Contents
Do independent variables need to be normally distributed?
They do not need to be normally distributed or continuous. It is useful, however, to understand the distribution of predictor variables to find influential outliers or concentrated values. A highly skewed independent variable may be made more symmetric with a transformation.
Do you always need to test for normality?
We usually apply normality tests to the results of processes that, under the null, generate random variables that are only asymptotically or nearly normal (with the ‘asymptotically’ part dependent on some quantity which we cannot make large); In the era of cheap memory, big data, and fast processors, normality tests …
Why do people test for normality?
In statistics, normality tests are used to determine if a data set is well-modeled by a normal distribution and to compute how likely it is for a random variable underlying the data set to be normally distributed.
Why do the residuals fail the assumption of normality?
Note that while a lack of normality of residuals are often caused by non-normality of the dependent variable, it could be that even though the dependent variable is normally distributed, that residuals fail the assumption of normality.
Do you use the residuals or the dependent variable?
My answer: Although the distributional assumptions for these models is on the residuals, for most designs found in education or social sciences it doesn’t really matter whether you use the residuals or the dependent variable. Who is right, and who is wrong?
Is it normal for all residuals to be normal?
There is no definitive answer, but for a 100% correct approach yes, all residuals should be normal. It depends on what you mean by comparing. Given the assumption of normal error terms, you can test based on the the assumption of two different distributions.
What is the normal distribution of a dependent variable?
The assumption is that the dependent variable (y) is normally distributed but with different means for different groups. As a consequence, if you plot just the distribution of y it can easily look very different from your standard bell shaped normal curve.