Unit 9- BUSN 3000

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University Of Georgia *

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3000

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Statistics

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Apr 3, 2024

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BUSN 3000 Unit 9: Models for Categorical Data Unit 9: Models for Categorical Data – Chi-squared Tests Goodness-of-fit test - one categorical variable, more than two categories Example: Where do BUSN 3000 students live? Is the distribution the same as the UGA population? Housing UGA population Observed counts (sample data) Expected counts (if Ho true) Off-campus (not UGA-owned or affiliated) 66% 22 25.08 Fraternity or Sorority housing 5.5% 14 2.09 On-campus dorms or other UGA-owned or affiliated housing 28.5% 2 10.83 Total 100% 38 38 Could these observed counts have occurred just by chance if the distribution of housing choices for BUSN 3000 students were really the same as the UGA population? H 0 : p off campus = 0.66 , p Greek = 0.055 , p on campus = 0.285 BUSN 3000 has same distribution as UGA population H A : at least one p different from what’s given The chi-squared ( χ 2 ) test statistic compares the observed counts to the expected counts. χ 2 = ( Observed count Expected count ) 2 Expected count = (22-25.08)^2/25.08 + (14-2.09)^2/2.09 + (2- 10.83)^2/10.83 When the observed counts are somewhat close to the expected counts… χ 2 is large / small and the evidence against the null hypothesis is strong / weak. When the observed counts differ greatly from the expected counts… χ 2 is large / small and the evidence against the null hypothesis is strong / weak. 1
BUSN 3000 Unit 9: Models for Categorical Data Student housing (continued) H 0 : p off campus = 0.66 , p Greek = 0.055 , p on campus = 0.285 H A : At least one p is different from these values. P-values for chi-squared tests How large must the chi-squared statistic be to convince us that the null hypothesis is not true? df = number of categories – 1 (always choose second option for chi squared) If the distribution of housing choices for BUSN students were really _____ the same as (Ho true) ______UGA students overall, sample results like ours would be… unlikely For α = 0.05 , state your conclusion in context. o There is sufficient / insufficient evidence to conclude that distribution of housing choices for BUSN students is ______ different from _______ the overall UGA population. Conducting a goodness-of-fit test using Analyze – Distribution in JMP 2
BUSN 3000 Unit 9: Models for Categorical Data Using residuals as a follow-up analysis Why is this follow-up necessary? at least one p is different form what is given (which ones? And by how much?) The residuals show which individual categories have large differences between observed and expected counts. A positive residual means observed count is ____ larger ____ than expected. A negative residual means observed count is ___ smaller ____ than expected. Values less than -2 or greater than 2 are unusual. Housing UGA population Observed counts Expected counts Deviation (obs-exp) Standardized residual Off-campus (not UGA-owned or affiliated) 66% 22 25.08 -3.08 -0.615 Fraternity or Sorority housing 5.5% 14 2.09 11.91 8.238 On-campus dorms or other UGA-owned or affiliated housing 28.5% 2 10.83 -8.83 -2.683 Total 100% 38 38 0 Checking conditions for a chi-squared test 1. Random – random selection means generalization to population (our sample may not be representative of BUSN 3000 population) Random Assignment means causation 2. Sample size large enough – expected counts must all be at least 5 (sample size condition is not met because or smallest expected value is 2.09) Two-way tables and segmented bar graphs 3 residual = observed count expected count expectedcount
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