Exam 1 Study Guide

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School

Louisiana State University, Shreveport *

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Course

704

Subject

Statistics

Date

Apr 3, 2024

Type

docx

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Uploaded by dbelmont1416la

Chapter 1 Study Guide 1. Distinguish between the purpose of statistical/data models and datamining/algorithmic models 2. Distinguish between the concepts of univariate analysis, bivariate analysis, and multivariate analysis 3. Describe the concept of a variate 4. Distinguish between nonmetric data and metric data 5. Identify examples of nominal, ordinal, interval, and ratio data 6. Describe an example of measurement error 7. Distinguish between measurement validity and reliability 8. Explain the purpose of a composite measure 9. Describe multicollinearity and its implications 10. Explain the purpose of a specified significance level 11. Define statistical power and list three factors that impact it 12. Distinguish between dependent and independent variables 13. Explain the purpose of dependence and interdependence techniques 14. Identify the mathematical forms of simple regression analysis, multiple regression analysis, analysis of variance, and multivariate analysis of variance 15. Describe an example of regression analysis 16. Describe an example of analysis of variance 17. Describe the concept of practical significance 18. Describe the consequences of a specification error 19. Describe the purpose of a validation sample 20. List the stages of the six-step approach to multivariate model building Chapter 2 Study Guide 1. Explain the importance of examining data 2. Distinguish between a variable, a case, and an observation 3. Explain the purpose of a histogram 4. Determine whether a distribution is skewed based on the value of its skewness 5. Determine whether a distribution is relatively peaked or flat based on the value of its kurtosis 6. Explain the purpose of a scatterplot 7. Describe how to interpret a boxplot 8. Define Pearson’s correlation coefficient 9. Define missing data process and missing data analysis 10. Describe the impact of missing data 11. List four steps for a missing data analysis 12. Distinguish between ignorable and unignorable missing data 13. Distinguish between missing data that is MAR, MCAR, and MNAR 14. Define outlier 15. Describe the two contexts in which outliers can be examined

16. Distinguish between univariate, bivariate, and multivariate methods for identifying outliers 17. List the four statistical assumptions on which multivariate techniques for developing statistical models are based 18. List the four basic outcomes data transformations can be used to achieve 19. Identify the number of dummy variables needed to include a nonmetric variable in a statistical model 20. Distinguish between indicator and effects coding in dummy variables

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