Transcription
Today we're going to discuss two-way tables. Another name for two-way tables are contingency tables. They allow you to easily organize and analyze bivariate data, otherwise known as two-variable data.
Marginal frequency distribution analyzes a variable using data in terms of margin, either column or row of a two-way table. Here's an example of a two-way table. We have male and female versus a sports. So, what is your favorite sport to play? A sample of 200 American high school students provided the following data.
Let's say we want to calculate the marginal distribution for baseball or softball. So, our focus is on baseball. We're going to take the total that play baseball and take that out of the grand total of people that provided the data, 200. So, 52 out of 200 gives us our marginal distribution of 26. 26 of this distribution prefer baseball or softball.
Joint frequency distribution joins two variables in the center of a two-way table. So, what we mean is, if we're looking at the same table with sports and gender, what does this 13 mean? What is the joint frequency distribution of the cell that contains, or describe it, of the cell that contains 13? So, 13 means that 13, we're joining male and golf. So, 13 males chose golf out of the 200 total.
Conditional frequency distribution places a restriction on the distribution. Look for keywords such as "if" followed by the context clues. So, once again, using our sports and gender related to male and females, if the student is female, so the condition that they are female, what is the conditional probability for basketball being the favorite sport? So, being female, total females is 115. What is the conditional probability for basketball being the favorite sport? So, basketball is 36. So, our condition is going to say there is 36 out of the 115 female students, which ends up being about 31.3 percent. And that's our lesson on two-way tables.