By the end of this lesson, you should be able to:
In the previous lesson, we introduced two important characteristics
of a distribution: the shape and the
center. In this section, you will discover ways to
summarize the spread of a distribution of data. The
spread of a distribution of data describes how far the observations tend
to be from each other. There are many ways to describe the spread of a
distribution, but one of the most popular measurements of spread is
called the “standard deviation.”
This activity introduces two measures of spread: the standard deviation and the variance.
Bird Flu Fever
Avian Influenza A H5N1, commonly called the bird flu, is a deadly illness that is currently only passed to humans from infected birds. This illness is particularly dangerous because at some point it is likely to mutate to allow humantohuman transmission. Health officials worldwide are preparing for the possibility of a bird flu pandemic.
Dr. K. Y. Yuen led a team of researchers who reported the body temperatures of people admitted to Chinese hospitals with confirmed cases of Avian Influenza. Their research team collected data on the body temperature at the time that people with the bird flu were admitted to the hospital. In the article, they reported on two groups of people, those with relatively uncomplicated cases of the bird flu and those with severe cases.
The table below presents the data representative of the body temperatures for the two groups of bird flu patients:
Relatively Uncomplicated Cases  Severe Cases 

38.1  39.1 
38.3  39.5 
38.4  38.9 
39.5  39.2 
39.7  39.9 
39.7  
39.0 
Let us focus on the relatively uncomplicated cases. Creating a histogram of such a small dataset does not provide much benefit. With only a handful of values, there is not much shape to the distribution.
We can, however, use numerical summaries to give an indication of the center of the distribution.
We will use these data to investigate some measures of the spread in a data set.
There is relatively little difference in the temperatures of the uncomplicated patients. The lowest is \(38.1 ^\circ \text{C}\), while the highest temperature is \(39.7 ^\circ \text{C}\).
The standard deviation is a measure of the spread in the distribution. If the data tend to be close together, then the standard deviation is relatively small. If the data tend to be more spread out, then the standard deviation is relatively large.
The standard deviation of the body temperatures is \(0.742 ^\circ \text{C}\). This number contains information from all the patients. If the patients’ temperatures had been more diverse, the standard deviation would be larger. If the patients’ temperatures were more uniform (i.e. closer together), then the standard deviation would have been smaller. If all the patients somehow had the same temperature, then the standard deviation would be zero.
We are working with a sample. To be explicit, we call \(0.742 ^\circ \text{C}\) the sample standard deviation. The symbol for the sample standard deviation is \(s\). This is a statistic. The parameter representing the population standard deviation is \(\sigma\) (pronounced /SIGma/). In practice, we rarely know the value of the population standard deviation, so we use the sample standard deviation \(s\) as an approximation for the unknown population standard deviation \(\sigma\).
At this point, you probably do not have much intuition regarding the standard deviation. We will use this statistic frequently. By the end of the semester you can expect to become very comfortable with this idea. For now, all you need to know is that if two variables are measured on the same scale, the variable with values that are further apart will have the larger standard deviation.
To calculate the sample standard deviation in Excel, follow these steps:
How is the standard deviation computed? Where does this “magic” number come from? How does one number include the information about the spread of all the points?
It is a little tedious to compute the standard deviation by hand. You will usually compute standard deviation with a computer. However, the process is very instructive and will help you understand conceptually what the statistic represents. As you work through the following steps, please remember the goal is to find a measure of the spread in a data set. We want one number that describes how spread out the data are.
First, observe the number line below, where each x represents the temperature of a patient with a relatively uncomplicated case of bird flu. As mentioned earlier, there is not a huge spread in the temperatures.
On your sketch of the number line, we draw a vertical line at 38.8 degrees, the sample mean. Now, draw horizontal lines from the mean to each of your \(\times\)’s. These horizontal line segments represent the spread of the data about the mean. Your plot should look something like this:
The length of each of the line segments represents how far each observation is from the mean. If the data are close together, these lines will be fairly short. If the distribution has a large spread, the line segments will be longer. The standard deviation is a measure of how long these lines are, as a whole.
The distance between the mean and an observation is referred to as a deviation. In other words, deviations are the lengths of the line segments drawn in the image above.
\[ \begin{array}{1cl} \text{Deviation} & = & \text{Value}  \text{Mean} \\ \text{Deviation} & = & x  \bar x \end{array} \]
If the observed value is greater than the mean, the deviation is positive. If the value is less than the mean, the deviation is negative.
The standard deviation is a complicated sort of average of the deviations. Making a table like the one below will help you keep track of your calculations. Please participate fully in this exercise. Writing your answers at each step and developing a table as instructed will greatly enhance the learning experience. By following these steps, you will be able to compute the standard deviation by hand, and more importantly, understand what it is telling you.
Step 01: The first step in computing the standard deviation by hand is to create a table, like the following. Enter the observed data in the first column.
Observation (\(x\)) 
Deviation from the Mean (\(x\bar x\)) 

