1. Chi Squared Test for Independence

2. Hypothesis Testing Null Hypothesis, – States that there is no significant difference between two (population) parameters ie. Two numbers are the same Alternative Hypothesis, – States that there is a significant difference between two (population) parameters Ie. Two numbers are different

3. Chi-Squared Test with GDC A researcher conjectures that seat belt usage, for drivers, is related to gender. Her data gathered is in the frequency distribution chart below. Construct a chi-squared hypothesis test to determine if there is enough evidence to support the researcher’s conjecture. Seat Belt Usage GenderYesNo Female5025 Male4045

4. Chi-Squared Steps Step 1: Write the null and alternative hypothesis

5. Chi-Squared Steps Step 2: Find the p-value – P-value is the probability value of evidence against the null hypothesis. Smaller the number the more chance that the two numbers in question really are significantly different GDC: 2 nd Matrix -- Edit – 2 x 2 – Enter Data P-Value: Stat – Tests -- -Test -- Calculate

6. Chi-Squared Steps Step 3: Select an alpha level α – Alpha level represents the chance of making a mistake, the mistake that you reject the null hypothesis when it is actually true Common Alpha levels are 1%, 5%, 10% Select α =.01 for this example

7. Chi-Squared Steps Step 4: A) Compare the p-value to the alpha level – P – value > alpha level B) Compare the against the critical value

8. Chi-Squared Steps Step 5: Interpret the comparison A) If the p-value > alpha level or < CV, DO NOT reject the null hypothesis B) If the p-value CV, REJECT the null hypothesis and accept the alternative hypothesis