1 Describing Categorical Data Here we study ways of describing a variable that is categorical.

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Describing Categorical Data

Here we study ways of describing a variable that is categorical.

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Say 50 people you know purchased a soft drink from a machine recently. A variable of interest might be the BRAND PURCHASED. Say the brands are made up of the 5 soft drinks Coke Classic, Diet Coke, Dr. Pepper, Pepsi-Cola, and Sprite (of course there are more varieties of soft drinks, but this is an illustrative example.)

Here each specific brand represents a different value on the variable brand purchased. Each specific brand represents an nonoverlapping class – each specific class represents a mutually exclusive category.

Here the variable brand purchased is a categorical or qualitative variable - values of the variable represent categories.

One thing that makes sense to do is ask each of the 50 people what they purchased. Then we could count the number of people who purchased Coke Classic and the others. The total number of people of the 50 who purchased Coke Classic would be the frequency.

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Soft Drink Frequency Relative Frequency

Percent Frequency

Coke Classic

19 0.38 38

Diet Coke 8 0.16 16

Dr. Pepper 5 0.1 10

Pepsi-Cola 13 0.26 26

Sprite 5 0.1 10

Total 50 1.00 100

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The first two columns on the previous screen, the Soft Drink and Frequency columns, make up what is called a frequency distribution. It is a tabular summary of data showing the number, or frequency, of items in each of several nonoverlapping classes.

The third column shows the relative frequency. We need the second column to create the third. To get the relative frequency in each row

take the frequency in that row and divide by the total frequency.

The fourth column shows the percent frequency. The fourth column equals the third column multiplied by 100.

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Do you know why we put information in columns? Because then we can call’um as we see’um. Sorry:)

So, the frequency, relative frequency and percent frequency distributions are different ways of summarizing information about a categorical variable.

Notes about our table.

1) The total, or sum, of the frequency column is equal to the number of observations, sometimes called n, in general.

2) The total, or sum, of the relative frequency column is equal to 1.

3) The total, or sum, of the percent frequency column is equal to 100 (sometimes it may be a little off due to rounding of decimal places).

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In our example here we had 50 people and we asked what soft drink they purchased. Studies occur that have thousands of people and they are asked several questions. Using a computer can help in the counting of responses.

Bar Graphs

Bar graphs just put the frequency, relative frequency and percent frequency distributions into visual form. The form is a graph with certain properties.

The horizontal axis does not have numbers on it and the axis represents the categories. In our soft drink example we would put each brand in a different location on the axis.

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Imagine you have a piece of construction paper that is red. Do you remember way back when in school you would cut strips of paper and then curl the paper with the scissors? Well, we will not need to curl the paper here!

I mention this silly example because I want you to think about cutting strips that are one inch wide. The height of each strip would then represent the frequency, relative frequency or percent frequency on the variable. You would tape each strip onto the graph above each category. (You could also put the bars sideways.)

So the vertical axis, or height, in the bar graphs is either the frequency, relative frequency or percent frequency distributions.

In constructing the bar graph on a qualitative variable a space is left between each bar to help us remember we have a qualitative a variable.

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RuralSuburbanUrban

Pe

rce

nt

50

40

30

20

10

0

This is an example of what a percent frequency graph would look like. The variable is “what is the type of area in which you live” and the height of each bar is the percent frequency. (See how each bar is like a cut out from a piece of paper?)

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Pie Charts

Say we order a pizza pie and it is cut up into pieces. Below I show a pizza pie cut, and I wanted it to show it cut into slices that hits the middle. If you get a quarter of the pie, you get one of the sections shown. 0.25 of the pie is an example of the relative frequency. So, par charts show each category getting its relative share of the pie.

A pie chart could really be the frequency, relative frequency or percent frequency pie, but the size of each piece of the pie is always the relative frequency.

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Describing Numerical Data

Here we study ways of describing a variable that is numerical.

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Numerical variables have values that are numbers. Remember that categorical variables may use numbers, but the variable really has values that represent groups.

Example of a categorical variable: eye color 1 = blue, 2 = green, 3 = red (especially on Friday morning).

Our initial method of describing a numerical variable will be basically the same as with a qualitative variable, with some modification in our understanding.

Let’s consider the variable age. Consider the first 20 people you see today. Consider yourself if you look in the mirror, but just count yourself once. The age of these folks could be 1 day to 110 years in Nebraska, right?

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Remember, a frequency distribution is a tabular summary of data showing the number, or frequency, of items in each of several nonoverlapping classes.

With a variable like eye color (qualitative), we typically make each color a class. But with a variable like age (quantitative), if we make each age a class then we could have so many classes that the distribution is hard to interpret. The authors suggest grouping the ages into classes and having anywhere from 5 to 15 classes.

Let’s digress for a minute and think about a data set. Say I have data on people. Say I have social security number, eye color, age and blood alcohol level last Thursday night at 11:30. On the next screen I have what the data might look like in Excel, or other computer programs. Note each column is a variable. Each row represents a person in this example. Thus in each row we see the values of the variables for each person.

