid model?d. Interpret the different coefficients.e. Olinda B. has 14 years of education, and has been with the company for the last 12 years. She now has a dispute with manegement arguing she is underpaid because other mangers with the same qualifications are paid at least \$60,000 while she is paid \$57000. Does she have a point? Use her expected salary level with 95% confidence level.
EducationExperience Salary SUMMARY OUTPUT 14 15 86010 15 12 62260 Regression Statistics 13 23 145110 Multiple R 0.83208 16 14 100250 R Square 0.692357 Part B: 69%.2 of the variability i 17 22 119470 Adjusted R 0.686014 15 16 68690 Standard E 16210.91 17 11 93390 Observatio 100 12 14 77290 14 5 47880 ANOVA 15 17 90990 df SS MS F 17 24 107570 Regression 2 5.7E+010 2.9E+010 109.1502 17 19 114970 Residual 97 2.5E+010 2.6E+008 15 19 99680 Total 99 8.3E+010 13 19 74810 17 9 88350 Coefficients tandard Erro t Stat P-value 18 11 67670 Intercept -5188.396 15968.57 -0.324913 0.745947 15 10 81760 Education 2085.315 1012.33 2.059916 0.042085 14 22 92840 Experience 4166.339 286.3665 14.54897 4.03E-026 16 15 67920 14 19 105750 Part D: Education: Every additional year of educatio 14 17 79290 Experience: Every additional year of experie 16 14 97790 18 10 89150 Part E: 74002.08 16 20 126750 UPL 106423.9 15 23 113920 LPL 41580.27 18 12 70030 Since she earns \$57000, her salary falls in the exeptable ran 15 20 110660 19 8 46690 12 10 36860 16 30 165400 14 8 101750 17 19 109360 13 19 84340 14 15 68670 17 20 131560 15 11 80090 15 36 175760 17 14 97480 18 21 109680 15 16 80320 15 12 75490 14 15 106830 16 6 47400 12 17 101400 16 10 64540 17 14 93830 15 13 66380 17 10 82430 any to nagers
14 11 50690 13 6 50620 15 16 76820 15 21 125360 14 9 100930 11 14 91850 13 18 91450 14 14 93090 16 22 122940 15 10 57040 18 19 142760 17 15 68620 16 22 134540 15 14 77030 16 1 30200 11 6 20860 16 16 87320 15 12 81640 16 16 80520 18 17 70160 15 19 120580 16 12 79100 15 3 56590 16 15 91560 17 17 116930 14 11 57100 15 14 92720 14 23 94470 18 5 31240 16 17 99090 16 12 89050 15 15 87920 16 11 57960 12 10 63330 14 18 99710 14 16 97440 17 23 132010 15 13 98940 16 18 98050 16 9 90070 15 8 85470 15 11 69640 14 11 44760 17 20 129790 14 26 158960 14 14 83430 14 21 115500 16 16 61260 16 16 82100 15 15 92630
15 14 83160 15 24 139260

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