Graphing Quantitative Data Sets

Graphing Quantitative Data Sets
Stem-and-leaf plot Each number is separated into a stem and a leaf. Similar to a histogram. Still contains original data values. 26 2 3 4 5 Data: 21, 25, 25, 26, 27, 28, , 36, 36, 45

Example: Constructing a Stem-and-Leaf Plot
The following are the numbers of text messages sent last month by the cellular phone users on one floor of a college dormitory. Display the data in a stem-and-leaf plot.

Solution: Constructing a Stem-and-Leaf Plot
The data entries go from a low of 78 to a high of 159. Use the rightmost digit as the leaf. For instance, 78 = 7 | and = 15 | 9 List the stems, 7 to 15, to the left of a vertical line. For each data entry, list a leaf to the right of its stem.

Solution: Constructing a Stem-and-Leaf Plot
Include a key to identify the values of the data. From the display, you can conclude that more than 50% of the cellular phone users sent between 110 and 130 text messages.

Graphing Quantitative Data Sets
Dot plot Each data entry is plotted, using a point, above a horizontal axis Data: 21, 25, 25, 26, 27, 28, 30, 36, 36, 45 26

Example: Constructing a Dot Plot
Use a dot plot organize the text messaging data. So that each data entry is included in the dot plot, the horizontal axis should include numbers between 70 and 160. To represent a data entry, plot a point above the entry's position on the axis. If an entry is repeated, plot another point above the previous point.

Solution: Constructing a Dot Plot
From the dot plot, you can see that most values cluster between 105 and 148 and the value that occurs the most is 126. You can also see that 78 is an unusual data value.

Graphing Qualitative Data Sets
Pie Chart A circle is divided into sectors that represent categories. The area of each sector is proportional to the frequency of each category.

Example: Constructing a Pie Chart
The numbers of motor vehicle occupants killed in crashes in 2005 are shown in the table. Use a pie chart to organize the data. (Source: U.S. Department of Transportation, National Highway Traffic Safety Administration) Vehicle type Killed Cars 18,440 Trucks 13,778 Motorcycles 4,553 Other 823

Solution: Constructing a Pie Chart
Find the relative frequency (percent) of each category. Vehicle type Frequency, f Relative frequency Cars 18,440 Trucks 13,778 Motorcycles 4,553 Other 823 37,594

Construct the pie chart using the central angle that corresponds to each category. To find the central angle, multiply 360º by the category's relative frequency. For example, the central angle for cars is 360(0.49) ≈ 176º

Vehicle type Frequency, f Relative frequency Central angle Cars 18,440 0.49 Trucks 13,778 0.37 Motorcycles 4,553 0.12 Other 823 0.02 360º(0.49)≈176º 360º(0.37)≈133º 360º(0.12)≈43º 360º(0.02)≈7º

Vehicle type Relative frequency Central angle Cars 0.49 176º Trucks 0.37 133º Motorcycles 0.12 43º Other 0.02 7º From the pie chart, you can see that most fatalities in motor vehicle crashes were those involving the occupants of cars.

Graphing Qualitative Data Sets
Bar graph A vertical bar graph in which the height of each bar represents frequency or relative frequency. The bars are positioned in order of decreasing height, with the tallest bar positioned at the left. Frequency Categories

Example: Constructing a Bar Graph
In a recent year, the retail industry lost $41.0 million in inventory shrinkage. Inventory shrinkage is the loss of inventory through breakage, pilferage, shoplifting, and so on. The causes of the inventory shrinkage are administrative error ($7.8 million), employee theft ($15.6 million), shoplifting ($14.7 million), and vendor fraud ($2.9 million). Use a Pareto chart to organize this data. (Source: National Retail Federation and Center for Retailing Education, University of Florida)

Solution: Constructing a Bar Graph
Cause $ (million) Admin. error 7.8 Employee theft 15.6 Shoplifting 14.7 Vendor fraud 2.9 From the graph, it is easy to see that the causes of inventory shrinkage that should be addressed first are employee theft and shoplifting.

