2.2  One Quantitative Variable
2.2  One Quantitative VariableOne quantitative variable is covered in Sections 2.2 and 2.3 of the Lock^{5} textbook. In these sections, you will learn how to describe the distribution of a quantitative variable in terms of shape, central tendency, and variability. You will be introduced to the normal distribution, z scores, percentiles, graphs, and the fivenumbersummary.
2.2.1  Graphs: Dotplots and Histograms
2.2.1  Graphs: Dotplots and HistogramsDotplots and histograms are both graphical displays that can be used with one quantitative variable. In both of these plots the horizontal axis represents the values of the variable. The number of dots in a dotplot, or the height of the bars in a histogram, represent the number of cases with each value or range of values.
 Dotplot
 Histogram
MinitabExpress – Dotplot
To create a dotplot in Minitab Express:
 Open the data set:
 On a PC or Mac: Select GRAPHS > Dotplot
 Select Simple
 Double click the variable Verbal SAT (2005) in the box on the left to insert the variable into the Y variable box
 Click OK
This should result in the following dotplot:
Select your operating system below to see a stepbystep guide for this example.
MinitabExpress – Histogram
To create a histogram in Minitab Express:
 Open the data set:
 On a PC or Mac: Select GRAPHS > Histogram
 Select Simple
 Double click the variable Verbal SAT(2005) in the box on the left to insert the variable into the Y variable box
 Click OK
This should result in the following histogram:
Select your operating system below to see a stepbystep guide for this example.
2.2.2  Outliers
2.2.2  OutliersSome observations within a set of data may fall outside the general scope of the other observations. Such observations are called outliers. Outliers can be identified by looking at a dotplot or histogram. In Lesson 3 you'll learn about boxplots which can also be used to identify outliers. When constructing a boxplot, Minitab Express identifies outliers using mathematical methods that you will see next week. This week we will identify outliers by making a relatively subjective judgement from a given a list of data points, a dotplot, or a histogram.
Example: Dotplot of Hours Watching TV
A sample of STAT 200 students was surveyed and asked how many hours per week they watch television. A dotplot was constructed using these data.
The rightmost dot is definitely an outlier because it is much higher than any other points. The other higher points, around 55, 50, and 46, may be outliers. Next week we will learn about some mathematical methods for identifying outliers that can help us make decisions in cases like this where it is not obvious which values are outliers.
Example: Histogram of Best Marriage Age
This sample of students was also asked what they believed was the best age to get married. A histogram was constructed using these data.
There appear to be three outliers in this sample, all on the higher end.
2.2.3  Shape
2.2.3  ShapeQuantitative variables are often discussed in terms of their shape. Both dotplots and histograms can be used to interpret a distribution's shape. A distribution may be described in terms of symmetry and skewness.
 Symmetrical Distribution

A distribution that is similar on both sides of the center.
 Normal Distribution

One specific type of symmetrical distribution. This is also known as a bellshaped distribution.
 Skewed
 A distribution in which values are more spread out on one side of the center than on the other.
 Right Skewed

A distribution in which the higher values (towards the right on a number line) are more spread out than the lower values. This is also known as positively skewed.
 Left Skewed

A distribution in which the lower values (towards the left on a number line) are more spread out than the higher values. This is also known as negatively skewed.
2.2.4  Measures of Central Tendency
2.2.4  Measures of Central TendencyQuantitative variables are often summarized using numbers to communicate their central tendency. The mean, median, and mode are three of the most commonly used measures of central tendency.
 Mean

