1. Two-Way Tables Two-way tables come about when we are interested in the relationship between two categorical variables. –One of the variables is the row variable. –The other is the column variable. –The combination of a row variable and a column variable is a cell.

2. Two-Way Tables Example: Row variable Column variable Column Totals Row Totals Overall Total Cells

3. Two-Way Tables To test whether or not there is a relationship between the row variable and the column variable we use the chi-square statistic (X 2 ), which can be calculated in the computer. The null hypothesis (H 0 ) is no relationship among the two variables, i.e. the variables are independent. The alternative hypothesis (H a ) is that there is a relationship, i.e. the variables are not independent. For 2x2 Tables, we require that all four expected cell counts be 5 or more. For tables larger than 2x2, we will use this approximation whenever the average of the expected counts is 5 or more and the smallest expected count is 1 or more.

4. Two-Way Tables The degrees of freedom for the chi-square test are (#rows – 1)*(#columns – 1). For the example we have (2-1)(2-1) = 1. The P-value is determined by looking in Table F.

5. Two-Way Tables A comparison of the proportion of “successes” in two populations leads to a 2x2 table. We can compare two population proportions either by the chi-square test or by the two-sample z test from section 8.2 These tests always give exactly the same result. The chi-square statistic is equal to the square of the z statistic and X 2 (1) critical values are equal to the squares of the corresponding N(0,1) critical values. Advantage of the z test: We can test either one-sided or two-sided alternatives Chi-square test always tests the two-sided alternative Advantage of chi-square: We can compare more than two populations z-Test compares only two populations

6. Two-Way Tables The P-value is thus found as follows:

7. Two-Way Tables Analysis in SPSS gives us: We are interested in this row: The P-value is 0.103. Because this is larger than 0.05 we fail to reject H 0 and conclude there is no significant relationship between gender and tomato enjoyment.

8. Link between Diabetes and Heart Disease? Background: Contradictory opinions: 1. A diabetic’s risk of dying after a first heart attack is the same as that of someone without diabetes. There is no link between diabetes and heart disease. Vs. 2. Diabetes takes a heavy toll on the body and diabetes patients often suffer heart attacks and strokes or die from cardiovascular complications at a much younger age. So we use hypothesis test based on the latest data to see what’s the right conclusion. There are a total of 5167 managed-care patients, among which 1131 patients are non- diabetics and 4036 are diabetics. Among the non-diabetic patients, 42% of them had their blood pressure properly controlled (therefore it’s 475 of 1131). While among the diabetic patients only 20% of them had the blood pressure controlled (therefore it’s 807 of 4036).

9. Link between Diabetes and Heart Disease? Data: Diabetes 1=Not have diabetes, 2=Have Diabetes Control 1=Controlled, 2=Uncontrolled

10. Link between Diabetes and Heart Disease? Data ControlledUncontrolledTotal Non-diabetes4756561131 Diabetes80732294036 Total128238855167

11. Link between Diabetes and Heart Disease?

12. Hypothesis test : 1.) Ho: There is no link between diabetes and heart disease. (There is no relationship between diabetes and heart disease. Diabetes and heart disease are independent.) 2.)Ha: There is link between diabetes and heart disease. (There is a relationship between diabetes and heart disease. Diabetes and heart disease are dependent.) 3.) Assume a significance level of.05

13. Link between Diabetes and Heart Disease? SPSS Output

14. Link between Diabetes and Heart Disease? 4.)The computer gives us a Chi-Square Statistic of 229.268 5.)The computer gives us a p-value of.000 6.) Because our p-value is less than alpha, we would reject the null hypothesis. 7.) There IS sufficient evidence that there is link between diabetes and heart disease.

15. Is there a relationship between exposure to R- rated movies and adolescent smoking? The study attempted to examine the relationship between exposure to R-Rated movies and smoking habits among adolescents. Smoking in R-rated movies is higher than any other movie-rating category. Therefore, the objective of this study was to determine if an association existed between parental restrictions on movies and adolescent cigarette use. SmokingNon-smokingTotal Complete Restriction14701715 Partial Restriction28821142402 No Restriction4999281427 Total80137434544

16. Is there a relationship between exposure to R- rated movies and adolescent smoking?

17. Hypothesis Test 1.)Ho: There is no relationship between exposure to R-rated movies and tobacco use among adolescents 2.)Ha: There is a relationship between the occurrence of tobacco use and the exposure to R- rated movies among adolescents 3.)alpha=.05 4.) The computer gives us a chi-square test statistic of 469.003

18. Is there a relationship between exposure to R-rated movies and adolescent smoking? SPSS Output

19. Is there a relationship between exposure to R-rated movies and adolescent smoking? 5.) The computer output gives us a p-value that is.000 6.) Decision Rule: –If p-value<alpha, we reject Ho –If p-value >alpha, we fail to reject Ho Because our p-value is less than our significance level (alpha), we would reject the null hypothesis 7.) Because we rejected Ho, we can conclude that there IS significant evidence that a relationship between exposure to r-rated movies and adolescent tobacco use exists.