Lectures

Categorical Data Analysis

by David Spade, PhD
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    About the Lecture

    The lecture Categorical Data Analysis by David Spade, PhD is from the course Statistics Part 2. It contains the following chapters:

    • Categorical Data Analysis
    • The Chi-Square Table
    • Astrology Example
    • Natural Question
    • Pitfalls to Avoid

    Included Quiz Questions

    1. The goodness-of-fit test is appropriate for sample means.
    2. The goodness-of-fit test is appropriate if data must are counts.
    3. The goodness-of-fit test is appropriate if the data come from a random sample.
    4. The goodness-of-fit test is appropriate when we expect to see at least five individuals in each cell.
    1. The null hypothesis is that the population distribution of one categorical variable is the same for each level of the other categorical variable.
    2. The null hypothesis is that the population proportions are the same for each cell.
    3. The null hypothesis is that the population distribution of one categorical variable is different for each level of the other categorical variable.
    4. The null hypothesis is that the population proportions are different in each cell.
    1. The null hypothesis is that two categorical variables are independent.
    2. The null hypothesis is that two categorical variables have a linear relationship.
    3. The null hypothesis is that the distribution of one categorical variable is the same for each level of the other categorical variable
    4. The null hypothesis is that the population proportions are the same in each cell.
    1. The population distribution must be normal.
    2. The data in the table are counts.
    3. The individuals in the study are independent.
    4. The sample size must be less than 10% of the population of interest for each categorical variable.
    1. A rejection of the hypothesis of independence between two categorical variables means that the change in one variable causes the change in the other.
    2. The chi-squared methods can not be used for data that are not counts.
    3. Large samples are not necessarily good for categorical data analysis because the degrees of freedom do not increase with sample size.
    4. The goodness-of-fit test, the test for homogeneity, and the test for independence are all based on the χ² distribution.

    Author of lecture Categorical Data Analysis

     David Spade, PhD

    David Spade, PhD


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