Descriptive Statistics for Quantitative Data

Descriptive options for quantitative data are much more robust than for categorical. As previously mentioned, descriptive statistics can be expressed both visually and numerically (usually in that order).

This section will expand on graphical methods, while the next few sections will focus on numerical summaries of quantitative data.

Graphical Methods for Quantitative Data

The first thing we may do, especially for quantitative data, is to examine it in a frequency table. We have many more graphical options beyond that for quantitative data, including:

• Stem-and-leaf plots
• Dot plots
• Line graphs
• Histograms
• Frequency polygons
• Time series plots

Each of these methods has its own distinct advantages and disadvantages.

Stem-and-Leaf Plots

One simple graph, the stem-and-leaf graph or stemplot, comes from the field of exploratory data analysis. This graph is a good choice when datasets are small. To create the plot, divide each observation of data into a “stem” and a “leaf.” The stem is the first part of the number, while the leaf consists of a final significant digit. For example, you could divide the number 23 into a stem of 2 and a leaf of 3. The number 432 could have a stem of 43 and leaf of 2. The decimal 9.3 could have a stem of 9 and leaf of 3. Write the stems in a vertical line from smallest to largest. Draw a vertical line to the right of the stems. Then write the leaves in increasing order next to their corresponding stems.

The stemplot is a quick way to organize things and gives a good picture of the data. You can quickly and easily find basic summary statistics such as the maximum, minimum, range, and some measures we will explore in the future, such as the median and quartiles. Stemplots can be good for seeing individual data points and mainly handle discrete or rounded continuous data.

Comparisons with Stem-and-Leaf Plots

Back-to-back or side-by-side stem-and-leaf plots allow for the comparison of two datasets in two columns. In a side-by-side stem-and-leaf plot, two sets of leaves share the same stem, with one set on the left and one on the right.

Line Graphs

Another type of graph that is useful for showing trends in specific data values (i.e., discrete data) is a line graph. In the particular line graph shown below, the x-axis (horizontal axis) consists of data values and the y-axis (vertical axis) consists of frequency points. The frequency points are connected using line segments.

NOTE: Line graphs can also be used with some ordinal categorical data.

Example

In a survey, 40 mothers were asked how many times per week a teenager must be reminded to complete chores. The results are shown in the table and chart below.

Number of times teenager is reminded	Frequency
0	2
1	5
2	8
3	14
4	7
5	4

Figure 2.27: Chore reminder data

Dot Plots

A dot plot consists of a number line and dots (or points) positioned above the number line.

Dot plots are very similar in functionality to stem-and-leaf plots but look a little bit cleaner. They can reveal an overall pattern and any outliers or extreme values. An outlier is an observation of data that does not fit the rest of the data. When graphed, an outlier will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. It takes some background information to fully explain outliers; we will cover them in more detail later.

Example

Consider the following data dealing with the hours of sleep students get per night: 5, 5.5, 6, 6, 6, 6.5, 6.5, 6.5, 6.5, 7, 7, 8, 8, 9

The dot plot for this data would be as follows:

Histograms

For most of the work in this book, histograms will display the data. One advantage of a histogram is that it can readily display large continuous datasets. A rule of thumb is to use a histogram when the dataset consists of 100 or more values.

A histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents (for instance, distance from your home to school). The vertical axis is labeled either “frequency” or “relative frequency” (or “percent frequency” or “probability”). The graph will have the same shape regardless of label. The histogram can give you a really good look at the overall shape of the data, the center, and the spread. However, you do lose individual data points.

A histogram is essentially a two-dimensional frequency table. To construct a histogram, you must first decide the size and number of bars, intervals, or classes, similarly to how you would with a frequency table.

Example

The following data are the heights (in inches to the nearest half-inch) of 100 male semiprofessional soccer players. The heights are continuous data, since height is measured.

60, 60.5, 61, 61, 61.5, 63.5, 63.5, 63.5, 64, 64, 64, 64, 64, 64, 64, 64.5, 64.5, 64.5, 64.5, 64.5, 64.5, 64.5, 64.5, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66.5, 66.5, 66.5, 66.5, 66.5, 66.5, 66.5, 66.5, 66.5, 66.5, 66.5, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67.5, 67.5, 67.5, 67.5, 67.5, 67.5, 67.5, 68, 68, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69.5, 69.5, 69.5, 69.5, 69.5, 70, 70, 70, 70, 70, 70, 70.5, 70.5, 70.5, 71, 71, 71, 72, 72, 72, 72.5, 72.5, 73, 73.5, 74

The smallest data value is 60. Since none of the data has more than one decimal, we want our starting point to have two decimal places. Since the numbers 0.5, 0.05, 0.005, etc. are convenient numbers, use 0.05 and subtract it from 60, the smallest value, for the convenient starting point.