The numerical average; calculated as the sum of all of the data values divided by the number of values.
The sample mean is represented as \(\overline{x}\) ("xbar") and the population mean is denoted as the Greek letter \(\mu\) ("mu"). The formula is the same for the sample mean and the population mean.
 Population Mean
 \(\mu=\dfrac{\Sigma x}{N}\)
 Sample Mean
 \(\overline {x} = \dfrac{\Sigma x}{n}\)
 Median
 The middle of the distribution that has been ordered from smallest to largest; for distributions with an even number of values, this is the mean of the two middle values.
 Mode
 The most frequently occurring value(s) in the distribution, may be used with quantitative or categorical variables.
Example: Hours Spent Studying
A professor asks a sample of 7 students how many hours they spent studying for the final. Their responses are: 5, 7, 8, 9, 9, 11, and 13.
Mean
\(\overline{x} = \dfrac{\sum x}{n} =\dfrac{5+7+8+9+9+11+13}{7} =\dfrac{62}{7} =8.857\)
The mean is 8.857 hours.
Median
The observations are already in order from smallest to largest. The middle observation is 9 hours. The median is 9 hours.
Mode
The most frequently occurring observation was 9 hours. The mode is 9 hours.
In this example, the mean, median, and mode are all similar. Recall from our discussion of shape, the mean, median, and mode are all equal when a distribution is symmetric. This distribution of hours spent studying is probably close to symmetrical.
Example: Test Scores
A teacher wants to examine students’ test scores. Their scores are: 74, 88, 78, 90, 94, 90, 84, 90, 98, and 80.
Mean
\(\overline{x}\: =\: \dfrac{\sum x}{n} = \dfrac{74+88+78+90+94+90+84+90+98+80}{10} = \dfrac{866}{10}=86.6\)
The mean score was 86.6.
Median
First, we need to put the scores in order from lowest to highest: 74, 78, 80, 84, 88, 90, 90, 90, 94, 98
Because there is an even number of scores, the median will be the mean of the middle two values. The middle two values are 88 and 90. \(\frac{88+90}{2}=89\)
The median is 89.
Mode
The most frequently occurring score was 90. There were 3 students who scored a 90; this is the mode. Because this distribution has one mode, it is unimodal.
In this example the mean is slightly lower than the median which is slightly lower than the mode. Recall from our discussion of shape that this occurs when a distribution is skewed to the left. This distribution is probably slightly skewed to the left.
Example: Household Size
A group of children are asked how many people live in their household. The following data is collected: 4, 3, 6, 2, 2, 4, 3.
Mean
\(\overline{x} = \dfrac{\sum x}{n}=\dfrac{4+3+6+2+2+4+3}{7}=\dfrac{24}{7}=3.429\)
The mean household size in this group of children is 3.429 people.
Median
First, we need to put all of the values in order from smallest to largest: 2, 2, 3, 3, 4, 4, 6
The value in the middle of this distribution is 3. The median is 3.
Mode
In this distribution, the most common values are 2, 3, and 4. Each of these values occurs twice. There are 3 modes: 2, 3, and 4. This distribution is multimodal.
2.2.4.1  Skewness & Central Tendency
2.2.4.1  Skewness & Central TendencyThe preferred measure of central tendency often depends on the shape of the distribution. In a symmetrical distribution, the mean, median, and mode are all equal. In these cases, the mean is often the preferred measure of central tendency.
For distributions that are strongly skewed or have outliers, the median is often the most appropriate measure of central tendency because in skewed distributions the mean is pulled out toward the tail. The median is more resistant to outliers compared to the mean. Of these three measures of central tendency, the mean is most influenced by outliers. Below you will see how the direction of skewness impacts the order of the mean, median, and mode.
2.2.5  Measures of Spread
2.2.5  Measures of SpreadVariance and standard deviation are measures of variability. The standard deviation is the most commonly used measure of variability when data are quantitative and approximately normally distributed. When computing the standard deviation by hand, it is necessary to first compute the variance. The standard deviation is equal to the square root of the variance. Here, you will learn how to compute these values by hand. After this lesson, you will always be computing standard deviation using software such as Minitab Express.
 Standard Deviation
 Roughly the average difference between individual data values and the mean. The standard deviation of a sample is denoted as \(s\). The standard deviation of a population is denoted as \(\sigma\).
 Sample Standard Deviation
 \(s=\sqrt{\dfrac{\sum (x\overline{x})^{2}}{n1}}\)
In order to compute the standard deviation for a sample we first compute deviations. The sum of the squared deviations (SS) divided by \(n1\), this is the variance (\(s^2\)).
The square root of the variance is the standard deviation: \(\sqrt{s^2}=s\).
 Deviation
 An individual score minus the mean.
 Sum of Squared Deviations
 Deviations squared and added together. This is also known as the sum of squares or SS.
 Variance
 Approximately the average of all of the squared deviations; for a sample represented as \(s^{2}\).
 Sum of Squares
 \(SS={\sum (x\overline{x})^{2}}\)
 Sample Variance
 \(s^{2}=\dfrac{\sum (x\overline{x})^{2}}{n1}\)
There are a number of methods for calculating the standard deviation. If you look through different textbooks or search online, you may find different formulas and procedures. To compute the standard deviation for a sample, we will use the formulas above and the following steps:
Step 1: Compute the sample mean: \(\overline{x} = \frac{\sum x}{n}\).
Step 2: Subtract the sample mean from each individual value: \(x\overline{x}\), these are the deviations.
Step 3: Square each deviation: \((x\overline{x})^{2}\), these are the squared deviations.
Step 4: Add the squared deviations: \(\sum (x\overline{x})^{2}\), this is the sum of squares.
Step 5: Divide the sum of squares by \(n1\): \(\frac{\sum (x\overline{x})^{2}}{n1}\), this is the sample variance \((s^{2})\).
Step 6: Take the square root of the sample variance: \(\sqrt{\frac{\sum (x\overline{x})^{2}}{n1}}\), this is the sample standard deviation.
Video Example
The video below walks through an example of computing a sample standard deviation by hand.
Example: Hours Spent Studying
A professor asks a sample of 7 students how many hours they spent studying for the final. Their responses are: 5, 7, 8, 9, 9, 11, and 13.
Step 1: Compute the mean
\(\overline{x} = \dfrac{\sum x}{n}=\dfrac{5+7+8+9+9+11+13}{7}=8.857\)
Step 2: Compute the deviations
\(x\)  \(x  \overline{x}\) 