Frequency Distributions and Their Graphs

Section Objectives Construct frequency distributions
Construct frequency histograms, frequency polygons, relative frequency histograms, and ogives

Frequency Distribution
A table that shows classes or intervals of data with a count of the number of entries in each class. The frequency, f, of a class is the number of data entries in the class. Class Frequency, f 1 – 5 5 6 – 10 8 11 – 15 6 16 – 20 21 – 25 26 – 30 4 Class width 6 – 1 = 5 Lower class limits Upper class limits

Constructing a Frequency Distribution
Decide on the number of classes. Usually between 5 and 20; otherwise, it may be difficult to detect any patterns. Find the class width. Determine the range of the data. Divide the range by the number of classes. Round up to the next convenient number.

Find the class limits. You can use the minimum data entry as the lower limit of the first class. Find the remaining lower limits (add the class width to the lower limit of the preceding class). Find the upper limit of the first class. Remember that classes cannot overlap. Find the remaining upper class limits.

Make a tally mark for each data entry in the row of the appropriate class. Count the tally marks to find the total frequency f for each class.

Example: Constructing a Frequency Distribution
The following sample data set lists the number of minutes 50 Internet subscribers spent on the Internet during their most recent session. Construct a frequency distribution that has seven classes.

Solution: Constructing a Frequency Distribution
Number of classes = 7 (given) Find the class width Round up to 12

Use 7 (minimum value) as first lower limit. Add the class width of 12 to get the lower limit of the next class. = 19 Find the remaining lower limits. Lower limit Upper limit 7 Class width = 12 19 31 43 55 67 79

The upper limit of the first class is 18 (one less than the lower limit of the second class). Add the class width of 12 to get the upper limit of the next class = 30 Find the remaining upper limits. Lower limit Upper limit 7 19 31 43 55 67 79 Class width = 12 18 30 42 54 66 78 90

Make a tally mark for each data entry in the row of the appropriate class. Count the tally marks to find the total frequency f for each class. Class Tally Frequency, f 7 – 18 IIII I 6 19 – 30 IIII IIII 10 31 – 42 IIII IIII III 13 43 – 54 IIII III 8 55 – 66 IIII 5 67 – 78 79 – 90 II 2 Σf = 50

Determining the Midpoint
Midpoint of a class Class Midpoint Frequency, f 7 – 18 6 19 – 30 10 31 – 42 13 Class width = 12

Determining the Relative Frequency
Relative Frequency of a class Portion or percentage of the data that falls in a particular class. Class Frequency, f Relative Frequency 7 – 18 6 19 – 30 10 31 – 42 13

Determining the Cumulative Frequency
Cumulative frequency of a class The sum of the frequency for that class and all previous classes. Class Frequency, f Cumulative frequency 7 – 18 6 19 – 30 10 31 – 42 13 6 + 16 + 29

Expanded Frequency Distribution
Class Frequency, f Midpoint Relative frequency Cumulative frequency 7 – 18 6 12.5 0.12 19 – 30 10 24.5 0.20 16 31 – 42 13 36.5 0.26 29 43 – 54 8 48.5 0.16 37 55 – 66 5 60.5 0.10 42 67 – 78 72.5 48 79 – 90 2 84.5 0.04 50 Σf = 50

Graphs of Frequency Distributions
Frequency Histogram A bar graph that represents the frequency distribution. The horizontal scale is quantitative and measures the data values. The vertical scale measures the frequencies of the classes. Consecutive bars must touch. data values frequency

Class Boundaries Class boundaries
The numbers that separate classes without forming gaps between them. The distance from the upper limit of the first class to the lower limit of the second class is 19 – 18 = 1. Half this distance is 0.5. Class Boundaries Frequency, f 7 – 18 6 19 – 30 10 31 – 42 13 6.5 – 18.5 First class lower boundary = 7 – 0.5 = 6.5 First class upper boundary = = 18.5

