Linear Regression
Linear regression is a statistical test used in a data set to determine and quantify any relation between the given variables. It is a method used to compute the value of a dependent variable from the independent one to make a concrete assumption of a study (Kumari & Yadav,2018). Linear regression is the most used statistical technique by many researchers because its concepts help predict dependent variables on one or more independent variables. Therefore, it is efficient and reliable in managing statistical data in research.
Strengths of Linear Regression
Researchers use linear regression to analyze statistical data in research. This statistical method has several strengths that help analyze research using applicable techniques that help understand and summarise the concepts of a study (Gupta et al., 2017). The linear regression’s technique of predicting the dependent variable from the independent variable makes it easier to use the method in a study and conclude with a value for testing variables. The method uses mathematical ways to calculate the predicted value. This approach makes the sampled data in a study less complex than other statistical techniques, thus summarizing and making it easier to use. Another strength of the statistical method is that it enables one to predict the values that can be used in finding the results and later used in future research.
Peer-reviewed Study
A study conducted how health researchers tend to use linear regression in making normality assumptions and the effects of such decisions in finding convincing results (Schmidt & Finan,2018). The study showed that sometimes it is not always right to base decisions on the basics findings, as this can create biasness thus leading to an inconclusive result. Due to the biasness, most health research tends to differ from the main goal of the study. For instance, in the case of the COVID-19 virus, the assumptions made in the first phase, in 2020, were mostly based on the statistical values of those infected. As a result, the health researchers made decisions regarding the number of those affected, not necessarily focusing on those not affected who could be victims as well. The linear regression has been used in this study to help health researchers understand that using 0the normality assumptions can differ with the research and lead to biasness.
References
Gupta, A., Sharma, A., & Goel, A. (2017). Review of regression analysis models. Int. J. Eng. Res., 6(08), 58-61. https://d1wqtxts1xzle7.cloudfront.net/60342486/review-of-regression-analysis-models-IJERTV6IS08006020190820-1603-1yhlia6-with-cover-page-v2.pdf?Expires=1634143997&Signature=BNbVtXYUbKo8M-
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