- Know the assumptions / properties of Pearson's Linear Correlation Coefficient.
- What values is it between?
- What does a value of zero mean / not mean?
- What happens if you change the scale of either variable?
- What happens if you switch the variables?
- What type of relationship does it measure?
- What type of distribution does it have?
- How many degrees of freedom does it have?
- What type of distribution do the ordered pairs (x,y) have?
- Know the assumptions / properties of the contingency tables.
- What is the null hypothesis?
- How are the sample data selected?
- What requirement must be met?
- What type of data is used?
- What type of distribution does it have?
- How many degrees of freedom does it have?
- What type of test is it?
- Know the assumptions / properties of multinomial experiments.
- What is the null hypothesis?
- What requirement must be met?
- What is the sample data?
- What distribution does it have?
- How many degrees of freedom does it have?
- What type of test is it?
- Know the guidelines for using the regression equation.
- Know the guidelines for using a regression equation from page 501.
- What is the equation that should be used if there is no significant linear correlation (pg 500)?
- Know the properties of multiple regression.
- When does the largest value of R-square occur?
- When does the largest value of the adjusted R-square occur?
- How is the Analysis of Variance used to test the regression equation?
- How does correlation between independent variables affect the choice of variables?
- What tools can be used to perform multiple regression.
- What are the degrees of freedom?
- Contingency Table. Use Statdisk. Write the null hypotheses. Find one (not all of them) expected frequency. Identify the degrees of freedom. Find the p-value. Write the decision and conclusion.
- A statistical test that you have never seen and a p-value is given. Give the conclusion.
- Know what happens to the test statistic of a contingency table when the data is manipulated. Three parts.
- Know what happens to the test statistic of a multinomial experiment when the data is manipulated. Two parts.
- Know what happens to the linear correlation coefficient when the data is manipulated. Three parts.
- Work a chi-square goodness of fit problem using Statdisk. The observed frequencies are given to you. Give the degrees of freedom, critical value, test statistic, p-value, decision, and conclusion.
- The value of the correlation coefficient, r, and the total variation are given. Find the coefficient of determination, explained variation, and unexplained variation.
- Linear regression problem. The sample size, correlation coefficient, means, and regression equation are given to you. Write the null hypothesis, look up the critical values, make a decision, make an estimation, give the value of the coefficient of determination, determine whether the data is normally distributed by looking at a normal probability plot.
- Work a linear regression problem using Statdisk. The raw data is given to you. Give the linear correlation coefficient, critical values, conclusion, regression equation from Statdisk, mean of y, and predict y for a given value of x.
- Use SPSS to create a correlation matrix (Analyze / Correlation / Bivariate). Indicate the variables that are correlated to the specified variable. Perform multiple regression and record the coefficients and p-values for each independent variable. Also record the r-square and adjusted r-square value. Determine which two variables are least significant by looking at the p-values and re-perform the multiple regression without those variables. Did the adjusted r-square increase or decrease by eliminating the variable? Determine whether a variable is normally distributed by performing a Kolmogorov Smirnov normality test.

# | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | Total |

Pts | 5 | 5 | 5 | 5 | 5 | 9 | 2 | 6 | 4 | 6 | 6 | 6 | 12 | 12 | 12 | 100 |