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What happens when the assumptions of your analysis are violated?
Violations of the assumptions of your analysis impact your ability to trust your results and validly draw inferences about your results. For a brief overview of the importance of assumption testing, check out our previous blog. When the assumptions of your analysis are not met, you have a few options as a researcher.
What happens when the assumption of normality is violated?
If the assumption of normality is violated, or outliers are present, then the linear regression goodness of fit test may not be the most powerful or informative test available, and this could mean the difference between detecting a linear fit or not.
Which is an example of a model assumption violation?
To check model assumption the transformed model has to be checked using residuals and in uence measures. Example 6.2. The sample analyzed consists of 50 observations of per capita expenditure on public schools and per capita income for each state and the District of Columbia in 1979.
What happens when your data violate linear regression assumptions?
If the X or Y populations from which data to be analyzed by linear regression were sampled violate one or more of the linear regression assumptions, the results of the analysis may be incorrect or misleading. For example, if the assumption of independence is violated, then linear regression is not appropriate.
Do you want to find no violation of assumptions?
Most people perform regression diagnostics hoping to find no violation of assumptions so they can publish their results. That’s backwards. You should perform the diagnostics hoping for violations, because each violation is something more you can learn from the data.
What happens when compound symmetry and sphericity assumptions are violated?
However, when this happens, the compound symmetry and sphericity assumptions have been violated, and independent contrasts cannot be computed. When the compound symmetry or sphericity assumptions have been violated, the univariate ANOVA table will give erroneous results.