Class Boundaries Class Class boundaries Frequency, f 7 – 18 6.5 – 18.5
19 – 30 18.5 – 30.5 10 31 – 42 30.5 – 42.5 13 43 – 54 42.5 – 54.5 8 55 – 66 54.5 – 66.5 5 67 – 78 66.5 – 78.5 79 – 90 78.5 – 90.5 2

Example: Frequency Histogram
Construct a frequency histogram for the Internet usage frequency distribution. Class Class boundaries Midpoint Frequency, f 7 – 18 6.5 – 18.5 12.5 6 19 – 30 18.5 – 30.5 24.5 10 31 – 42 30.5 – 42.5 36.5 13 43 – 54 42.5 – 54.5 48.5 8 55 – 66 54.5 – 66.5 60.5 5 67 – 78 66.5 – 78.5 72.5 79 – 90 78.5 – 90.5 84.5 2

Solution: Frequency Histogram (using Midpoints)

Solution: Frequency Histogram (using class boundaries)
You can see that more than half of the subscribers spent between 19 and 54 minutes on the Internet during their most recent session.

Frequency Polygon A line graph that emphasizes the continuous change in frequencies. data values frequency

Example: Frequency Polygon
Construct a frequency polygon for the Internet usage frequency distribution. Class Midpoint Frequency, f 7 – 18 12.5 6 19 – 30 24.5 10 31 – 42 36.5 13 43 – 54 48.5 8 55 – 66 60.5 5 67 – 78 72.5 79 – 90 84.5 2

Solution: Frequency Polygon
The graph should begin and end on the horizontal axis, so extend the left side to one class width before the first class midpoint and extend the right side to one class width after the last class midpoint. You can see that the frequency of subscribers increases up to 36.5 minutes and then decreases.

Relative Frequency Histogram Has the same shape and the same horizontal scale as the corresponding frequency histogram. The vertical scale measures the relative frequencies, not frequencies. data values relative frequency

Example: Relative Frequency Histogram
Construct a relative frequency histogram for the Internet usage frequency distribution. Class Class boundaries Frequency, f Relative frequency 7 – 18 6.5 – 18.5 6 0.12 19 – 30 18.5 – 30.5 10 0.20 31 – 42 30.5 – 42.5 13 0.26 43 – 54 42.5 – 54.5 8 0.16 55 – 66 54.5 – 66.5 5 0.10 67 – 78 66.5 – 78.5 79 – 90 78.5 – 90.5 2 0.04

Solution: Relative Frequency Histogram
From this graph you can see that 20% of Internet subscribers spent between 18.5 minutes and 30.5 minutes online.

Cumulative Frequency Graph or Ogive A line graph that displays the cumulative frequency of each class at its upper class boundary. The upper boundaries are marked on the horizontal axis. The cumulative frequencies are marked on the vertical axis. data values cumulative frequency

Constructing an Ogive Construct a frequency distribution that includes cumulative frequencies as one of the columns. Specify the horizontal and vertical scales. The horizontal scale consists of the upper class boundaries. The vertical scale measures cumulative frequencies. Plot points that represent the upper class boundaries and their corresponding cumulative frequencies.

Constructing an Ogive Connect the points in order from left to right.
The graph should start at the lower boundary of the first class (cumulative frequency is zero) and should end at the upper boundary of the last class (cumulative frequency is equal to the sample size).

Example: Ogive Construct an ogive for the Internet usage frequency distribution. Class Class boundaries Frequency, f Cumulative frequency 7 – 18 6.5 – 18.5 6 19 – 30 18.5 – 30.5 10 16 31 – 42 30.5 – 42.5 13 29 43 – 54 42.5 – 54.5 8 37 55 – 66 54.5 – 66.5 5 42 67 – 78 66.5 – 78.5 48 79 – 90 78.5 – 90.5 2 50

Solution: Ogive From the ogive, you can see that about 40 subscribers spent 60 minutes or less online during their last session. The greatest increase in usage occurs between 30.5 minutes and 42.5 minutes.

