Examination 1

Directions:
Respond to each item in a clear and concise paragraph. Do not copy passages from the lectures or book. Rather, express your own understanding of the material. Do not write anything or everything that comes to mind; instead, try to devise a brief answer that captures the most relevant information.

1. Discuss reliability and validity. How do they affect data analysis?

2. Create a variable to measure the concept of happiness as a categorical and a numeric variable. Which is a better measure of the concept? Explain your choice.

3. What is an 'outlier' and how do you detect it?

4. Why is regression analysis a more powerful statistical tool than analysis of means or crosstabulations?

5. What is multicollinearity? Why is it important?

6. What is model specification in regression analysis, and why is it so important?

7. Add another block to the least squares model you computed in class and interpret the results. Explain why you added the additional variables and how the model changed from the previous step.

8. Compare and contrast ordinary linear regression and logistic regression? Give an example of the kind of data you might analyze with logistic regression.

9. Why do we use the natural logarithm of odds in logistic regression?

10. Add another block to the logistic model you computed in class and interpret the results. Explain why you added the additional variables and how the model changed from the previous step.

11. What is the main research question in Gager and Yakibu? What data did they use?

12. Describe the results displayed for model 4 in Table 2 of Gager and Yakibu.

13. What is the main research question in Fujimoto? What data did the author use?

14. Interpret the results in table 5 or table 6 of Fujimoto. Indicate which table you are interpreting in your answer.

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