# Contingency Tables von David Spade, PhD

### Über den Vortrag

Der Vortrag „Contingency Tables“ von David Spade, PhD ist Bestandteil des Kurses „Statistics Part 1“. Der Vortrag ist dabei in folgende Kapitel unterteilt:

• Contingency Tables
• Marginal Distributions
• Checking the Work
• Conditional Distributions
• Independence of Two Categorical Variables

### Quiz zum Vortrag

1. A contingency table describes the distribution of two categorical variables at the same time.
2. A contingency table describes the distribution of a categorical variable.
3. A contingency table describes the distribution of two quantitative variables at the same time.
4. A contingency table describes the distribution of a categorical variable over time.
5. A contingency table describes the distribution of a quantitative variable over time.
1. The marginal distribution of the column variable can be obtained by looking at the column totals of the table.
2. The marginal distribution of the row variable can be obtained by looking at the column totals of the table.
3. The marginal distribution of the column variable can be obtained by looking at the row totals of the table.
4. The table shows only what the distribution of one of the two variables looks like.
5. The table shows only what the distribution of one of the three variables looks like.
1. We only talk about relative frequencies.
2. We only talk about absolute frequencies.
3. Here, we only pay attention to the observations that take the given value of the first variable.
4. Here, we cannot complete a check on our work by making sure that the percentages we came up with add up to 100%.
5. We consider the importance of both relative and absolute frequencies.
1. Two categorical variables are independent if the conditional distribution of one variable is the same for all categories of the other variable.
2. Two categorical variables are dependent if the conditional distribution of one variable is the same for all categories of the other variable.
3. If two variables are dependent, we need to show that all of the conditional distributions are different.
4. The easiest way to determine dependence is to look at the conditional distribution of one variable for each category of the other variables.
5. If two variables are dependent, we need to show that all of the conditional distributions are opposite.
1. Simpson’s Paradox describes a situation when a trend appears for one group of data but disappears when groups of data are combined.
2. Simpson’s Paradox occurs when the data comes out in a way that you do not expect.
3. Simpson’s Paradox occurs when the marginal distributions of each of the two variables differ.
4. Simpson’s Paradox occurs when the conditional distribution of one variable given one value of the other differs from the conditional variable of one variable given another value of the second variable.
5. Simpson’s Paradox describes a trend that only appears when data are combined.
1. Two-way table
2. Two-times table
3. Two-cross table
4. Two-fold table
5. Three-way table
1. It can be obtained by adding up all of the entries in that row.
2. It can be obtained by multiplying all of the entries in that row.
3. It can be obtained by dividing all of the entries in that row.
4. It can be obtained by squaring all of the entries in that row.
5. It can be obtained by adding all of the entries in other rows.
1. 100%
2. 70%
3. 80%
4. 90%
5. 0%
1. 100%
2. 70%
3. 80%
4. 90%
5. 0%
1. Simpson's Paradox may occur when the conditional relative frequencies within each variable differ from the overall results.
2. Simpson's Paradox may occur when the marginal relative frequencies within each variable differ from the overall results.
3. Simpson's Paradox may occur when the conditional relative frequencies within each variable are the same as the overall results.
4. Simpson's Paradox may occur when the absolute frequencies within each variable differ from the overall results.
5. Simpson's Paradox may occur when the absolute frequencies within each variable are the same as the overall results.

### Dozent des Vortrages Contingency Tables

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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like
von Lourdes K. am 18. Oktober 2017 für Contingency Tables

It is better than that I watched in other web site. I really recommend this lecture.

Great lecture
von Fungai M. am 25. Juni 2017 für Contingency Tables

Clear explanations that makes understanding of concepts easy. Relevant examples too