Graphing Paired Data Sets
Each entry in one data set corresponds to one entry in a second data set. Graph using a scatter plot. The ordered pairs are graphed as points in a coordinate plane. Used to show the relationship between two quantitative variables. y x Larson/Farber 4th ed.

Example: Constructing a Time Series Chart
The table lists the number of cellular telephone subscribers (in millions) for the years 1995 through Construct a time series chart for the number of cellular subscribers. (Source: Cellular Telecommunication & Internet Association)

Solution: Constructing a Time Series Chart
Let the horizontal axis represent the years. Let the vertical axis represent the number of subscribers (in millions). Plot the paired data and connect them with line segments.

Solution: Constructing a Time Series Chart
The graph shows that the number of subscribers has been increasing since 1995, with greater increases recently.

Objectives Determine the mean, median, and mode of a population and of a sample Determine the weighted mean of a data set and the mean of a frequency distribution Describe the shape of a distribution as symmetric, uniform, or skewed and compare the mean and median for each

Measures of Central Tendency
Measure of central tendency A value that represents a typical, or central, entry of a data set. Most common measures of central tendency: Mean Median Mode

Measure of Central Tendency: Mean
Mean (average) The sum of all the data entries divided by the number of entries. Sigma notation: Σx = add all of the data entries (x) in the data set. Population mean: Sample mean:

Example: Finding a Sample Mean
The prices (in dollars) for a sample of roundtrip flights from Chicago, Illinois to Cancun, Mexico are listed. What is the mean price of the flights?

Solution: Finding a Sample Mean
The sum of the flight prices is Σx = = 3695 To find the mean price, divide the sum of the prices by the number of prices in the sample The mean price of the flights is about $

Measure of Central Tendency: Median
The value that lies in the middle of the data when the data set is ordered. Measures the center of an ordered data set by dividing it into two equal parts. If the data set has an odd number of entries: median is the middle data entry. even number of entries: median is the mean of the two middle data entries.

Example: Finding the Median
The prices (in dollars) for a sample of roundtrip flights from Chicago, Illinois to Cancun, Mexico are listed. Find the median of the flight prices

Solution: Finding the Median
First order the data. There are seven entries (an odd number), the median is the middle, or fourth, data entry. The median price of the flights is $427.

Example: Finding the Median
The flight priced at $432 is no longer available. What is the median price of the remaining flights?

Solution: Finding the Median
First order the data. There are six entries (an even number), the median is the mean of the two middle entries. The median price of the flights is $412.

Measure of Central Tendency: Mode
The data entry that occurs with the greatest frequency. If no entry is repeated the data set has no mode. If two entries occur with the same greatest frequency, each entry is a mode (bimodal).

Example: Finding the Mode
The prices (in dollars) for a sample of roundtrip flights from Chicago, Illinois to Cancun, Mexico are listed. Find the mode of the flight prices

Solution: Finding the Mode
Ordering the data helps to find the mode. The entry of 397 occurs twice, whereas the other data entries occur only once. The mode of the flight prices is $397.

Example: Finding the Mode
At a political debate a sample of audience members was asked to name the political party to which they belong. Their responses are shown in the table. What is the mode of the responses? Political Party Frequency, f Democrat 34 Republican 56 Other 21 Did not respond 9

Solution: Finding the Mode
Political Party Frequency, f Democrat 34 Republican 56 Other 21 Did not respond 9 The mode is Republican (the response occurring with the greatest frequency). In this sample there were more Republicans than people of any other single affiliation. Larson/Farber 4th ed.

Comparing the Mean, Median, and Mode
All three measures describe a typical entry of a data set. Advantage of using the mean: The mean is a reliable measure because it takes into account every entry of a data set. Disadvantage of using the mean: Greatly affected by outliers (a data entry that is far removed from the other entries in the data set).

