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Regression Analysis Assumptions

   

Added on  2019-10-18

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Module 4 DiscussionRoberto Hernandez posted May 2, 2017 12:46 AMDr. Liu and classmates,Taylor (2010), states that regressions are form of statistical analysis frequently used to test casual hypothesis in social science research. Two examples of situations where regression was used as an Air Force Recruiter. The first situation was determining that young adults graduating from high school would visit the recruiting office after graduation. The factor was that recent graduates were not ready to go to college, but wanted to become independent and would join the Air Force. Every year as a recruiter thenumber of recent graduates that would come to the recruiting office were higher than those that had graduated years earlier. Another situation was the number of special forcesapplicants would increase when there was an invitation to take the physical test at the local gym. The test was conducted by active duty special forces members and it was advertised to everyone in the schools, local community, and media outlets. The number of applicants rose compared to not having any special event. Regression analysis is usefulin applications because it helps when predicting an outcome and identifying the impacting factors. This helps to create a better way to conduct business, especially in sales.RobertReferences:Taylor, M.Z.. (2010). 21st Century Political Science: A reference handbook. Chapter 57 Regression Analysis. Georgia Institution of Technology. Retrieved 2010. From: http://web.a.ebscohost.com.ezproxy.trident.edu:2048/ehost/ebookviewer/ebook/ZTAwMHhuYV9fNDgxMTAxX19BTg2?sid=4d2673ab-9a37-46bf-b4c5-8db602d16b63@sessionmgr4006&vid=0&format=EB&rid=1You are correct about the use of regression modeling to test the causal relationships but there are few assumptions are needed to run regression which are necessary to check before modeling process. Theseare the assumptions which enables us to make use regression efficiently for better results, lets list them and discuss a little bit as below:1. Linear Relationship: The dependent variable and independent variable must be linearly related to each other.2. Multivariate Normality: All the variables included in the model should be normally distributed.3. No Multicollinearity: All the independent variables must be independent from each other.4. No Autocorrelation: The residuals should be independent from each other.5. Homoscedasticity: The variance of all variables must have same finite variance.
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