1. Explain how graphical methods can complement the empirical measures when examining data.

2.List potential underlying causes of outliers. Be sure to include attributions to both the respondent and the researcher.

3. Discuss why outliers might be classified as beneficial and as problematic.

4. Distinguish between data that are missing at random (MAR) and missing completely at random (MCAR). Explain how each type affects the analysis of missing data.

5. Describe the conditions under which a researcher would delete a case with missing data versus the conditions under which a researcher would use an imputation method.

6. Evaluate the following statement: In order to run most multivariate analyses, it is not necessary to meet all the assumptions of normality, linearity, homoscedasticity, and independence.

7. Discuss the following statement: Multivariate analyses can be run on any data set, as long as the sample size is adequate.