Example: Comparing the Mean, Median, and Mode
Find the mean, median, and mode of the sample ages of a class shown. Which measure of central tendency best describes a typical entry of this data set? Are there any outliers? Ages in a class 20 21 22 23 24 65

Solution: Comparing the Mean, Median, and Mode
Ages in a class 20 21 22 23 24 65 Mean: Median: Mode: 20 years (the entry occurring with the greatest frequency)

Mean ≈ 23.8 years Median = 21.5 years Mode = 20 years The mean takes every entry into account, but is influenced by the outlier of 65. The median also takes every entry into account, and it is not affected by the outlier. In this case the mode exists, but it doesn't appear to represent a typical entry.

Sometimes a graphical comparison can help you decide which measure of central tendency best represents a data set. In this case, it appears that the median best describes the data set because it is not as affected by outliers.

Weighted Mean Weighted Mean
The mean of a data set whose entries have varying weights. where w is the weight of each entry x

Example: Finding a Weighted Mean
You are taking a class in which your grade is determined from five sources: 50% from your test mean, 15% from your midterm, 20% from your final exam, 10% from your computer lab work, and 5% from your homework. Your scores are 86 (test mean), 96 (midterm), 82 (final exam), 98 (computer lab), and 100 (homework). What is the weighted mean of your scores? If the minimum average for an A is 90, did you get an A?

Solution: Finding a Weighted Mean
Source Score, x Weight, w x∙w Test Mean 86 0.50 86(0.50)= 43.0 Midterm 96 0.15 96(0.15) = 14.4 Final Exam 82 0.20 82(0.20) = 16.4 Computer Lab 98 0.10 98(0.10) = 9.8 Homework 100 0.05 100(0.05) = 5.0 Σw = 1 Σ(x∙w) = 88.6 Your weighted mean for the course is You did not get an A.

Mean of Grouped Data Mean of a Frequency Distribution Approximated by
where x and f are the midpoints and frequencies of a class, respectively

Finding the Mean of a Frequency Distribution
In Words In Symbols Find the midpoint of each class. Find the sum of the products of the midpoints and the frequencies. Find the sum of the frequencies. Find the mean of the frequency distribution.

Example: Find the Mean of a Frequency Distribution
Use the frequency distribution to approximate the mean number of minutes that a sample of Internet subscribers spent online during their most recent session. Class Midpoint Frequency, f 7 – 18 12.5 6 19 – 30 24.5 10 31 – 42 36.5 13 43 – 54 48.5 8 55 – 66 60.5 5 67 – 78 72.5 79 – 90 84.5 2

Solution: Find the Mean of a Frequency Distribution
Class Midpoint, x Frequency, f (x∙f) 7 – 18 12.5 6 12.5∙6 = 75.0 19 – 30 24.5 10 24.5∙10 = 245.0 31 – 42 36.5 13 36.5∙13 = 474.5 43 – 54 48.5 8 48.5∙8 = 388.0 55 – 66 60.5 5 60.5∙5 = 302.5 67 – 78 72.5 72.5∙6 = 435.0 79 – 90 84.5 2 84.5∙2 = 169.0 n = 50 Σ(x∙f) =

The Shape of Distributions
Symmetric Distribution A vertical line can be drawn through the middle of a graph of the distribution and the resulting halves are approximately mirror images.

Uniform Distribution (rectangular) All entries or classes in the distribution have equal or approximately equal frequencies. Symmetric.

Skewed Left Distribution (negatively skewed) The “tail” of the graph elongates more to the left. The mean is to the left of the median mean always follows the tail.

Skewed Right Distribution (positively skewed) The “tail” of the graph elongates more to the right. The mean is to the right of the median.

