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American Intercontinental University

Unit 5 Individual Project

BUSN311-1301B-10: Quantitative Methods and Analysis

Instructor Leonidas Murembya

April 23, 2013,

Abstract

This paper will be discussing regression analysis using AIU’s survey responses from the AIU data set in order to complete a regression analysis for benefits & intrinsic, benefits & extrinsic and benefit and overall job satisfaction. Plus giving an overview of these regressions along with what it would mean to a manager (AIU Online).

Introduction

Regression analysis can help us predict how the needs of a company are changing and where the greatest need will be. That allows companies to hire employees they need before they are needed so they are not caught in a lurch. Our regression analysis looks at comparing two factors only, an independent variable and dependent variable (Murembya, 2013).

Benefits and Intrinsic Job Satisfaction

Regression output from Excel

SUMMARY OUTPUT Regression Statistics

Multiple R 0.018314784

R Square 0.000335431 The portion of the relations explained

Adjusted R Square -0.009865228 by the line 0.00033% of relation is

Standard Error 1.197079687 Linear.

Observations 100 ANOVA df SS MS F Significance F

Regression 1 0.04712176 0.047122 0.032883 0.856477174

Residual 98 140.4339782 1.433

Total 99 140.4811 Coefficients Standard Error t Stat P-value Lower 95% Upper 95%

Intercept 4.731133588 1.580971255 2.992549 0.003501 1.593747586 7.86852

Intrinsic -slope 0.055997338 0.308801708 0.181338 0.856477 -0.5568096 0.668804 Line equation is benefits =4.73 + 0.0559 (intrinsic) Intercept- t-stat HO: Coefficients is zero. Intrinsic t-stat is zero…...

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