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Introduction to Correlation and Regression Analysis

   

Added on  2021-04-19

9 Pages1100 Words185 Views
Running head: CORRELATION AND REGRESSION ANALYSISCorrelation and Regression AnalysisName of the StudentName of the UniversityAuthor Note

1CORRELATION AND REGRESSION ANALYSISTable of ContentsIntroduction................................................................................................................................2Analysis......................................................................................................................................2Part 1......................................................................................................................................2Part 2......................................................................................................................................2Part 3......................................................................................................................................3Part 4......................................................................................................................................3Part 5......................................................................................................................................3Conclusion..................................................................................................................................4

2CORRELATION AND REGRESSION ANALYSISIntroductionA correlation coefficient measures the linear relationship between two variables(Bluman 2013). It is used in linear regression (Costa 2013). There are various measures ofcorrelation coefficients like Spearman’s rank correlation coefficient,Pearson’s productmoment correlation coefficient etc (Wang 2013). Regression coefficient represents the rate ofchange of dependent variable as a function of changes of independent variables. AnalysisPart 1The simple linear regression model is yi=a + b*xi + ɛi, where the dependent variable is y andthe independent variable is x. Here, regression coefficient is b and intercept is a. ɛ is theresidual part. Population parameter should be estimated and the simple linear regressionmodel will be ^yi=^a + ^b *xi .Here, the residual = yi -^yi . Parameters should be estimated using ordinary least squaremethod. Thus, ^a = ́y-^b∗ ́x,^b =∑i=1n(Xi− ́x)∗(yi− ́y)∑i=1n(xi− ́x)2(Holicky 2013).The simple linear regression of salary on experience has been done using excel. The outcomehas been provided in appendix (Table 1.1).

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