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Section 7 I chose to employ the liner regression analysis to the fictitious study owing to the need to determine whether or not there indeed exists a causal relationship between elements of the work environment within which public accountants work, and the ever increasing rate of employee turnover in the industry. Linear regression results to clear and concise results, arrived at by the examination of coefficients such as the Multiple R, Standard error, among others. In the proposed case study, elements such as hours worked, can easily be analyzed against the probability of leaving the accounting industry by use of linear regression. Section 8 The Multiple R, which denotes the degree of correlation between the independent and dependent variables, should exhibit a value exceeding 0 for good correlation. This being said, all the regression analyses conducted on the various elements of the public accounting work environment were all above 0. As such, this reveals varying levels of correlation between the high employee turnover rate at accounting firms and the work factors being examined in the fictitious statistical study. An analysis of the regression analysis reveals that the regression reported a high accuracy, with all the regression analyses resulting with a value of above 0 for all instances. Furthermore, the difference existent between the standard error and the adjusted R extenuates the accuracy of the analyses. Statistical Test on Hypothesis Regression analysis conducted on the work elements of hours worked on a daily basis, availability of vacation time, and the competitiveness, vis a vis the probability of leaving provided by the participants provides a
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