5  \(5  8.857 = 3.857\) 
7  \(7  8.857 = 1.857\) 
8  \(8  8.857 = 0.857\) 
9  \(9  8.857 = 0.143\) 
9  \(9  8.857 = 0.143\) 
11  \(11  8.857 = 2.143\) 
13  \(13  8.857 = 4.143\) 
Step 3: Square the deviations
\(x\)  \(x  \overline{x}\)  \((x\overline{x})^{2}\) 

5  \(5  8.857 = 3.857\)  \(3.857^{2} = 14.876\) 
7  \(7  8.857 = 1.857\)  \(1.857^{2} = 3.448\) 
8  \(8  8.857 = 0.857\)  \(0.857^{2} = 0.734\) 
9  \(9  8.857 = 0.143\)  \(0.143^{2} = 0.0020\) 
9  \(9  8.857 = 0.143\)  \(0.143^{2} = 0.0020\) 
11  \(11  8.857 = 2.143\)  \(2.143^{2} = 4.592\) 
13  \(13  8.857 = 4.143\)  \(4.143^{2} = 17.164\) 
Step 4: Sum the squared deviations
\(SS=\sum (x\overline{x})^{2}=14.876+3.448+0.734+.020+.020+4.592+17.164=40.854\)
The sum of squares is 40.854
Step 5: Divide by n  1 to compute the variance
\(s^{2}=\dfrac{\sum (x\overline{x})^{2}}{n1}=\dfrac{40.854}{71}=6.809\)
The variance is 6.809
Step 6: Take the square root of the variance
\(s=\sqrt{s^{2}}=\sqrt{6.809}=2.609\)
The standard deviation is 2.609
2.2.6  Minitab Express: Central Tendency & Variability
2.2.6  Minitab Express: Central Tendency & VariabilityMinitab Express may be used to compute descriptive statistics such as the mean, median, mode, standard deviation, and variance.
Note that these are the default setting in Minitab Express:
If you want the mode or variance, you will need to select them under the Statistics tab.
MinitabExpress – Central Tendency
To obtain measures of central tendency and variability in Minitab Express:
 Open the data set:
 On a PC: from the menu select STATISTICS > Describe
On a Mac: from the menu select Statistics > Summary Statistics > Descriptive Statistics  Double click the variable Height in the box on the left to insert the variable into the Variable box
 Click on the Statistics tab and select the descriptive statistics that you want displayed
 Click OK
This should result in the following output:
Variable  N  N*  Mean  SE Mean  StDev  Minimum  Q1  Median  Maximum 

