Section 2.2: Quantitative Data

The graph for quantitative data looks similar to a bar graph, except there are some major differences. First, in a bar graph the categories can be put in any order on the horizontal axis. There is no set order for these data values. You can’t say how the data is distributed based on the shape, since the shape can change just by putting the categories in different orders. With quantitative data, the data are in specific orders, since you are dealing with numbers. With quantitative data, you can talk about a distribution, since the shape only changes a little bit depending on how many categories you set up. This is called a frequency distribution.

1. 
   Find the range = largest value – smallest value
   
2. 
   Pick the number of classes to use. Usually the number of classes is between five and twenty. Five classes are used if there are a small number of data points and twenty classes if there are a large number of data points (over 1000 data points). (Note: categories will now be called classes from now on.)
   
3. 
   . Always round up to the next integer (even if the answer is already a whole number go to the next integer). If you don’t do this, your last class will not contain your largest data value, and you would have to add another class just for it. If you round up, then your largest data value will fall in the last class, and there are no issues.
   
4. 
   Create the classes. Each class has limits that determine which values fall in each class. To find the class limits, set the smallest value as the lower class limit for the first class. Then add the class width to the lower class limit to get the next lower class limit. Repeat until you get all the classes. The upper class limit for a class is one less than the lower limit for the next class.
   
5. 
   In order for the classes to actually touch, then one class needs to start where the previous one ends. This is known as the class boundary. To find the class boundaries, subtract 0.5 from the lower class limit and add 0.5 to the upper class limit.
   
6. 
   Sometimes it is useful to find the class midpoint. The process is
   
7. 
   To figure out the number of data points that fall in each class, go through each data value and see which class boundaries it is between. Utilizing tally marks may be helpful in counting the data values. The frequency for a class is the number of data values that fall in the class.
   

Note: the above description is for data values that are whole numbers. If you data value has decimal places, then your class width should be rounded up to the nearest value with the same number of decimal places as the original data. In addition, your class boundaries should have one more decimal place than the original data. As an example, if your data have one decimal place, then the class width would have one decimal place, and the class boundaries are formed by adding and subtracting 0.05 from each class limit.

Table #2.21 contains the amount of rent paid every month for 24 students from a statistics course. Make a relative frequency distribution using 7 classes.

1) Find the range:

Always round up to the next integer even if the width is already an integer.

Every value in the data should fall into exactly one of the classes. No data values should fall right on the boundary of two classes.

Make sure the total of the frequencies is the same as the number of data points.

summary(variable) – so you can find out the minimum and maximum.

breaks = seq(min, number above max, by = class width)

breaks – so you can see the breaks that R made.

variable.cut=cut(variable, breaks, right=FALSE) – this will cut up the data into the classes.

variable.freq=table(variable.cut) – this will create the frequency table.

variable.freq – this will display the frequency table.
For the data in Example #2.2.1, the R command would be:

rent<-c(1500, 1350, 350, 1200, 850, 900, 1500, 1150, 1500, 900, 1400, 1100, 1250, 600, 610, 960, 890, 1325, 900, 800, 2550, 495, 1200, 690)

summary(rent)
Output:

Min. 1st Qu. Median Mean 3rd Qu. Max.

350.0 837.5 1030.0 1082.0 1331.0 2550.0
breaks=seq(350, 3000, by = 315)

breaks
Output:

[1] 350 665 980 1295 1610 1925 2240 2555 2870

These are your lower limits of the frequency distribution. You can now write your own table.

rent.freq=table(rent.cut)

rent.freq
Output:

rent.cut

4 8 5 6

0 0 1 0
It is difficult to determine the basic shape of the distribution by looking at the frequency distribution. It would be easier to look at a graph. The graph of a frequency distribution for quantitative data is called a frequency histogram or just histogram for short.

Draw a histogram for the distribution from example #2.2.1.

The class boundaries are plotted on the horizontal axis and the frequencies are plotted on the vertical axis. You can plot the midpoints of the classes instead of the class boundaries. Graph #2.2.1 was created using the midpoints because it was easier to do with the software that created the graph. On R, the command is

hist(variable, col="type in what color you want", breaks, main="type the title you want", xlab="type the label you want for the horizontal axis", ylim=c(0, number above maximum frequency) – produces histogram with specified color and using the breaks you made for the frequency distribution.

For this example, the command in R would be (assuming you created a frequency distribution in R as described previously):

hist(variable, col="type in what color you want", number of classes, main="type the title you want", xlab="type the label you want for the horizontal axis") – produces histogram with specified color and number of classes (though the number of classes is an estimate and R will create the number of classes near this value).

For this example, the R command without a frequency distribution created first would be:

hist(rent, col="blue", 7, main="Monthly Rent Paid by Students", xlab="Monthly Rent ($)")

A variation on a frequency distribution is a relative frequency distribution. Instead of giving the frequencies for each class, the relative frequencies are calculated.

Find the relative frequency for the grade data.

From example #2.2.1, the frequency distribution is reproduced in table #2.2.2.

Divide each frequency by the number of data points.