1)True or False:

a)Goodness of fit tests are right-tailed

b)The expected frequencies for a Goodness of Fit test are computed by taking the proportion of n designated by the proposed distribution.

c)Suppose that a chi-square test of independence using a contingency table is to be conducted to compare 5 sources of fraud against 2 genders, the degrees of freedom would be 10.

d)In a test of one-way ANOVA, the alternative hypothesis claims that at least two of the means are not equal.

e)The F distribution depends upon two degrees of freedom - degrees of freedom in the numerator and degrees of freedom in the denominator.

2) Outline the basic ideas behind the chi-square test of independence. What null and alternative hypotheses are used?

3) How does a chi-square test of homogeneity differ from a chi-square test of independence?

4) What is the purpose of ANOVA? What distribution is used for ANOVA? What are some of the properties of that distribution? Give examples of typical null and alternative hypotheses for one way ANOVA.

5) Explain the difference between correlation analysis and regression analysis. Give an example of a lurking variable. If a statistician computed a value of r = -2.83 what would you tell that statistician? Under what circumstances can we calculate r? Can the regression equation be assumed to hold 100 years from now?

6) Are there other correlation coefficients other than the Pearson Product Moment Correlation Coefficient?

7) What is meant when we say that two variables have a strong positive (or negative) linear correlation? Is it possible that two variables could be strongly related but have a low linear correlation? Can you give an example?

Give a very general description of how the least-squares criterion is involved in the construction of the least squares line.

8) Sensitivity vs. Specificity

In medical research they use the terms sensitivity and specificity. How do these relate to Type 1 and Type II errors in statistics? What is the relationship between Type I and Type II errors? Define power of a test.

What are the differences between classical and p-value hypothesis testing?