Chapter 4 Displaying and Summarizing Quantitative Data.

Chapter 4

Displaying and Summarizing Quantitative Data

Objectives

• Histogram• Stem-and-leaf

plot• Dotplot• Shape • Center • Spread• Outliers• Mean • Median

• Range• Interquartile

range (IQR)• Percentile• 5-Number

summary• Resistant• Variance• Standard

Deviation

Dealing With a Lot of Numbers…

• Summarizing the data will help us when we look at large sets of quantitative data.

• Without summaries of the data, it’s hard to grasp what the data tell us.

• The best thing to do is to make a picture…

• We can’t use bar charts or pie charts for quantitative data, since those displays are for categorical variables.

Reasons for Constructing Quantitative Frequency Tables

1. Large data sets can be summarized.

2. Can gain some insight into the nature of data.

3. Have a basis for constructing a histogram.

Ways to chart quantitative data

• Histograms and stemplots

These are summary graphs for a single variable. They

are very useful to understand the pattern of variability in

the data.

• Line graphs: time plots

Use when there is a meaningful sequence, like time. The

line connecting the points helps emphasize any change

over time.

• Other graphs to reflect numerical summaries are

Dotplots and Cumulative Frequency Curves (Ogive).

HISTOGRAMQuantitative Data

Histogram

• To make a histogram we first need to organize the data using a quantitative frequency table.

• Two types of quantitative data1. Discrete – use ungrouped frequency

table to organize.

2. Continuous – use grouped frequency table to organize.

Quantitative Frequency Tables – Ungrouped

• What is an ungrouped frequency table? An ungrouped frequency table simply lists the data values with the corresponding frequency counts with which each value occurs.

• Commonly used with discrete quantitative data.


• Example: The at-rest pulse rate for 16 athletes at a meet were 57, 57, 56, 57, 58, 56, 54, 64, 53, 54, 54, 55, 57, 55, 60, and 58. Summarize the information with an ungrouped frequency distribution.


• Example Continued

Note: The (ungrouped) classes are the observed values themselves.

Quantitative Relative Frequency Tables -

Ungrouped

Note: The relative frequency for a class is obtainedby computing f/n.

Quantitative Frequency Tables – Grouped

• What is a grouped frequency table? A grouped frequency table is obtained by constructing classes (or intervals) for the data, and then listing the corresponding number of values (frequency counts) in each interval.

• Commonly used with continuous quantitative data.


• Later, we will encounter a graphical display called the histogram. We will see that grouped frequency tables are used to construct these displays.


• There are several procedures that one can use to construct a grouped frequency tables.

• However, because of the many statistical software packages (MINITAB, SPSS etc.) and graphing calculators (TI-83 etc.) available today, it is not necessary to try to construct such distributions using pencil and paper.


• A frequency table should have a minimum of 5 classes and a maximum of 20 classes.

• For small data sets, one can use between 5 and 10 classes.

• For large data sets, one can use up to 20 classes.


• Example: The weights of 30 female students majoring in Physical Education on a college campus are as follows: 143, 113, 107, 151, 90, 139, 136, 126, 122, 127, 123, 137, 132, 121, 112, 132, 133, 121, 126, 104, 140, 138, 99, 134, 119, 112, 133, 104, 129, and 123. Summarize the data with a frequency distribution using seven classes.

Quantitative Frequency Tables – Grouped Example Continued

• NOTE: We will introduce the histogram here to help us explain a grouped frequency distribution.


• What is a histogram? A histogram is a graphical display of a frequency or a relative frequency table that uses classes and vertical (horizontal) bars (rectangles) of various heights to represent the frequencies.

Histogram

• The most common graph used to display one variable quantitative data.


• The MINITAB statistical software was used to generate the histogram in the next slide.

• The histogram has seven classes.

• Classes for the weights are along the x-axis and frequencies are along the y-axis.

• The number at the top of each rectangular box, represents the frequency for the class.


