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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual probabilities in the contingency table. It is also important to note that the expected frequency for each column must be at least 5. The expected frequencies are then used to calculate the chi-square value and P-value.

Tags
Expected FrequencyContingency TableObserved FrequenciesIndependenceStatistical ProbabilityChi square ValueP valueIndependent VariablesEvent ProbabilitiesData Analysis

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8.13 : Determination of Expected Frequency

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8.1 : 모집단 모수를 추정하기 위한 분포

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8.2 : 자유도

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8.3 : 스튜던트 t 분포

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8.4 : z 분포와 t 분포 중에서 선택

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8.5 : 카이제곱 분포

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8.6 : Chi-square에 대한 임계값 찾기

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8.7 : 모집단 표준 편차 추정

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8.8 : 적합도 테스트

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8.9 : 적합도 검정에서 예상되는 빈도

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8.10 : 분할표

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8.11 : 독립성 시험 소개

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8.12 : 독립성 검정을 위한 가설 검정

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8.14 : 균질성 테스트

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8.15 : F 분포

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