Correlation versus a multiple linear regression.

Based on the assigned reading, describe and explain the differences between a correlation versus a multiple linear regression

By definition, correlations are analysis that helps us know the association or absence of association between two variables “X” and “Y”. In comparison, regression predicts the value of the dependent variable between two or more variables (Tanni, Patino & Ferreira, 2020). There are many differences between correlations and multiple linear regressions. One significant difference is that correlation is used in measuring the degree of a relationship between two variables (x and Y). Simultaneously, regression deals with how one variable affects the other(Roy, 2018). In other words, regression describes how an independent variable is numerically related to the dependent variable. Secondly, in terms of usage, correlation is used to represent the linear relationship between two variables.

In contrast, regression is used to fit the best line and estimate one variable based on another variable. Thirdly, in correlation, there is no difference between dependent and independent variables. In other words, the correlation between x and y is similar to y and x. on the other hand, the regression of y on x is different from x on y (“Statistics Unit 5 – Inferential Statistics / Correlations – AMPLab”, 2021). fourthly, another difference is that correlation indicates the strength of association between variables. This is different from regression since regression reflects the unit of change in the independent variable on the dependent variable. Lastly, correlation aims to find a numerical value that expresses the relationship between variables. In contrast, regression aims to predict the values of the random variables based on the values of a fixed variable.













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