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Regression on hourly earning and age

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Added on 2019-12-28

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AHE+.549*(age)-3.742*gender+8.217*education+.566 3 C3 Difference from the outcome of C1 and C2 The regression applied in case of C1 has one variable as age with AHE however, in case of C2 AHE has been compared with three variables such as age, gender and education.

Regression on hourly earning and age

Added on 2019-12-28

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QuantitativeBusiness : QuestionC

TABLE OF CONTENTSQUESTION C..................................................................................................................................3C1 Regression on hour earning on age........................................................................................3C2 Running regression of AHE on age, gender and education...................................................4C3 Difference from the outcome of C1 and C2...........................................................................5C4 Predicting earning using regression.......................................................................................5C5 ................................................................................................................................................5C6.................................................................................................................................................5C7.................................................................................................................................................7

QUESTION CC1 Regression on hour earning on ageIn the below mentioned table, regression has been found with the average hourly earningand age. It has been found that regression of age on average hourly earning is .561. This reflectsthe values of earning changes with respect to age. For example, those with the higher age groupmight have good earning in comparison to those who have recently joined the job. In thismanner, outcome differs in accordance with the age.a. Dependent Variable: Average Hourly Earningsb. The independent variable or constant was the age factorModel SummaryModelRR SquareAdjusted R SquareStd. Error of the EstimateChange StatisticsR Square ChangeF Changedf110.0240.02310.29643440.024364.9771.153aModelSum of SquaresdfMean SquareFSig.1Regression38693.566138693.566364.977Residual1606044.87415149106.017Total1644738.44115150ANOVAa.000bModelUnstandardized CoefficientsStandardized CoefficientstSig.BStd. ErrorBeta1(Constant)2.7630.8743.1620.002Age0.5610.0290.15319.1040Coefficientsa

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