Height  525  0  67.0090  0.1947  4.4616  51.0000  64.0000  67.0000  82.0000 
Select your operating system below to see a stepbystep guide for this example.
2.2.7  The Empirical Rule
2.2.7  The Empirical RuleA normal distribution is symmetrical and bellshaped.
The Empirical Rule is a statement about normal distributions. Your textbook uses an abbreviated form of this, known as the 95% Rule, because 95% is the most commonly used interval. The 95% Rule states that approximately 95% of observations fall within two standard deviations of the mean on a normal distribution.
 Normal Distribution
 A specific type of symmetrical distribution, also known as a bellshaped distribution
 Empirical Rule

On a normal distribution about 68% of data will be within one standard deviation of the mean, about 95% will be within two standard deviations of the mean, and about 99.7% will be within three standard deviations of the mean
 95% Rule
 On a normal distribution approximately 95% of data will fall within two standard deviations of the mean; this is an abbreviated form of the Empirical Rule
Example: Pulse Rates
Suppose the pulse rates of 200 college men are bellshaped with a mean of 72 and standard deviation of 6.
 About 68% of the men have pulse rates in the interval \(72\pm1(6)=[66, 78]\).
 About 95% of the men have pulse rates in the interval \(72\pm2(6)=[60, 84]\).
 About 99.7% of the men have pulse rates in the interval \(72\pm 3(6)=[54, 90]\).
Example: IQ Scores
IQ scores are normally distributed with a mean of 100 and a standard deviation of 15.
 About 68% of individuals have IQ scores in the interval \(100\pm 1(15)=[85,115]\).
 About 95% of individuals have IQ scores in the interval \(100\pm 2(15)=[70,130]\).
 About 99.7% of individuals have IQ scores in the interval \(100\pm 3(15)=[55,145]\).
2.2.8  zscores
2.2.8  zscoresOften we want to describe an observation in relation to the distribution of all observations. We can do this using a zscore. By converting observations to zscores, we can compare observations from different distributions.
 zscore

Distance between an individual score and the mean in standard deviation units; also known as a standardized score.
 zscore
 \(z=\dfrac{x  \overline{x}}{s}\)

\(x\) = original data value
\(\overline{x}\) = mean of the original distribution
\(s\) = standard deviation of the original distribution
This equation could also be rewritten in terms of population values: \(z=\frac{x\mu}{\sigma}\)
Later in the course, we will learn more about the zdistribution, which is a special case of the normal distribution.
 zdistribution

