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Asked Dec 1, 2019
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Discuss the reasons and situations in which researchers would want to use linear regression. How would a researcher know whether linear regression would be the appropriate statistical technique to use? What are some of the benefits of fitting the relationship between two variables to an equation for a straight line?

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Step 1

Reasons and situations to use linear regression:

A linear regression is suitable in the following situations:

  • If a plot of the response variable and the explanatory variable show a linear relationship;
  • If the residuals are homoscedastic;
  • If the residuals are independent of one another;
  • If the residuals have a normal distribution (or can be manipulated to attain an approximate normal distribution).
Step 2

How to decide whether linear regression should be used:

The easiest and most suitable method to determine whether a linear regression is suitable in a given situation, is by using a graphical plot. When a dataset is available with observations on the potential response variable, with those of the corresponding explanatory variable, a researcher should draw a scatterplot of the response variable versus the explanatory variable. If the plot seems quite linear, then it is suitable to fit a linear regression model to the data. If the plot is non-linear but in such a pattern that a transformation (such as, logarithmic, square, cubic, square root, cube root, etc.) on either one or both the response and the explanatory v...

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