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# Two-way tables review

Two-way tables organize data based on two categorical variables.

## Two way frequency tables

Two-way frequency tables show how many data points fit in each category.
Here's an example:
PreferenceMaleFemale
Prefers dogs3622
Prefers cats826
No preference26
The columns of the table tell us whether the student is a male or a female. The rows of the table tell us whether the student prefers dogs, cats, or doesn't have a preference.
Each cell tells us the number (or frequency) of students. For example, the 36 is in the male column and the prefers dogs row. This tells us that there are 36 males who preferred dogs in this dataset.
Notice that there are two variables—gender and preference—this is where the two in two-way frequency table comes from.
Want a review of making two-way frequency tables? Check out this video.
Want to practice making frequency tables? Check out this exercise.
Want to practice reading frequency tables? Check out this exercise

## Two way relative frequency tables

Two-way relative frequency tables show what percent of data points fit in each category. We can use row relative frequencies or column relative frequencies, it just depends on the context of the problem.
For example, here's how we would make column relative frequencies:
Step 1: Find the totals for each column.
PreferenceMaleFemale
Prefers dogs3622
Prefers cats826
No preference26
Total4654
Step 2: Divide each cell count by its column total and convert to a percentage.
PreferenceMaleFemale
Prefers dogsstart fraction, 36, divided by, 46, end fraction, approximately equals, 78, percentstart fraction, 22, divided by, 54, end fraction, approximately equals, 41, percent
Prefers catsstart fraction, 8, divided by, 46, end fraction, approximately equals, 17, percentstart fraction, 26, divided by, 54, end fraction, approximately equals, 48, percent
No preferencestart fraction, 2, divided by, 46, end fraction, approximately equals, 4, percentstart fraction, 6, divided by, 54, end fraction, approximately equals, 11, percent
Totalstart fraction, 46, divided by, 46, end fraction, equals, 100, percentstart fraction, 54, divided by, 54, end fraction, equals, 100, percent
Notice that sometimes your percentages won't add up to 100, percent even though we rounded properly. This is called round-off error, and we don't worry about it too much.
Two-way relative frequency tables are useful when there are different sample sizes in a dataset. In this example, more females were surveyed than males, so using percentages makes it easier to compare the preferences of males and females. From the relative frequencies, we can see that a large majority of males preferred dogs left parenthesis, 78, percent, right parenthesis compared to a minority of females left parenthesis, 41, percent, right parenthesis.
Want a review of making two-way relative frequency tables? Check out this video.
Want to practice making relative frequency tables? Check out this exercise.
Want to practice reading relative frequency tables? Check out this exercise

## Want to join the conversation?

• Why would someone have the columns add up to 100% instead of having the rows add up to 100%?
• It depends on what you would like to compare. In the example above, if you want to know "Of dog lovers, what proportion are male?" Adding the rows up to 100 would be appropriate. If you wanted to know "Of males, what proportion are dog lovers?" adding the columns up to 100 would be more appropriate.
• even tough I am an experienced engineer, i had to spend some time (more than 4 repeats to get 3/4 score) on the last "Trends in categorical data" practice test. I had to learn tendencies by trial and error; "is it probable? is it more probable?".
and also when to use row or column percentages was a bit dependent on the language itself: "dog lovers among men!" or "men among dog lovers!"
It would be better to give extra information about these during the course to let newcomers learn better.
• I am by no means close to any of you, but yes I agree that language at times is either confusing or too concise, or perhaps is just my inexperience with the material.
• “From the relative frequencies, we can see that a large majority of males preferred dogs (78%) compared to a minority of females (41%)”

I still don’t understand what can and cannot be compared. Since this a column relative frequency table shouldn’t you only be allowed to compare data points that are in the COLUMN? How can you here compare between those two genders as the above quoted statement does?
Shouldn’t you only be able to say that males are more likely to prefer dogs over cats and that females are more likely to prefer cats over dogs?
• I am trying to analyze a two-way table that involves data in the form of scores out of 10 rather than frequencies. How could I analyze this with conditional or marginal distributions?