\(38.1\) 
\(38.138.8=0.7\) 
\(38.3\) 

\(38.4\) 

\(39.5\) 

\(39.7\) 

\(\bar x = 38.8\) 
Step 02: The second column of the table contains the deviations from the mean. Complete column 2 of the table above.
Check Results for Step 2
Step 03: Add a third column to your table. To get the values in this column, square the deviations from the mean that you found in Column 2.
Click Here for a Blank TableStep 04: Now, add up the squared deviations from the mean.
Check Results for Step 04Step 05: Divide the sum of the squared deviations by \(n  1\). Write this value at the bottom of Column 3 of your table.
The number you computed in Step 05 is called the sample variance. It is a measure of the spread in a data set. It has very nice theoretical properties. The variance plays an important role in Statistics. We denote the sample variance by the symbol \(s^2\).
It can be shown that the sample variance is an unbiased estimator of the true population variance (which is denoted \(\sigma^2\).) This means that the sample variance can be considered a reasonable estimator of the population variance. If the sample size is large, this estimator tends to be very good.
Check the Results for Step 05
Step 06: Take the square root of the sample variance to get the sample standard deviation.
The sample standard deviation is defined as the square root of the sample variance.
\[\text{Sample Standard Deviation} = s = \sqrt{ s^2 } = \sqrt{\strut\text{Sample Variance}}\]
The standard deviation has the same units as the original observations. We use the standard deviation heavily in statistics.
The sample standard deviation (\(s\)) is an estimate of the true population standard deviation (\(\sigma\)).
Standard Deviation
The standard deviation is one number that describes the spread in a set of data. If the data points are close together the standard deviation will be smaller than if they are spread out.
At this point, it may be difficult to understand the meaning and usefulness of the standard deviation. For now, it is enough for you to recognize the following points:
Variance
The variance is the square of the standard deviation. The sample variance is denoted by the symbol \(s^2\). You found the sample standard deviation for patient temperatures of uncomplicated cases of bird in the bird above is \(s = 0.74162\). So, the sample variance for this data set is \(s^2 = 0.74162^2 = 0.550\). Be aware, if you had squared the rounded value of \(s^2 = 0.742\) in the calculation, you would have gotten an answer of 0.551 instead. This would be considered incorrect!
To calculate the sample variance in Excel:
The standard deviation and variance are two commonly used measures of the spread in a data set. Why is there more than one measure of the spread? The standard deviation and the variance each have their own pros and cons.
The variance has excellent theoretical properties. It is an unbiased estimator of the true population variance. That means that if many, many samples of \(n\) observations were drawn, the variances computed for all the samples would be centered nicely around the true population variance, \(\sigma^2\). Because of these benefits, the variance is regularly used in higherlevel statistics applications. One drawback of the variance is that the units for the variance are the square of the units for the original data. In the bird flu example, the body temperatures were measured in degrees Centigrade. So, the variance will have units of degrees Centigrade squared \((^\circ \text{C})^2\). What does degrees Centigrade squared mean? How do you interpret this? It doesn’t make any sense. This is one of the major drawbacks of the sample variance.
Because we take the square root of the variance to get the standard deviation, the standard deviation is in the same units as the original data. This is a great advantage, and is one of the reasons that the standard deviation is commonly used to describe the spread of data.
Enter the patient temperature data for the severe cases of bird flu into Excel. Then use Excel to calculate the numerical summaries you have learned so far. As a reminder, the temperatures of patients with a severe case of bird flu are:
For the next two questions, consider the histograms below comparing weight (in kilograms) of men (top histogram) to elephant seals (bottom histogram).
Weight of Men Compared to Weight of Seals
Review of Parameters and Statistics
We have now learned some statistics that can be used to estimate population parameters. For example, we use \(\bar x\) to estimate the population mean \(\mu\). The sample statistics \(s\) estimates the true population standard deviation \(\sigma\). The following table summarizes what we have done so far:
Sample Statistic 
Population Parameter 