Range Range The difference between the maximum and minimum data entries in the set. The data must be quantitative. Range = (Max. data entry) – (Min. data entry)

Example: Finding the Range
A corporation hired 10 graduates. The starting salaries for each graduate are shown. Find the range of the starting salaries. Starting salaries (1000s of dollars)

Solution: Finding the Range
Ordering the data helps to find the least and greatest salaries. Range = (Max. salary) – (Min. salary) = 47 – 37 = 10 The range of starting salaries is 10 or $10,000. minimum maximum

Deviation, Variance, and Standard Deviation
The difference between the data entry, x, and the mean of the data set. Population data set: Deviation of x = x – μ Sample data set: Deviation of x = x – x

Example: Finding the Deviation
A corporation hired 10 graduates. The starting salaries for each graduate are shown. Find the deviation of the starting salaries. Starting salaries (1000s of dollars) Solution: First determine the mean starting salary.

Solution: Finding the Deviation
Determine the deviation for each data entry. Salary ($1000s), x Deviation: x – μ 41 41 – 41.5 = –0.5 38 38 – 41.5 = –3.5 39 39 – 41.5 = –2.5 45 45 – 41.5 = 3.5 47 47 – 41.5 = 5.5 44 44 – 41.5 = 2.5 37 37 – 41.5 = –4.5 42 42 – 41.5 = 0.5 Σx = 415 Σ(x – μ) = 0

Population Variance Population Standard Deviation Sum of squares, SSx

Finding the Population Variance & Standard Deviation
In Words In Symbols Find the mean of the population data set. Find deviation of each entry. Square each deviation. Add to get the sum of squares. x – μ (x – μ)2 SSx = Σ(x – μ)2

Finding the Population Variance & Standard Deviation
In Words In Symbols Divide by N to get the population variance. Find the square root to get the population standard deviation.

Example: Finding the Population Standard Deviation
A corporation hired 10 graduates. The starting salaries for each graduate are shown. Find the population variance and standard deviation of the starting salaries. Starting salaries (1000s of dollars) Recall μ = 41.5.

Solution: Finding the Population Standard Deviation
Determine SSx N = 10 Salary, x Deviation: x – μ Squares: (x – μ)2 41 41 – 41.5 = –0.5 (–0.5)2 = 0.25 38 38 – 41.5 = –3.5 (–3.5)2 = 12.25 39 39 – 41.5 = –2.5 (–2.5)2 = 6.25 45 45 – 41.5 = 3.5 (3.5)2 = 12.25 47 47 – 41.5 = 5.5 (5.5)2 = 30.25 44 44 – 41.5 = 2.5 (2.5)2 = 6.25 37 37 – 41.5 = –4.5 (–4.5)2 = 20.25 42 42 – 41.5 = 0.5 (0.5)2 = 0.25 Σ(x – μ) = 0 SSx = 88.5

Solution: Finding the Population Standard Deviation
Population Variance Population Standard Deviation The population standard deviation is about 3.0, or $3000.

Sample Variance Sample Standard Deviation

Finding the Sample Variance & Standard Deviation
In Words In Symbols Find the mean of the sample data set. Find deviation of each entry. Square each deviation. Add to get the sum of squares.

Finding the Sample Variance & Standard Deviation
In Words In Symbols Divide by n – 1 to get the sample variance. Find the square root to get the sample standard deviation.

Example: Finding the Sample Standard Deviation
The starting salaries are for the Chicago branches of a corporation. The corporation has several other branches, and you plan to use the starting salaries of the Chicago branches to estimate the starting salaries for the larger population. Find the sample standard deviation of the starting salaries. Starting salaries (1000s of dollars)

Solution: Finding the Sample Standard Deviation
Determine SSx n = 10 Salary, x Deviation: x – μ Squares: (x – μ)2 41 41 – 41.5 = –0.5 (–0.5)2 = 0.25 38 38 – 41.5 = –3.5 (–3.5)2 = 12.25 39 39 – 41.5 = –2.5 (–2.5)2 = 6.25 45 45 – 41.5 = 3.5 (3.5)2 = 12.25 47 47 – 41.5 = 5.5 (5.5)2 = 30.25 44 44 – 41.5 = 2.5 (2.5)2 = 6.25 37 37 – 41.5 = –4.5 (–4.5)2 = 20.25 42 42 – 41.5 = 0.5 (0.5)2 = 0.25 Σ(x – μ) = 0 SSx = 88.5

Solution: Finding the Sample Standard Deviation
Sample Variance Sample Standard Deviation The sample standard deviation is about 3.1, or $3100.