Histogramwith 7 classes for theweights.


• Observations• From the histogram, the

classes (intervals) are 85 – 95, 95 – 105,105 – 115 etc. with corresponding frequencies of 1, 3, 4, etc.

• We will use this information to construct the group frequency distribution.


• Observations (continued)

• Observe that the upper class limit of 95 for the class 85 – 95 is listed as the lower class limit for the class 95 – 105.

• Since the value of 95 cannot be included in both classes, we will use the convention that the upper class limit is not included in the class.



• That is, the class 85 – 95 should be interpreted as having the values 85 and up to 95 but not including the value of 95.

• Using these observations, the grouped frequency distribution is constructed from the histogram and is given on the next slide.



• In the grouped frequency distribution, the sum of the relative frequencies did not add up to 1. This is due to rounding to four decimal places.

• The same observation should be noted for the cumulative relative frequency column.

Creating a Histogram

It is an iterative process—try and try again.

What bin size should you use?

• Not too many bins with either 0 or 1 counts

• Not overly summarized that you lose all the information

• Not so detailed that it is no longer summary

Rule of thumb: Start with 5 to10 bins.

Look at the distribution and refine your bins.

(There isn’t a unique or “perfect” solution.)

Not summarized enough

Too summarized

Same data set

Histograms

• Frequency Distributions• Example

Definitions

Lower Class Limits

are the smallest numbers that can actually belong to different classes

Lower Class Limits

are the smallest numbers that can actually belong to different classes

Lower ClassLimits

Upper Class Limits

are the largest numbers that can actually belong to different classes

Upper ClassLimits

Class Boundaries

are the numbers used to separate classes, but without the gaps created by class limits

Class Boundaries

number separating classes

- 0.5

99.5

199.5

299.5

399.5

499.5

Class Boundaries

number separating classes

ClassBoundaries

- 0.5

99.5

199.5

299.5

399.5

499.5

Class Midpoints or Class Mark

midpoints of the classesClass midpoints can be found by adding the lower class limit to the upper class limit and dividing the sum by two.

Class Midpoints

midpoints of the classes

ClassMidpoints

49.5

149.5

249.5

349.5

449.5

Class Width

is the difference between two consecutive lower class limits or two consecutive lower class boundaries

Class Width

100

100

100

100

100

Summary of Terminology• Class - non-overlapping intervals the data is

divided into.• Class Limits –The smallest and largest

observed values in a given class.• Class Boundaries – Fall halfway between the

upper class limit for the smaller class and the lower class limit for larger class. Used to close the gap between classes.

• Class Width – The difference between the class boundaries for a given class.

• Class mark – The midpoint of a class.

Constructing A Frequency Table

1. Decide on the number of classes (should be between 5 and 20) .

2. Calculate (round up).

3. Starting point: Begin by choosing a lower limit of the first class.

4. Using the lower limit of the first class and class width, proceed to list the lower class limits.

5. List the lower class limits in a vertical column and proceed to enter the upper class limits.

6. Go through the data set putting a tally in the appropriate class for each data value.

class width (highest value) – (lowest value)

number of classes

Histogram

Then to complete the Histogram, graph the Frequency Table data.

Frequency Histogram vs Relative Frequency Histogram

A bar graph in which the horizontal scale represents the classes of data values and the vertical scale represents the frequencies.


Has the same shape and horizontal scale as a histogram, but the vertical scale is marked with relative frequencies.

Histograms - Facts• Histograms are useful when the

data values are quantitative.• A histogram gives an estimate

of the shape of the distribution of the population from which the sample was taken.

• If the relative frequencies were plotted along the vertical axis to produce the histogram, the shape will be the same as when the frequencies are used.