Mean 
\(\bar x\) 
\(\mu\) 
Standard Deviation 
\(s\) 
\(\sigma\) 
Variance 
\(s^2\) 
\(\sigma^2\) 
\(\vdots\) 
\(\vdots\) 
\(\vdots\) 
Unless otherwise specified, we will always use Excel to find the sample variance and sample mean. In each case, the sample statistic estimates the population parameter. The ellipses \(\vdots\) in this table hint that we will add rows in the future.
Optional Reading: Formulas for \(s\) and \(s^2\) (Hidden)
Click Here if you love MathNeither the standard deviation nor the variance is resistant to outliers. This means that when there are outliers in the data set, the standard deviation and the variance become artificially large. It is worth noting that the mean is also not resistant. When there are outliers, the mean will be “pulled” in the direction of the outliers.
The mean and standard deviation are used to describe the center and spread when the distribution of the data is symmetric and bellshaped. If data are not symmetric and bellshaped, we typically use the fivenumber summary (discussed below) to describe the spread, because this summary is resistant.
Recall the five steps of the Statistical Process (and the mnemonic “Daniel Can Discern More Truth).
Step 1: 
Daniel 
Design the study 
Step 2: 
Can 
Collect data 
Step 3: 
Discern 
Describe the data 
Step 4: 
More 
Make inferences 
Step 5: 
Truth 
Take action 
Step 3 of this process is “Describe the data.” You have already learned about the mean, median, mode, standard deviation, variance and histograms. These can be good ways to describe the data. The following information on percentiles, quartiles, 5number summaries, and boxplots will help you learn other common ways to describe data, especially if the data are skewed or contain outliers.
For symmetric, bellshaped data, the mean and standard deviation provide a good description of the center and shape of the distribution. The mean and standard deviation are not sufficient to describe a distribution that is skewed or has outliers. An outlier is any observation that is very far from the others. The mean is pulled in the direction of the outlier. Also, the standard deviation is inflated by points that are very far from the mean.
Now, you have probably had some experience with percentiles in the past especially when you received a score on a standardized test such as the ACT. Even though percentiles are commonly used, they are generally misunderstood. Before examining the wrong site/wrong patient data, let’s review percentiles. Even if you think you understand percentiles, please study this section carefully.
Imagine a very long street with houses on one side. The houses increase in value from left to right. At the left end of the street is a small cardboard box with a leaky roof. Next door is a slightly larger cardboard box that does not leak. The houses eventually get larger and more valuable. The rightmost house on the street is a huge mansion.
The home values are representative of data. If we have a list of data, sorted in increasing order, and we want to divide it into 100 equal groups, we only need 99 dividers (like fences) to divide up the data. The first divider is as large or larger than 1% of the data. The second divider is as large or larger than 2% of the data, and so on. The last divider, the 99^{th}, is the value that is as large or larger than 99% of the data. These dividers are called percentiles. A percentile is a number such that a specified percentage of the data are at or below this number. For example, the 99^{th} percentile is a number such that 99% of the data are at or below this value. As another example, half (50%) of the data lie at or below the 50^{th} percentile. The word percent means \(\div 100\). This can help you remember that the percentiles divide the data into 100 equal groups.
Quartiles are special percentiles. The word quartile is from the Latin quartus, which means “fourth.” The quartiles divide the data into four equal groups. The quartiles correspond to specific percentiles. The first quartile, Q_{1}, is the 25^{th} percentile. The second quartile, Q_{2}, is the same as the 50^{th} percentile or the median. The third quartile, Q_{3}, is equivalent to the 75^{th} percentile.
Wrong Site/Wrong Patient Lawsuits
Percentiles can be used to describe the center and spread of any distribution and are particularly useful when the distribution is skewed or has outliers. To explore this issue, you will use software to calculate percentiles of data on costs incurred by hospitals due to certain lawsuits. The lawsuits in question were about surgeries performed on the wrong patient, or on the right patient but the wrong part of the patient’s body (the wrong site).
To calculate percentiles and quartiles in Excel, do the following
Open the data file you are using. For this example, open the file WrongSiteWrongPatient.xlsx.
You will use Excel’s percentile (inclusive) function: =percentile.inc()
This Excel function requires two inputs (or arguments), separated by a comma. The first input is the cell range reference. The second input is the desired percentile.
The wrongsite data in the file ranges from row 2 to row 412 in column A. So, to calculate the 25^{th} percentile of the wrong site data you should enter the following formula in a blank cell somewhere in the file:
