Report on Regression Analysis

Added on - 03 Feb 2020

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TABLE OF CONTENTSPart 1 Regression analysis...............................................................................................................3(1)Reporting of results.................................................................................................................4(2) Discussion..............................................................................................................................4(3) Discussing findings in relation to literature...........................................................................5Part 2 Weak form of efficiency.......................................................................................................5(1)Descriptive statistics...............................................................................................................52 Autoregressive model.............................................................................................................123 Days of effect..............................................................................................................................16
Part 1 Regression analysisVariables Entered/RemovedaModelVariablesEnteredVariablesRemovedMethod1Grad, Ceoten,profmarg,Imktval, Ageb.Entera. Dependent Variable: Salaryb. All requested variables entered.Model SummaryModelRR SquareAdjusted RSquareStd. Error ofthe Estimate1.506a.256.232518.85851a. Predictors: (Constant), Grad, Ceoten, profmarg, Imktval,AgeANOVAaModelSum ofSquaresdfMeanSquareFSig.1Regression14298720.94552859744.18910.623.000bResidual41458979.455154269214.152Total55757700.400159a. Dependent Variable: Salaryb. Predictors: (Constant), Grad, Ceoten, profmarg, Imktval, AgeCoefficientsaModelUnstandardizedCoefficientsStandardizedCoefficientstSig.BStd. ErrorBeta
1(Constant)-906.402364.398-2.487.014profmarg-11.7805.191-.161-2.269.025Imktval251.78737.265.4906.757.000Ceoten10.3426.131.1261.687.094Age-.7095.367-.010-.132.895Grad-72.54985.623-.061-.847.398a. Dependent Variable: Salary(1)Reporting of resultsRegression table revealed that R= 0.506 and R square is 0.256. Value of level ofsignificance is 0.00 which is indicating that there is a significance difference between dependentvariable salary and other predictors relevant to CEO experience and age etc. Sum of squarevalued at 14298720.945for regression followed by beta value for performance= -11.78,lmktval= 251,Ceoten=10.34, age =-709 and grade= -72.(2) DiscussionThe given equation is reflecting that salary is dependent on the years of experience, age,grade profit that cover specific percentage of sales. Multiplication of variables with beta value isindicating the variation that comes in dependent variable with change in independent variable.This equation is justified because in real business world only by considering years of experienceand grade and profitability salary is given to the CEO. Estimated coefficients reflect that withchange in small change in performance salary reduced by -11.78 points followed by marketvalue which reflect that with change in same salary of CEO get changed by 251 points. Years ofexperience that have a significant impact on salary of CEO is reflected by low value of 10.34.Age factor play a less decisive role in determining CEO salary as coefficient value is only -0.709. Grade value is -72 and this reflects that there is negative relationship among grade andsalary. It can be said that model describe the relationship among variables in better way as it isreflecting the percentage change that may come in the dependent variable due to change inindependent variable which is 25%. Correlation coefficient is 0.506 and this indicate that there ismoderate relationship among the variables. Coefficient is indicating the point change that comesin the salary due to change in predictors. All the assumptions are fulfilled in this model.
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