To conduct a valid regression analysis, both x and y must be approximately normally distributed.
True
False
False
In regression analysis, the assumption is that the errors, or residuals, are normally distributed, not necessarily the independent variable (x) or the dependent variable (y). The errors refer to the difference between the actual observed values of the dependent variable and the predicted values obtained from the regression equation.
To check the assumption of normality for the errors, one can use techniques such as a histogram or a normal probability plot. These graphical methods can help assess if the errors are approximately normally distributed.
However, it is generally not necessary for the independent variable (x) or the dependent variable (y) to be normally distributed for regression analysis. In fact, regression analysis can still provide valid results even if the variables do not follow a normal distribution.