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Chap 16-1 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chapter 16 Goodness-of-Fit Tests and Contingency Tables Statistics for Business and Economics 6 th Edition

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 16-2 Chapter Goals After completing this chapter, you should be able to: Use the chi-square goodness-of-fit test to determine whether data fits a specified distribution Perform tests for normality Set up a contingency analysis table and perform a chi-square test of association

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 16-3 Does sample data conform to a hypothesized distribution? Examples: Do sample results conform to specified expected probabilities? Are technical support calls equal across all days of the week? (i.e., do calls follow a uniform distribution?) Do measurements from a production process follow a normal distribution? Chi-Square Goodness-of-Fit Test

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 16-4 Are technical support calls equal across all days of the week? (i.e., do calls follow a uniform distribution?) Sample data for 10 days per day of week: Sum of calls for this day: Monday 290 Tuesday 250 Wednesday 238 Thursday 257 Friday 265 Saturday 230 Sunday 192 Chi-Square Goodness-of-Fit Test (continued) = 1722

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 16-5 Logic of Goodness-of-Fit Test If calls are uniformly distributed, the 1722 calls would be expected to be equally divided across the 7 days: Chi-Square Goodness-of-Fit Test: test to see if the sample results are consistent with the expected results

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 16-6 Observed vs. Expected Frequencies Observed O i Expected E i Monday Tuesday Wednesday Thursday Friday Saturday Sunday TOTAL1722

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 16-7 Chi-Square Test Statistic The test statistic is where: K = number of categories O i = observed frequency for category i E i = expected frequency for category i H 0 : The distribution of calls is uniform over days of the week H 1 : The distribution of calls is not uniform

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 16-8 The Rejection Region Reject H 0 if H 0 : The distribution of calls is uniform over days of the week H 1 : The distribution of calls is not uniform 0 2 Reject H 0 Do not reject H 0 (with k – 1 degrees of freedom) 2

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap 16-9 Chi-Square Test Statistic H 0 : The distribution of calls is uniform over days of the week H 1 : The distribution of calls is not uniform 0 =.05 Reject H 0 Do not reject H 0 2 k – 1 = 6 (7 days of the week) so use 6 degrees of freedom: 2.05 = Conclusion: 2 = > 2 = so reject H 0 and conclude that the distribution is not uniform

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap Goodness-of-Fit Tests, Population Parameters Unknown Idea: Test whether data follow a specified distribution (such as binomial, Poisson, or normal) without assuming the parameters of the distribution are known Use sample data to estimate the unknown population parameters

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap Suppose that a null hypothesis specifies category probabilities that depend on the estimation (from the data) of m unknown population parameters The appropriate goodness-of-fit test is the same as in the previously section except that the number of degrees of freedom for the chi-square random variable is Where K is the number of categories Goodness-of-Fit Tests, Population Parameters Unknown (continued)

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap Test of Normality The assumption that data follow a normal distribution is common in statistics Normality was assessed in prior chapters Normal probability plots (Chapter 6) Normal quintile plots (Chapter 8) Here, a chi-square test is developed

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap Test of Normality Two population parameters can be estimated using sample data: For a normal distribution, Skewness = 0 Kurtosis = 3 (continued)

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap Bowman-Shelton Test for Normality Consider the null hypothesis that the population distribution is normal The Bowman-Shelton Test for Normality is based on the closeness the sample skewness to 0 and the sample kurtosis to 3 The test statistic is as the number of sample observations becomes very large, this statistic has a chi-square distribution with 2 degrees of freedom The null hypothesis is rejected for large values of the test statistic

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap Bowman-Shelton Test for Normality The chi-square approximation is close only for very large sample sizes If the sample size is not very large, the Bowman- Shelton test statistic is compared to significance points from text Table 16.7 (continued) Sample size N 10% point 5% pointSample size N 10% point 5% point

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap The average daily temperature has been recorded for 200 randomly selected days, with sample skewness and kurtosis Test the null hypothesis that the true distribution is normal From Table 16.7 the 10% critical value for n = 200 is 3.48, so there is not sufficient evidence to reject that the population is normal Example: Bowman-Shelton Test for Normality

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap Contingency Tables Used to classify sample observations according to a pair of attributes Also called a cross-classification or cross- tabulation table Assume r categories for attribute A and c categories for attribute B Then there are (r x c) possible cross-classifications

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap r x c Contingency Table Attribute B Attribute A12...CTotals 1 2. r Totals O 11 O 21. O r1 C 1 O 12 O 22. O r2 C 2 …………………………………… O 1c O 2c. O rc C c R1R2...RrnR1R2...Rrn

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap Test for Association Consider n observations tabulated in an r x c contingency table Denote by O ij the number of observations in the cell that is in the i th row and the j th column The null hypothesis is The appropriate test is a chi-square test with (r-1)(c-1) degrees of freedom

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap Test for Association Let R i and C j be the row and column totals The expected number of observations in cell row i and column j, given that H 0 is true, is A test of association at a significance level is based on the chi-square distribution and the following decision rule (continued)

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap Contingency Table Example H 0 : There is no association between hand preference and gender H 1 : Hand preference is not independent of gender Left-Handed vs. Gender Dominant Hand: Left vs. Right Gender: Male vs. Female

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap Contingency Table Example Sample results organized in a contingency table: Gender Hand Preference LeftRight Female Male Females, 12 were left handed 180 Males, 24 were left handed sample size = n = 300: (continued)

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap Logic of the Test If H 0 is true, then the proportion of left-handed females should be the same as the proportion of left-handed males The two proportions above should be the same as the proportion of left-handed people overall H 0 : There is no association between hand preference and gender H 1 : Hand preference is not independent of gender

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap Finding Expected Frequencies Overall: P(Left Handed) = 36/300 = Females, 12 were left handed 180 Males, 24 were left handed If no association, then P(Left Handed | Female) = P(Left Handed | Male) =.12 So we would expect 12% of the 120 females and 12% of the 180 males to be left handed… i.e., we would expect (120)(.12) = 14.4 females to be left handed (180)(.12) = 21.6 males to be left handed

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap Expected Cell Frequencies Expected cell frequencies: Example: (continued)

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap Observed vs. Expected Frequencies Observed frequencies vs. expected frequencies: Gender Hand Preference LeftRight Female Observed = 12 Expected = 14.4 Observed = 108 Expected = Male Observed = 24 Expected = 21.6 Observed = 156 Expected =

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap The Chi-Square Test Statistic where: O ij = observed frequency in cell (i, j) E ij = expected frequency in cell (i, j) r = number of rows c = number of columns The Chi-square test statistic is:

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap Observed vs. Expected Frequencies Gender Hand Preference LeftRight Female Observed = 12 Expected = 14.4 Observed = 108 Expected = Male Observed = 24 Expected = 21.6 Observed = 156 Expected =

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap Contingency Analysis 2.05 = Reject H 0 = 0.05 Decision Rule: If 2 > 3.841, reject H 0, otherwise, do not reject H 0 Do not reject H 0 Here, 2 = < 3.841, so we do not reject H 0 and conclude that gender and hand preference are not associated

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Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chap Chapter Summary Used the chi-square goodness-of-fit test to determine whether sample data match specified probabilities Conducted goodness-of-fit tests when a population parameter was unknown Tested for normality using the Bowman-Shelton test Used contingency tables to perform a chi-square test for association Compared observed cell frequencies to expected cell frequencies

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