1. What are the five steps in the hypothesis testing procedure? Are the steps followed in sequence? Explain why or why not. How does the five-step procedure for hypothesis testing differ when comparing two groups using a t- or z-test? How is the process similar?
2. What is the importance of a 5% significance level? Why would you choose 5% as opposed to 10% or 1%? What effect does the level of significance have on a Type II error? Give examples.
3. Under what conditions would you use a t-test as opposed to a z-test? Can you use the t-table to determine the critical value of the z-test? Explain why. What are the differences between a one-tailed and a two-tailed test? Give Examples.
4. Explain the different between r and ρ. How are they used in statistical analysis?
5. previously we tested the hypothesis about two means, now we look at testing the hypothesis about several means. By using the ANOVA we can test the hypothesis with a specified value of α. Discuss the process used to reject or fail to reject the null hypothesis when using the ANOVA. Note: specifically discuss the MS (factor) and MS (error).
6. The idea of regression is to build a model that estimates or predicts one quantitative variable (y) by using at least one other quantitative variable (x). Multiple linear regression, uses more than one x variable to estimate the value of the y variable.
explain the following:
- When would you use a multiple regression analysis? Give an example.
- Which variable represents the independent variable, which variable represents the dependent variable?
- What does independent variable mean?
- What does dependent variable mean?
- How would you come up with a list of possible x variables that may be helpful in estimating y.
- How would you collect data on the y and x variables from step 1?
- How would you check the relationships between each x and y variable and how would you eliminate those x variables that aren't strongly related to y.
- How would you avoid the problem of multicolinerity?