The correct answer is C. normality of residuals.
Residuals are the differences between the actual values of the dependent variable and the predicted values of the dependent variable. The assumption of normality of residuals states that the residuals are normally distributed. This means that the residuals should be evenly scattered around the regression line, with no particular pattern.
If the residuals are not normally distributed, it can affect the accuracy of the regression model. For example, if the residuals are skewed, the regression model may not be able to accurately predict the values of the dependent variable.
There are a number of ways to check the normality of residuals. One way is to use a histogram of the residuals. A histogram is a graphical representation of the distribution of data. If the residuals are normally distributed, the histogram should be bell-shaped.
Another way to check the normality of residuals is to use a normal probability plot. A normal probability plot is a graph that shows the relationship between the residuals and the expected values of the residuals. If the residuals are normally distributed, the points on the normal probability plot should fall close to a straight line.
If the residuals are not normally distributed, there are a number of things that can be done to address the issue. One option is to transform the data. Transformations can help to make the data more normally distributed. Another option is to use a different type of regression model, such as a non-parametric regression model.
I hope this helps! Let me know if you have any other questions.