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Excel Printout for Canadian Industrial Supplies0

Excel Printout for Canadian Industrial Supplies0 - Question...

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Unformatted text preview: Question 2 Name: ———-—n.—____ Excel Printout for Canadian Industrial Supplies Salary Employ Gender Clerical Age 495. 69 1 0 47 Gender O-lemale 406 46 0 0 40 1—male 567 125 1 D 39 523 20 1 0 45 Clerical O-technical 575 173 1 o 56 1-clerical 437 37 o o 25 664 237 1 o 48 491 52 1 o 28 472 67 o o 45 407 124 1 o 30 378 - 12 1 0 20 725 313 1 o 46 690 291 0 o 47 440 34 o o 23 662 275 1 o I 48 523 111 1 o 56 428 14 1 0 27 535 39 1 o 29 533 188 0 0 58 523 44 1 o 34 446 21 o o 24 523 35 1 o 25 493 46 1 o 21 478 43 1 0 25 507 27 o 0 22 478 19 1 0 24 645 229 1 o 58 577 276 o o 58 554 330 1 o 52 654 331 1 0 60 566 72 1 o 41 433 35 0 o 27 466 84 1 o 47 365 25 o 1 21 677 220 0 1 39 473 31 1 1 25 B44 300 1 1 55 685 311 1 1 50 356 6 G 1 32 444 13 1 1 44 476 69 o 1 46 403 76 0 456 53 0 409 17 0 691 354 0 463 64 0 436 88 0 413 . 1 1 1 734 407 0 4o 47 53 58 42 21 53 .54-fi—L—l—hd—L Simgle-r Matrix Salem Emgioy Gender Clerical Age Salary 1 Employ 0.862 1 Gender 0.225 0.034 1 Clerical -0.064 0.043 -0.364 Age 0.603 0.699 -0.016 0.095 1 Salaries vs Months of employment SUMMARY OUTPUT: Regression Statistics Multiple H 0.862 R Square 0.744 Adjusted Fl Square 0.738 Standard Error 51.163 Observations 49 ANOVA- __—T'—ss_"T_—_— Regression 1 357098 357098 Residual 47 123030 2618 Total 48 480127 1 F 136 Significance F 0 Coefficients Standard Error t Stat P-value Intercept 426.654 10.665 40.005 0.000 Employ - 0.742 0.064 11 .680 0.000 Normal Probability Piotr 0. 00 20.00 40.00 v ' Sample Percéntile' Employ Line Fit Plot Residuals é 2 § Salaries vs Employment and age SUMMARY OUTPUT Regression Statistics Multiple R 0862 R Square 0.744 Adjusted R Square 0.733 Standard Error 51.716 Observations 49 ANOVA df 38 MS F Significance F Regression 2 357098 178549 67 0 Residual 46 123029 2675 Total 48 480127 Coefficient Standard tStat P-value 5 Error Intercept 426.279 26.936 15.826 0.000 Em ploy 0.741 0.090 8.256 0.000 Age 0.012 0.818 0.015 0.98 Salaries vs Employment, age, and gender SUMMARY OUTPUT Regression Statistics Multiple R 0.884 R Square 0.782 Adjusted R Square 0.768 Standard Error 48.202 Observations , 49 ANOVA df 55 MS F Significance F Regression 3 375572 125191 54 0 Residual 45 104555 2323 Total 48 480127 —__—_______.____— Coefficients Standard Error 1‘ Stat _ P-value Intercept 400.898 26.670 15.032 0.000 Employ 0.726 ' 0.084 8.661 0.000 Age 0.133 0.764 0.173 0.863 Gender 39.321 13.945 2.820 0.007 Salaries vs Employment, Age, Gender, and Clerical SUMMARY OUTPUT Regression Statistics Multiple R 0.885 R Square 0.783 Adjusted R Square 0.764 Standard Error 48.624 Observations 49 ANOVA df 85 MS F Significance F Regression 4 3760972 94024.3 39.8 0.0 Residual 44 1040302 2364.3 ‘ Total 48 4801 27.4 Coefficie Standard 1‘ Stat P-vaiue nts Error Intercept 403.797 27.599 14.631 0.000 Em ploy ' 0.726 0.085 8.581 0.000 Age 0.160 0.773 0.207 0.837 Gender 36.736 15.099 2.433 0.019 Clerical -7.529 15.980 ’ -0.471 0.640 —-—-———-——_.___ Salaries vs Employment and Gender SUMMARY OUTPUT Regression Statistics Multiple R 0.884 R Square 0.782 Adjusted R Square 0.773 Standard Error 47.691 Observations 49 ANOVA . df SS MS F Significance F Regression 2 375502 187751 83 0 Residual 46 1 04625 2274 Total 48 480127 ——'—'——_——m —I——u—.—______ Coefficients Standard Error tStat P-vaiue Intercept 404.97 12.53 32.33 0.00 Employ 0.74 0.06 12.43 0.00 Gender 7 39.19 13.78 2.84 0.01 —-—-—-———n—..—_ Salaries versus Months‘gf Engiloyment and Gender '- Besidflélé ,vs Fitted Value-é '3 Residuals Fitted Values Salaries versus Months of Employment and Gender Salary o§§§§§£§§ p 8 40.00 film 80.03” 100.00 131% Sarrple Percentile Salaries versus Months of Employment and Gender Based on the fitted values, the residuals have been ranked and then divided into three strips StriE 1 StriE 2 StriE 3 Residuals Residuals Residuals m Mean -7.8 Mean 9.2 Mean -1.4 Standard Error 9.8 Standard Error 9.5 Standard Error 14.6 Median -1.9 Median 6.8 Median 11.8 Mode #N/A Mode #N/A Mode #N/A Standard Deviation 39.1 Standard Deviation 37.8 Standard Deviation 601 Sample Variance 1532.2 Sample Variance 1430.5 Sample Variance 3613.8 Kurtosis 0.6 Kurtosis -0.9 Kurtosis 1.5 Skewness 0.3 Skewness 0.1 Skewness -0.9 Range 157.1 Range 121.8 Range 243.2 Minimum -75.0 Minimum -52.9 Minimum -133.2 Maximum 82.1 Maximum 68.8 Maximum 110.0 Sum 424.3 Sum 147.8 Sum -23.5 Count 16 Count 16 Count 17 -——————-—_._—.—_— ...
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