Correlation Statistics V Regression Statistics Essay

Correlation Statistics V Regression Statistics Essay

Correlation and regression are two important test statistics that are utilized in a study that focuses on understanding the relationship between two variables and/or the effect of one variable on another. In correlation statistics, there are two variables that are related to each other whereas in regression, and explanatory variable and a response variable are utilized (“Introduction to Correlation and Regression Analysis,” 2013). Generally, the main aim of correlation statistics is to examine whether two measurement variables co differ and determine the strength of the link between variables. On the contrary, regression statistics focuses on expressing the relationship between two measurement variables using an equation. As a result, of the difference in focus, correlation statistics and regression statistics are suitable for different circumstances.

Regression statistics is suitable for situation where the problem of interest or issue being examined is the nature of relationship between a dependent variable and an independent variable. In this case, the dependent variable is considered as the response variable whereas the independent variable is regarded as the explanatory variable. For instance, if the problem of interest is the impact of age on height, a regression analysis is the most suitable test statistics since it will help in providing insights on how height (the dependent or response variable) is influenced by age (the independent or explanatory variable). Through this process, the researcher will examine the nature of the relationship between age and height, which helps in predicting the height of a specific age. In contrast, correlation statistics is suitable for circumstances that require examining the linear relationship between variables in order to quantify the strength of the relationship. For instance, if the problem of interest is the approximate the relationship between gestational age and birth weight of an infant, correlation analysis is more appropriate since it helps examine the variance of gestational age relative to infant birth weight.

In addition to being suitable for different circumstances, correlation and regression are associated with different advantages over each other despite the common use testing hypotheses regarding cause-and-effect relationships, examining relationship between two variables, and estimating the value of one variable relative to the value of another variable. Regression statistics provide more advantageous results as compared to correlation statistics. The main advantage of a regression statistic over a correlation statistic is that regression generates results that are clearly linked to the obtained measurement. Generally, a correlation statistic reduces a series…


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