Example: Using Technology to Find the Standard Deviation
Sample office rental rates (in dollars per square foot per year) for Miami’s central business district are shown in the table. Use a calculator or a computer to find the mean rental rate and the sample standard deviation. (Adapted from: Cushman & Wakefield Inc.) Office Rental Rates 35.00 33.50 37.00 23.75 26.50 31.25 36.50 40.00 32.00 39.25 37.50 34.75 37.75 37.25 36.75 27.00 35.75 26.00 29.00 40.50 24.50 33.00 38.00

Solution: Using Technology to Find the Standard Deviation
Sample Mean Sample Standard Deviation

Interpreting Standard Deviation
Standard deviation is a measure of the typical amount an entry deviates from the mean. The more the entries are spread out, the greater the standard deviation.

Quartiles Fractiles are numbers that partition (divide) an ordered data set into equal parts. Quartiles approximately divide an ordered data set into four equal parts. First quartile, Q1: About one quarter of the data fall on or below Q1. Second quartile, Q2: About one half of the data fall on or below Q2 (median). Third quartile, Q3: About three quarters of the data fall on or below Q3.

Example: Finding Quartiles
The test scores of 15 employees enrolled in a CPR training course are listed. Find the first, second, and third quartiles of the test scores Solution: Q2 divides the data set into two halves. Lower half Upper half Q2

Solution: Finding Quartiles
The first and third quartiles are the medians of the lower and upper halves of the data set. Lower half Upper half Q1 Q2 Q3 About one fourth of the employees scored 10 or less, about one half scored 15 or less; and about three fourths scored 18 or less.

Interquartile Range Interquartile Range (IQR)
The difference between the third and first quartiles. IQR = Q3 – Q1 Is resistant to outliers…WHY???

Example: Finding the Interquartile Range
Find the interquartile range of the test scores. Recall Q1 = 10, Q2 = 15, and Q3 = 18 Solution: IQR = Q3 – Q1 = 18 – 10 = 8 The test scores in the middle portion of the data set vary by at most 8 points.

Box-and-Whisker Plot Box-and-whisker plot
Exploratory data analysis tool. Highlights important features of a data set. Requires (five-number summary): Minimum entry First quartile Q1 Median Q2 Third quartile Q3 Maximum entry

Drawing a Box-and-Whisker Plot
Find the five-number summary of the data set. Construct a horizontal scale that spans the range of the data. Plot the five numbers above the horizontal scale. Draw a box above the horizontal scale from Q1 to Q3 and draw a vertical line in the box at Q2. Draw whiskers from the box to the minimum and maximum entries. Whisker Maximum entry Minimum entry Box Median, Q2 Q3 Q1

Example: Drawing a Box-and-Whisker Plot
Draw a box-and-whisker plot that represents the 15 test scores. Recall Min = 5 Q1 = 10 Q2 = 15 Q3 = 18 Max = 37 Solution: 5 10 15 18 37 About half the scores are between 10 and 18. By looking at the length of the right whisker, you can conclude 37 is a possible outlier.

Percentiles and Other Fractiles
Summary Symbols Quartiles Divides data into 4 equal parts Q1, Q2, Q3 Deciles Divides data into 10 equal parts D1, D2, D3,…, D9 Percentiles Divides data into 100 equal parts P1, P2, P3,…, P99

Example: Interpreting Percentiles
The ogive represents the cumulative frequency distribution for SAT test scores of college-bound students in a recent year. What test score represents the 72nd percentile? How should you interpret this? (Source: College Board Online)

Solution: Interpreting Percentiles
The 72nd percentile corresponds to a test score of This means that 72% of the students had an SAT score of 1700 or less.

Graphing Quantitative Data Sets

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