What if model assumptions are not met?
For example, when statistical assumptions for regression cannot be met (fulfilled by the researcher) pick a different method. Regression requires its dependent variable to be at least least interval or ratio data.
What test to use if homogeneity of variance is violated?
ANOVA
For example, if the assumption of homogeneity of variance was violated in your analysis of variance (ANOVA), you can use alternative F statistics (Welch’s or Brown-Forsythe; see Field, 2013) to determine if you have statistical significance.
What to do if assumptions of linear models are violated?
Another model might be better to explain your data (for example, non-linear regression, etc). You would still have to check that the assumptions of this “new model” are not violated. Your data may not contain enough covariates (dependent variables) to explain the response (outcome). In this case, you cannot do anything else.
How can I check the assumption of normality?
Check the assumption visually using Q-Q plots. A Q-Q plot, short for quantile-quantile plot, is a type of plot that we can use to determine whether or not the residuals of a model follow a normal distribution. If the points on the plot roughly form a straight diagonal line, then the normality assumption is met.
How to check the assumption of linear regression?
1. Check the assumption visually using Q-Q plots. A Q-Q plot, short for quantile-quantile plot, is a type of plot that we can use to determine whether or not the residuals of a model follow a normal distribution. If the points on the plot roughly form a straight diagonal line, then the normality assumption is met.
What are the assumptions for Negative serial correlation?
Depending on the nature of the way this assumption is violated, you have a few options: For positive serial correlation, consider adding lags of the dependent and/or independent variable to the model. For negative serial correlation, check to make sure that none of your variables are overdifferenced.