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# Chi-square = 2.85 Chi-square crit = 5.99

Chi-square = 2.85 Chi-square crit = 5.99 Achievement is unrelated to whether or not a child attended preschool.  2 as a test for goodness of fit. So far. . . . The expected frequencies that we have calculated come from the data They test rather or not two variables are related.

## Chi-square = 2.85 Chi-square crit = 5.99

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1. Chi-square = 2.85 • Chi-square crit = 5.99 • Achievement is unrelated to whether or not a child attended preschool.

2. 2 as a test for goodness of fit • So far. . . . • The expected frequencies that we have calculated come from the data • They test rather or not two variables are related

3. 2 as a test for goodness of fit • But what if: • You have a theory or hypothesis that the frequencies should occur in a particular manner?

4. Example • M&Ms claim that of their candies: • 30% are brown • 20% are red • 20% are yellow • 10% are blue • 10% are orange • 10% are green

5. Example • Based on genetic theory you hypothesize that in the population: • 45% have brown eyes • 35% have blue eyes • 20% have another eye color

6. To solve you use the same basic steps as before (slightly different order) • 1) State the hypothesis • 2) Find 2 critical • 3) Create data table • 4) Calculate the expected frequencies • 5) Calculate 2 • 6) Decision • 7) Put answer into words

7. Example • M&Ms claim that of their candies: • 30% are brown • 20% are red • 20% are yellow • 10% are blue • 10% are orange • 10% are green

8. Example • Four 1-pound bags of plain M&Ms are purchased • Each M&Ms is counted and categorized according to its color • Question: Is M&Ms “theory” about the colors of M&Ms correct?

9. Step 1: State the Hypothesis • H0: The data do fit the model • i.e., the observed data does agree with M&M’s theory • H1: The data do not fit the model • i.e., the observed data does not agree with M&M’s theory • NOTE: These are backwards from what you have done before

10. Step 2: Find 2 critical • df = number of categories - 1

11. Step 2: Find 2 critical • df = number of categories - 1 • df = 6 - 1 = 5 •  = .05 • 2 critical = 11.07

12. Step 3: Create the data table

13. Step 3: Create the data table Add the expected proportion of each category

14. Step 4: Calculate the Expected Frequencies

15. Step 4: Calculate the Expected Frequencies Expected Frequency = (proportion)(N)

16. Step 4: Calculate the Expected Frequencies Expected Frequency = (.30)(2081) = 624.30

17. Step 4: Calculate the Expected Frequencies Expected Frequency = (.20)(2081) = 416.20

18. Step 4: Calculate the Expected Frequencies Expected Frequency = (.20)(2081) = 416.20

19. Step 4: Calculate the Expected Frequencies Expected Frequency = (.10)(2081) = 208.10

20. Step 5: Calculate 2 O = observed frequency E = expected frequency

21. 2

22. 2

23. 2

24. 2

25. 2

26. 2 15.52

27. Step 6: Decision • Thus, if 2 > than 2critical • Reject H0, and accept H1 • If 2 < or = to 2critical • Fail to reject H0

28. Step 6: Decision 2 = 15.52 2 crit = 11.07 • Thus, if 2 > than 2critical • Reject H0, and accept H1 • If 2 < or = to 2critical • Fail to reject H0

29. Step 7: Put answer into words • H1: The data do not fit the model • M&M’s color “theory” did not significantly (.05) fit the data

30. Practice • Among women in the general population under the age of 40: • 60% are married • 23% are single • 4% are separated • 12% are divorced • 1% are widowed

31. Practice • You sample 200 female executives under the age of 40 • Question: Is marital status distributed the same way in the population of female executives as in the general population ( = .05)?

32. Step 1: State the Hypothesis • H0: The data do fit the model • i.e., marital status is distributed the same way in the population of female executives as in the general population • H1: The data do not fit the model • i.e., marital status is not distributed the same way in the population of female executives as in the general population

33. Step 2: Find 2 critical • df = number of categories - 1

34. Step 2: Find 2 critical • df = number of categories - 1 • df = 5 - 1 = 4 •  = .05 • 2 critical = 9.49

35. Step 3: Create the data table

36. Step 4: Calculate the Expected Frequencies

37. Step 5: Calculate 2 O = observed frequency E = expected frequency

38. 2 19.42

39. Step 6: Decision • Thus, if 2 > than 2critical • Reject H0, and accept H1 • If 2 < or = to 2critical • Fail to reject H0

40. Step 6: Decision 2 = 19.42 2 crit = 9.49 • Thus, if 2 > than 2critical • Reject H0, and accept H1 • If 2 < or = to 2critical • Fail to reject H0

41. Step 7: Put answer into words • H1: The data do not fit the model • Marital status is not distributed the same way in the population of female executives as in the general population ( = .05)

42. Practice • Is there a significant ( = .05) relationship between gender and a persons favorite Thanksgiving “side” dish? • Each participant reported his or her most favorite dish.

43. Results Side Dish Gender

44. Step 1: State the Hypothesis • H1: There is a relationship between gender and favorite side dish • Gender and favorite side dish are independent of each other

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