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8.13 : Determination of Expected Frequency

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

From Chapter 8:

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8.13 : Determination of Expected Frequency

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8.1 : Distributions to Estimate Population Parameter

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8.2 : Degrees of Freedom

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8.3 : Student t Distribution

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8.4 : Choosing Between z and t Distribution

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8.5 : Chi-square Distribution

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8.6 : Finding Critical Values for Chi-Square

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8.7 : Estimating Population Standard Deviation

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8.8 : Goodness-of-Fit Test

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8.9 : Expected Frequencies in Goodness-of-Fit Tests

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8.10 : Contingency Table

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8.11 : Introduction to Test of Independence

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8.12 : Hypothesis Test for Test of Independence

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8.14 : Test for Homogeneity

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8.15 : F Distribution

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