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Strength & Weakness of Regression Model Assignment Sample

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Strength & Weakness of Regression Model Assignment

Strength of regression method

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The term "regression analysis" derives its meaning from the idea "that it is all about statistics", data refers to the facts and numbers that define the research field. The benefits of regression analysis are that it allows the analyst to calculate the statistics in order to assist and make informed choices about the testing business now and in the future (Moravcikova et al. 2017). Therefore in answering the research question, it benefited by significant as well as relevant answers and that too helps in robustness check, it assists in the study of "data on kissing, dating, and having sex", "generally finding the similar findings" both in the sphere of "junior high school".

 Here in this concentrated research topic, the regression has fitted with reliable data. That provided with simplified and specified data that helps in demonstrating the research question. The study included "respondents in their twenties" in the survey of lovers "after junior high school" and found comparable findings, but "the women variable now" has been noticed with "inverted U-shaped curve with a median" considering the age structure of "32.7 years." The research got comparable findings when we deducted age compounded from the regression tests.

The weakness of the regression method

Regression fails to suit dynamic datasets because it maintains a linear relationship between the dependent and independent variables. In most real-world situations, the relationship between the dataset's variables is not really linear, so a straight line does not in itself properly suit the results. Here in this concentrated research, the respondent number us considered too much expanded (Moravcikova et al. 2019). Therefore using this method may raise questions on the findings. Here the test result has stated that Japanese men are more asexual in number than that woman in their twenties and thirties. Thus being Japan represents heterosexual marriage. Therefore this can be considered as another reason and another factor of the test.


The Correlation method would be the better alternative because this is a method, in which a researcher tests two variables, understands and evaluates their statistical relationship with no influence from any other variable. Human minds are capable of incredible tasks. Here in this study using the variable like "income rate", "years of education", "marriage dummy", etc. are considered to analyse the data in order to get the appropriate result (Pirohov et al. 2019). As a result of using this correlation approach, the researcher would be able to find a stronger relationship between the variables studied. As a result, a better understanding and interpretation of the variables will result in a knowledgeable outcome. Thus a result, the study of homosexuality in culture serves as an additional variable that can be examined using this correlation approach in relation to years of schooling and the married dummy

Reference List


Leventhal, A.M., Bello, M.S., Galstyan, E., Higgins, S.T. and Barrington-Trimis, J.L., 2019. Association of cumulative socioeconomic and health-related disadvantage with disparities in smoking prevalence in the United States, 2008 to 2017. JAMA internal medicine, 179(6), pp.777-785.

Moravcikova, D., Krizanova, A., Kliestikova, J. and Rypakova, M., 2017. Green Marketing as the Source of the Competitive Advantage of the Business. Sustainability, 9(12), p.2218.

Pirohov, O., Andronov, V., Rybka, E., Krainiukov, O., Karpets, K., Semenyshyna, I., Kapitan, R., Promska, A. and Horbov, O., 2019. Development of the correlation method for operative detection of recurrent states 

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