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SS# Eye color age Blood alcohol level

123456789 Blue 22 .00

987654321 Blue 21 .016

567891234 Green 19 .010

345678912 Blue 27 .00

654321987 Brown 20 .00

000000000 Red 22 .023

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The reason for my digression was to have you begin to think about data sets. (Typically) A variable is in a column. The values down the column are for different people (or what ever the subject might be). I believe it is useful to think about data as you consider statistical ideas. Here we are looking at how to describe a column of data, one variable.

Now, when we have a numerical variable like age we have to think about how many classes to have. We want each class to have more than a few people in it. For now, let’s not worry too much about how many classes to have.

The “width” of each class should be equal. Using age as an example, we might have classes that have 5 consecutive ages included. The first class might be 10-14 year olds, then 15-19 year olds and so on.

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Class “limits” need to be considered. Each person should be in only one class. Each class has a lower limit and an upper limit and these limits are exclusive to the class.

On the next screen I have an example of the frequency, relative frequency and percent frequency distributions for the variable age for 50 people.

The frequency column is just the counting of the number of people in each class. The relative frequency is the frequency of each class divided by the total number of people in the data set. The percent frequency is the relative frequency times 100.

(Look back at the distributions we had for the qualitative variable. Does it look the same?)

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Age Frequency Relative Frequency

Percent Frequency

10-14 19 0.38 38

15-19 8 0.16 16

20-24 5 0.1 10

25-29 13 0.26 26

30-34 5 0.1 10

Total 50 1.00 100

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Do you know why we put information in columns? Because then we can call’um as we see’um. Sorry:)

So, the frequency, relative frequency and percent frequency distributions are different ways of summarizing information about a numerical variable.

Notes about our table.

1) The total, or sum, of the frequency column is equal to the number of observations, n.

2) The total, or sum, of the relative frequency column is equal to 1.

3) The total, or sum, of the percent frequency column is equal to 100.

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Bar graphs are used for qualitative variables. What amounts to the same thing for quantitative variables are called histograms.

Histograms just put the the frequency, relative frequency and percent frequency distributions into visual form. The form is a graph with certain properties.

The variable of interest is put along the horizontal axis. We would have the variable age on the axis.

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Imagine you have a piece of construction paper that is blue. Do you remember way back when in school you would cut strips of paper and then curl the paper with the scissors? Well, we will not need to curl the paper here!

I mention this silly example because I want you to think about cutting strips that are of the same with and are as wide as the class width (remember class widths are equal). The height of each strip would then represent the frequency, relative frequency or percent frequency on the variable. You would tape each strip onto the graph above each category.

So the vertical axis, or height, in the bar graphs is either the frequency, relative frequency or percent frequency distributions.

In constructing the histogram on a quantitative variable THERE IS NO SPACE between each bar to help us remember we have a quantitative variable.

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Pie Charts

The authors do not mention it, but pie charts could be made in a similar fashion to what we saw before.

Cumulative Distributions

Have you every accumulated a bunch of junk in your room? Yea, me to. Each day more stuff just shows up. So tomorrow I will have all the stuff I have today and more.

Cumulative distributions are kind of like my story. When you look at the frequency distribution we just saw, a slight modification can make then into cumulative distributions. For the cumulative frequency, start with the first class in the first row. The cumulative value for this row is the frequency.

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But the cumulative value for the second row is the frequency for the first row plus the frequency for the second row. So to get the cumulative frequency for a given row, add up the frequencies for that row and all previous rows.

The cumulative relative frequency and cumulative percent frequency are found as before: cumulative relative frequency is cumulative frequency divided by total and the cumulative percent frequency is the cumulative relative frequency times 100.

What’s a henway? About 4 or 5 pounds!

What’s an Ogive? It is what we call a graph of a cumulative frequency distribution. The horizontal axis has values of the variable and the vertical axis has the appropriate cumulative frequency.

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Freq

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24

68

1012

1416

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10 to 19 20 to 29 30 to 39 40 to 49 50 to 59

Freq

What is the most frequently occurring age group in this example? How many times does it occur? (the group is 30-39 and the frequency 17)

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Cumulative Freq

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10

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50

60

0 10 20 30 40 50 60

Age

Cumulative Freq

This is a frequency Ogive (or polygon). Note here that what was accumulated was just the frequency. The highest frequency is 50 because that was the total number of folks in the study. What would be the highest value if we had a cumulative relative frequency? (1, right?)

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Summary

With both categorical and numerical data one way to summarize the data is to look at frequency information of groups. The categorical data are already in natural groups and the numerical data has to be grouped. Then we might look at the frequency, relative frequency, or the percent frequency of each group.

The relative frequency of a group = group frequency divided by the total frequency across all groups.

The percent frequency is the relative frequency times 100.

Bar charts, pie charts and histograms are based on these ideas.