This results in 60 – 0.05 = 59.95, which is more precise than, say, 61.5 by one decimal place. The starting point is, then, 59.95.

The largest value is 74, so 74 + 0.05 = 74.05 is the ending value.

Next, calculate the width of each bar or class interval. To calculate this width, subtract the starting point from the ending value and divide by the number of bars (you must choose the number of bars you desire). Suppose you choose eight bars.

NOTE:

We will round up to two and make each bar or class interval two units wide. Rounding up to two is one way to prevent a value from falling on a boundary. Rounding to the next number is often necessary even if it goes against the standard rules of rounding. For this example, using 1.76 as the width would also work. In determining the number of bars or class intervals, some follow the guideline to take the square root of the number of data values and then round to the nearest whole number, if necessary. For example, if there are 150 values of data, take the square root of 150 and round to 12 bars or intervals.

Some values in datasets might fall on boundaries for different intervals. Different researchers may set up histograms for the same data in different ways. There is more than one correct way to set up a histogram.

 

 

 

 

The boundaries are:

• 59.95
• 59.95 + 2 = 61.95
• 61.95 + 2 = 63.95
• 63.95 + 2 = 65.95
• 65.95 + 2 = 67.95
• 67.95 + 2 = 69.95
• 69.95 + 2 = 71.95
• 71.95 + 2 = 73.95
• 73.95 + 2 = 75.95

The heights 60 through 61.5 inches are in the interval 59.95–61.95. The heights that are 63.5 are in the interval 61.95–63.95. The heights that are 64 through 64.5 are in the interval 63.95–65.95. The heights 66 through 67.5 are in the interval 65.95–67.95. The heights 68 through 69.5 are in the interval 67.95–69.95. The heights 70 through 71 are in the interval 69.95–71.95. The heights 72 through 73.5 are in the interval 71.95–73.95. The height 74 is in the interval 73.95–75.95.

The following histogram displays the heights on the x-axis and relative frequency on the y-axis.

 

Frequency Polygons

Frequency polygons are analogous to line graphs but instead utilize binning techniques to make continuous data visually easy to interpret. They are essentially combinations of histograms and line graphs.

To construct a frequency polygon, first examine the data and decide on the number of intervals, or class intervals, to use on the x-axis and y-axis. After choosing the appropriate ranges, begin plotting the data points. After all the points are plotted, draw line segments to connect them.

Frequency polygons are sometimes more useful than histograms for comparing continuous distributions. This is achieved by overlaying the frequency polygons drawn for different datasets.

Example

A frequency polygon was constructed from the frequency table below.

Lower bound	Upper bound	Frequency	Cumulative frequency
49.5	59.5	5	5
59.5	69.5	10	15
69.5	79.5	30	45
89.5	89.5	40	85
99.5	99.5	15	100

Figure 2.31: Frequency distribution for calculus final test scores

The first label on the x-axis is 44.5. This represents an interval extending from 39.5 to 49.5. Since the lowest test score is 54.5, this interval is used only to allow the graph to touch the x-axis. The point labeled 54.5 represents the next interval, or the first “real” interval from the table, and contains five scores. This reasoning is followed for each of the remaining intervals, with the point 104.5 representing the interval from 99.5 to 109.5. Again, this interval contains no data and is only used so that the graph will touch the x-axis. Looking at the graph, we say that this distribution is skewed because one side of the graph does not mirror the other side.

Time Series Plots

Suppose that we want to study the temperature range of a region for an entire month. Every day at noon, we take note of the temperature in a log. A variety of statistical studies could be done with this data. We could find the mean or the median temperature for the month. We could construct a histogram displaying the number of days that temperatures reached a certain range of values. However, all of these methods ignore a portion of the data that we have collected.

One feature of the data that we may want to consider is time. Since each date is paired with the temperature reading for the day, we don’t have to think of the data as being random. We can instead use the times given to impose a chronological order on the data. A graph that recognizes this ordering and displays the changing temperature as the month progresses would be a time series graph.

