- 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)?
- Linear regression problem. Look at a scatter plot and identify the linear correlation as strong negative, moderate negative, none, moderate positive, or strong positive. Then, summary information is given - do not go back and change your answer to the first part. Write the null and alternative hypotheses. Give the value of the test statistic and critical values. Write the decision and conclusion. Give the percent of the total variation that can be explained by the regression equation. Estimate a value of the dependent variable for a specific value of the independent variable.
- Linear regression problem. Look at a scatter plot and identify the linear correlation as strong negative, moderate negative, none, moderate positive, or strong positive. Then, summary information is given - do not go back and change your answer to the first part. Write the null and alternative hypotheses. Give the value of the test statistic and critical values. Write the decision and conclusion. Give the percent of the total variation that can be explained by the regression equation. Estimate a value of the dependent variable for a specific value of the independent variable.
- Contingency Table. Write the null and alternative hypotheses. Find one (not all of them) expected frequency. Identify the degrees of freedom and the critical value. Calculate the test statistic. Write the decision and conclusion.
- Know what happens to the linear correlation coefficient when the data is manipulated. Three parts.
- Look at a multiple regression output. Pick the three most significant independent variables (The ones with the smallest p-values or largest t-values [called t statistics on the output]). Pick the three least significant independent variables (The ones with the largest p-values or smallest t-values [called t statistics on the output]).
- Know what happens to the test statistic of a multinomial experiment when the data is manipulated. Two parts.
- Know what happens to the test statistic of a contingency table when the data is manipulated. Two parts.
- Work a chi-square goodness of fit problem using your TI calculator. Find the test statistic (using the calculator). Look up the critical value (in the Chi-square table). Write the decision. Fill in the blanks to complete the conclusion.
- Work a linear regression problem using your TI calculator. Find the linear correlation coefficient. Test to see if there is significant linear correlation (use Table A6 to find the critical value). Write the regression equation (Remember, if you fail to reject in step 2, then the regression equation is y-hat equals y-bar).

# |
1 |
2 |
3 |
4 |
5 |
6 |
7 |

pts |
7 | 6 | 6 | 5 | 16 | 16 | 14 |

# |
8 |
9 |
10 |
11 |
12 |
13 |
Total |

pts |
3 | 6 | 2 | 2 | 8 | 6 | 97 |