A bellshaped distribution with a mean of 0 and standard deviation of 1, also known as the standard normal distribution.
Example: Milk
A study of 66,831 dairy cows found that the mean milk yield was 12.5 kg per milking with a standard deviation of 4.3 kg per milking (data from Berry, et al., 2013).
A cow produces 18.1 kg per milking. What is this cow’s zscore?
\(z=\frac{x\overline{x}}{s} =\frac{18.112.5}{4.3}=1.302\)
This cow’s zscore is 1.302; her milk production was 1.302 standard deviations above the mean.
A cow produces 12.5 kg per milking. What is this cow’s zscore?
\(z=\frac{x\overline{x}}{s} =\frac{12.512.5}{4.3}=0\)
This cow’s zscore is 0; her milk production was the same as the mean.
A cow produces 8 kg per milking. What is this cow’s zscore?
\(z=\frac{x\overline{x}}{s} =\frac{812.5}{4.3}=1.047\)
This cow’s zscore is 1.047; her milk production was 1.047 standard deviations below the mean.
Berry, D. P., Coyne, J., Boughlan, B., Burke, M., McCarthy, J., Enright, B., Cromie, A. R., McParland, S. (2013). Genetics of milking characteristics in dairy cows. Animal, 7(11), 17501758.
Example: Comparing Test Scores
SATMath scores are normally distributed with a mean of 500 and standard deviation of 100. ACTMath scores are normally distributed with a mean of 18 and standard deviation of 6. A student has taken both tests. They scored 600 on the SATMath and 22 on the ACTMath. Which score is more impressive?
We can't directly compare the student's SAT and ACT scores because they are on different scales. We can convert these test scores into zscores so we can directly compare them.
\(z_{SAT}=\frac{600500}{100}=1\)
This student scored 1 standard deviation above the mean on the SATMath.
\(z_{ACT}=\frac{2218}{6}=0.667\)
This student scored 0.667 standard deviations above the mean on the ACTMath.
The student's SATMath score is more impressive than their ACTMath score because the zscore is higher. They scored better than a larger proportion of other test takers on the SATMath.
Practice: Computing zscores
Type in the answer you think is correct  then click the 'Check' button to see how you did.
Click the right arrow to proceed to the next question. When you have completed all of the questions you will see how many you got right and the correct answers.
For each question, compute the zscore.
Type in the answer you think is correct  then click the 'Check' button to see how you did.
Click the right arrow to proceed to the next question. When you have completed all of the questions you will see how many you got right and the correct answers.
For each question, compute the zscore.
2.2.9  Percentiles
2.2.9  PercentilesThere are slightly different definitions of percentiles and different statistical software and textbooks may use different formulas. In this course, we will be using the definition from your textbook:
 Percentile
 Proportion of a distribution less than a given value.
Example: Test Scores
Test scores are often reported in terms of percentiles. For example, if a student scores in the 90th percentile on a test then he or she scored better than 90% of students who took the test.
2.2.10  Five Number Summary
2.2.10  Five Number Summary Five Number Summary
 Minimum, Q_{1}, Median, Q_{3}, Maximum
Q_{1} is the first quartile, this is the 25th percentile
Q_{3} is the third quartile, this is the 75th percentile
Five number summaries are used to describe some of the key features of a distribution. Using the values in a five number summary we can also compute the range and interquartile range.
 Range
 The difference between the maximum and minimum values.
 Range
 \(Range = Maximum  Minimum\)
 Note:
 The range is heavily influenced by outliers. For this reason, the interquartile range is often preferred because it is resistant to outliers.
 Interquartile range (IQR)
 The difference between the first and third quartiles.
 Interquartile Range
 \(IQR = Q_3  Q_1\)
Example: Hours Spent Studying
A professor asks a sample of students how many hours they spent studying for the final. The five number summary for their responses is (5, 7, 9, 11, 13).
Range
The maximum is 13 and the minimum is 5.
\(Range = 13  5 = 8\)
Interquartile Range
The third quartile is 11 and the first quartile is 7.
\(IQR = Q_3  Q_1 = 11  7 = 4\)
Example: Test Scores
A teacher wants to examine students’ test scores. The five number summary for their scores is (74, 80, 89, 90, 98).
Range
The highest score is 98. The lowest score is 74.
\(Range = 98  74 = 24\)
Interquartile Range
The third quartile is 90 and the first quartile is 80.
\(IQR = Q3  Q1 = 90  80 = 10\)