You may notice that some of the values for percentiles given in
Excel are different from those given in other softwares. This is due to
the slightly different ways in which percentiles can be calculated. In
this course, be sure to use the percentiles that come from Excel’s
percentile.inc() function.
The first quartile (\(Q_1\)) or 25^{th} percentile (calculated in Excel) of the wrongsite data is: $29,496. (This result is illustrated in the figure below.) This means that 25 percent of the time hospitals lost a wrongsite lawsuit, they had to pay $29,496 or less. The 25^{th} percentile can be written symbolically as: P_{25} = $29,496. Other percentiles can be written the same way. The 99^{th} percentile can be written as P_{99}.
1st percentile  0 
2nd percentile  0 
3rd percentile  0 
…  … 
24th percentile  28633.4 
25th percentile  29496 
26th percentile  31067 
Another way to summarize data is with the fivenumber summary. The fivenumber summary is comprised of the minimum, the first quartile, the second quartile (or median), the third quartile, and the maximum.
Statistical packages can give different results for some computations. This is because there are several reasonable ways to define certain quantities, such as the quartiles. As such, you may find that some of the values that are given in Excel are different than what other software may give.
To find the values for a fivenumber summary in Excel, do the following
Open the data file you are using. For this example, open the file WrongSiteWrongPatient.xlsx.
You can use the function =percentile.inc() in Excel to find the 1^{st} quartile, median and 3^{rd} quartile (otherwise known as the 25^{th}, 50^{th}, and 75^{th} percentiles respectively).
Type the following into a blank cell of the WrongSiteWrongPatient.xlsx worksheet
A boxplot is a graphical representation of the fivenumber summary. Unlike the mean or standard deviation, a boxplot is resistant to outliers. That means that it won’t be “pulled” one way or the other by extraordinarily large or small values in the data as will a mean, for instance. We will illustrate the process of making a boxplot using the wrongsite data.
Follow the steps below to learn how a boxplot relates to the fivenumber summary. Learning what each part of the boxplot represents will enable you to interpret the plot correctly.
Step 01: To draw a boxplot, start with a number line.
Step 02: Draw a vertical line segment above each of the quartiles.
Step 03: Connect the tops and bottoms of the line segments, making a box.
Step 04: Make a smaller mark above the values corresponding to the minimum and the maximum.
Step 05: Draw a line from the left side of the box to the minimum, and draw another line from the right side of the box the maximum.
Step 06: These last two lines look like whiskers, so this is sometimes called a boxandwhisker plot.
To create a boxplot in Excel, do the following
Open the data file you are using. For this example, open the file WrongSiteWrongPatient.xlsx.
Highlight the data you want to plot (in this case, cells A2 to
A412 contain the wrongsite lawsuit data).
Go to the Insert ribbon in Excel and select the histogram icon from the “Charts” section of the ribbon. Then select the only option under the Box and Whisker category.
Sidebyside boxplots are a powerful way to compare data from different samples visually. For example, we may be interested in comparing the results of wrongsite lawsuits and wrongpatient lawsuits.
In the plot we can quickly see that a wrongpatient lawsuit resulted in the largest cost to the hospital, more than $1.2 million. However, in general, wrongpatient lawsuits tend to result in a lower cost to the hospital than wrongsite lawsuits.
To create sidebyside boxplots in Excel, do the following
Open the data file you are using. For this example, open the file WrongSiteWrongPatient.xlsx. Since these two datasets are next to each other in the file, the easiest way to create sidebyside boxplots plots is to highlight both columns of data. Then follow the same steps as for a single boxplot.
To make a sidebyside boxplot plot easier to understand you can add a legend. This is most easily done by clicking the green ‘+’ near the upper right corner of the plot that appears when the plot is selected. Then, in the menu that appears, ensure the “legend” box is checked.
Alternatively, if the green ‘+’ is not visible to you, follow these steps shown in the image below. After selecting the plot go to the Design ribbon and select Add Chart Element. In the menu that appears select Legend, and then choose where you would like the legend to appear on the plot, as shown below.
Additional formatting can be applied to further improve the appearance of the chart.
The standard deviation is a number that describes how spread out the data are. A larger standard deviation means the data are more spread out than data with a smaller standard deviation.
Quartiles/percentiles, FiveNumber Summaries, and Boxplots are tools that help us understand data. The fivenumber summary of a data set contains the minimum value, the first quartile, the median, the third quartile, and the maximum value. A boxplot is a graphical representation of the fivenumber summary.
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