Содержание
- 2. Outliers Impact
- 3. Assumptions Parametric tests based on the normal distribution assume: Additivity and linearity Normality something or other
- 4. Additivity and Linearity The outcome variable is, in reality, linearly related to any predictors. If you
- 5. Normality Something or Other The normal distribution is relevant to: Parameters Confidence intervals around a parameter
- 6. When does the Assumption of Normality Matter? In small samples – The central limit theorem allows
- 7. Spotting Normality
- 8. The P-P Plot
- 9. Assessing Skew and Kurtosis
- 11. Homoscedasticity/ Homogeneity of Variance When testing several groups of participants, samples should come from populations with
- 12. Assessing Homoscedasticity/ Homogeneity of Variance Graphs (see lectures on regression) Levene’s Tests Tests if variances in
- 14. Homogeneity of Variance
- 15. Independence The errors in your model should not be related to each other. If this assumption
- 16. Reducing Bias Trim the data: Delete a certain amount of scores from the extremes. Windsorizing: Substitute
- 17. Trimming the Data
- 18. Robust Methods
- 19. Transforming Data
- 20. Log Transformation
- 21. Square Root Transformation
- 22. Reciprocal Transformation
- 23. But …
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