Multiple regressions are a tool in statistics that helps one to analyze how numerous independent variables are related to a separate variable. In the quantities article, the author used multiple regressions because of various reasons. Foremost, many regressions gave the author the ability to determine the relative influence of different variable predictors to the criterion value. The other reason as to why the author used multiple regressions is because it gave him the ability to identify outliers and abnormalities.
I believe that multiple regressions were the best choice because of various reasons. The fundamental purpose is that numerous regressions in most cases usually give an accurate result. The regression allowed the author to establish a measure of relationships between the independent and dependent variable. Regression is also an appropriate choice because it acts as an assessment tool (Kramer, 2016). The author had the capability of determining the accuracy of the prediction because of the regression. That made the data more reliable. It was an appropriate choice because the author had the chance of using multiple variables. Regression allowed the author to test various independent variables that could sum to explain issues of the dependent variable. That is what makes numerous regressions from other models which would enable you only to use an independent variable (LaBuda, 2016) . Multiple regressions were the most accurate choice because it gave the author the ability to put in new trends.
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The author displayed the data in the quantitative article. The results did not stand alone. That is because the independent variables were relying on the dependent variable. The independent variables had to be developed so that it can explain the issues of dependent variables.
References
Kramer, C. (2016). Simple Regression in Multiple Regression Notation. The American Statistician , 20 (3), 25. http://dx.doi.org/10.2307/2681496
LaBuda, M. (2016). Multiple regression analysis of twin data obtained from selected samples. Genetic Epidemiology , 3 (6), 425-433. http://dx.doi.org/10.1002/gepi.1370030607