Time series graphs are important tools in various applications of statistics. When recording values of the same variable over an extended period of time, sometimes it is difficult to discern any trend or pattern. However, once the same data points are displayed graphically, some features jump out. Time series graphs make trends easy to spot.

To construct a time series graph, we must look at both pieces of our paired dataset. We start with a standard Cartesian coordinate system. The horizontal axis is used to plot dates or other time increments, and the vertical axis is used to plot the values of the variable that we are measuring. By doing this, we make each point on the graph correspond to a date and a measured quantity. The points on the graph are typically connected by straight lines in the order in which they occur.

Example

The following data shows the Annual Consumer Price Index each month for ten years. Construct a time series graph for the Annual Consumer Price Index data only.

Year	January	Feburary	March	April	May	June	July
2009	211.143	212.193	212.709	213.240	213.856	215.693	215.351
2010	216.687	216.741	217.631	218.009	218.178	217.965	218.011
2011	220.223	221.309	223.467	224.906	225.964	225.722	225.922
2012	226.655	227.663	229.392	230.085	229.815	229.478	229.104
2013	230.280	232.166	232.773	232.531	232.945	233.504	233.596
2014	233.916	234.781	236.293	237.072	237.900	238.343	238.250
2015	233.707	234.722	236.119	236.599	237.805	238.638	238.654
2016	236.916	237.111	238.132	239.261	240.236	241.038	240.647
2017	242.839	243.603	243.801	244.524	244.733	244.955	244.786
2018	247.867	248.991	249.554	250.546	251.588	251.989	252.006
2019	251.712	252.776	254.202	255.548	256.092	256.143	256.571

Year	August	September	October	November	December	Annual
2009	215.834	215.969	216.177	216.330	215.949	214.537
2010	218.312	218.439	218.711	218.803	219.179	218.056
2011	226.545	226.889	226.421	226.230	225.672	224.939
2012	230.379	231.407	231.317	230.221	229.601	229.594
2013	233.877	234.149	233.546	233.069	233.049	232.957
2014	237.852	238.031	237.433	236.151	234.812	236.736
2015	238.316	237.945	237.838	237.336	236.525	237.017
2016	240.853	241.428	241.729	241.353	241.432	240.007
2017	245.519	246.819	246.663	246.669	246.524	245.120
2018	252.146	252.439	252.885	252.038	251.233	251.107
2019	256.558	256.759	257.346	257.208	256.974	255.657

Figure 2.33: CPI data

Figure References

Figure 2.28: Kindred Grey (2020). Chore reminder (line graph). CC BY-SA 4.0.

Figure 2.29: Kindred Grey (2020). Student sleep hours. CC BY-SA 4.0.

Figure 2.30: Kindred Grey (2020). Soccer player heights. CC BY-SA 4.0.

Figure 2.32: Kindred Grey (2020). Calculus final test scores (frequency polygon). CC BY-SA 4.0.

Figure 2.33: Data retrieved from https://www.usinflationcalculator.com/inflation/consumer-price-index-and-annual-percent-changes-from-1913-to-2008

Figure 2.34: Kindred Grey (2020). CPI time series plot. CC BY-SA 4.0.

Figure Descriptions

Figure 2.28: Line graph showing the number of times a teenager needs to be reminded to do chores on the x-axis (range 1-6 by 1) and frequency on the y-axis (range 0-16 by 2).

Figure 2.29: Dot plot showing ‘frequency of average time (in hours) spent sleeping per night’. The number line is marked in intervals of 1 from 5 to 9. Dots above the line show 1 person reporting 5 hours, 1 with 5.5, 3 with 6, 4 with 6.5, 2 with 7, 2 with 8, and 1 with 9 hours.

Figure 2.30: Histogram consists of 8 bars with the y-axis in increments of 0.05 from 0-0.45 measuring relative frequency and the x-axis in intervals of 2 from 57.95-75.95 measuring heights. The highest is 65.95-67.95 (0.4 relative frequency).

Figure 2.32: X axis measures scores and the y axis measures frequency. Highest frequency is 40 when the score is 84.5.

Figure 2.34: Times series graph that matches the supplied data. The x-axis shows years from 2010 to 2019, and the y-axis shows the annual CPI. Constant positive trend.