Categorical Data Analysis von David Spade, PhD

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Über den Vortrag

Der Vortrag „Categorical Data Analysis“ von David Spade, PhD ist Bestandteil des Kurses „Statistical analysis (WUM - 2nd year Research Methodology)“. Der Vortrag ist dabei in folgende Kapitel unterteilt:

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

Quiz zum Vortrag

  1. The goodness-of-fit test is appropriate for sample means.
  2. The goodness-of-fit test is appropriate if data medians must count.
  3. The goodness-of-fit test is appropriate if the data comes from a random sample.
  4. The goodness-of-fit test is appropriate when we expect to see at least five individuals in each cell.
  5. The goodness-of-fit test is appropriate when we want to evaluate the difference between observed and expected values.
  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.
  5. The null hypothesis is that the population distribution is the same for at least one different level of the other categorical variables.
  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.
  5. The null hypothesis is that two categorical variables have a logarithmic relationship.
  1. The population distribution must be normal.
  2. Patients are randomized if appropriate.
  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.
  5. The patients are likely to be representative of the population.
  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 numbered.
  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.
  5. Goodness-of-fit test data should be spread out in a random manner.
  1. 5.3
  2. 5.1
  3. 5.2
  4. 5.4
  5. 5.5
  1. 101.2
  2. 91.2
  3. 111.2
  4. 121.2
  5. 131.2
  1. Row variable and column variable are independent
  2. Row variable and column variable are dependent
  3. Row variable and column variable are associated
  4. Row variable and column variable are correlated
  5. Row variable and column variable are ambiguous

Dozent des Vortrages Categorical Data Analysis

 David Spade, PhD

David Spade, PhD

Dr. David Spade is an Assistant Professor of Mathematical Sciences and Statistics at the University of Wisconsin-Milwaukee and holds a courtesy appointment as an Assistant Professor of Statistics at the University of Missouri-Kansas City, USA.
He obtained his MS in Statistics in 2010 and then completed his PhD in Statistics from Ohio State University in 2013.
An experienced mathemathics instructor, Dr. Spade has been teaching diverse statistics courses from the introductory to the graduate level since 2007.
Within Lecturio, he teaches courses on Statistics.


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