Making Histograms on the TI-83/84

Use of Stat Plots on the TI-83/84

Raw Data: 548, 405, 375, 400, 475, 450, 412

375, 364, 492, 482, 384, 490, 492

490, 435, 390, 500, 400, 491, 945

435, 848, 792, 700, 572, 739, 572

Frequency Table Data:

Class Limits Frequency

350 to < 450

450 to < 550

550 to < 650

650 to < 750

750 to < 850

850 to < 950

11

10

2

2

2

1

STEM AND LEAF PLOTQuantitative Data

Stem-and-Leaf Plots• What is a stem-and-leaf plot? A

stem-and-leaf plot is a data plot that uses part of a data value as the stem to form groups or classes and part of the data value as the leaf.

• Most often used for small or medium sized data sets. For larger data sets, histograms do a better job.

• Note: A stem-and-leaf plot has an advantage over a grouped frequency table or hostogram, since a stem-and-leaf plot retains the actual data by showing them in graphic form.

StemplotsHow to make a stemplot:

1) Separate each observation into a stem, consisting of all but the final (rightmost) digit, and a leaf, which is that remaining final digit. Stems may have as many digits as needed. Use only one digit for each leaf—either round or truncate the data values to one decimal place after the stem.

2) Write the stems in a vertical column with the smallest value at the top, and draw a vertical line at the right of this column.

3) Write each leaf in the row to the right of its stem, in increasing order out from the stem.

Original data: 9, 9, 22, 32, 33, 39, 39, 42, 49, 52, 58, 70

STEM LEAVES

Include key – how to read the stemplot.

0|9 = 9

Stem-and-Leaf Plots

• Example: Consider the following values – 96, 98, 107, 110, and 112. Construct a stem-and-leaf plot by using the units digits as the leaves.

Stem-and-Leaf Plot

Stems and leaves for the data values.

Stem-and-leaf plot for the data values.

Stem Leaf

09 6 810 711 0 2

Key: 09|6 = 96

Your Turn: Stem-and-Leaf Plots

• A sample of the number of admissions to a psychiatric ward at a local hospital during the full phases of the moon is as follows: 22, 30, 21, 27, 31, 36, 20, 28, 25, 33, 21, 38, 32, 35, 26, 19, 43, 30, 30, 34, 27, and 41.

• Display the data in a stem-and-leaf plot with the leaves represented by the unit digits.

Stem-and-Leaf Plot

Stem Leaf

1 92 0 1 1 2 5 6 7 7 83 0 0 0 1 2 3 4 5 6 84 1 3

Key: 1|9 = 19

Variations of the StemPlot

• Splitting Stems – (too few stems or classes) Split stems to double the number of stems when all the leaves would otherwise fall on just a few stems.

• Each stem appears twice.• Leaves 0-4 go on the 1st stem and leaves 5-9 go on

the 2nd stem.• Example: data –

120,121,121,123,124,124,125,125,125,126,126,128,129,130,132,

132,133,134,134,134,135,137,138,138,138,139

StemPlot StemPlot (splitting stems)

12 0 1 13445556689 12 0 1 1344

13 0223444578889 12 5556689

13 0223444

13 578889

Stemplots are quick and dirty histograms that can easily be

done by hand, therefore, very convenient for back of the

envelope calculations. However, they are rarely found in

scientific or laymen publications.

Stemplots versus Histograms

Stemplots versus Histograms

• Stem-and-leaf displays show the distribution of a quantitative variable, like histograms do, while preserving the individual values.

• Stem-and-leaf displays contain all the information found in a histogram and, when carefully drawn, satisfy the area principle and show the distribution.

58

Stem-and-Leaf Example

• Compare the histogram and stem-and-leaf display for the pulse rates of 24 women at a health clinic. Which graphical display do you prefer?

5 6

6 0 4 4 4

6 8 8 8 8

7 2 2 2 2

7 6 6 6 6

8 0 0 0 0 4 4

8 8

4

4

4 8 2 6 0

4 8 2 6 0

4 8 2 6 0

6 0 8 2 6 0 85 6 6 7 7 8 8

Key: 5|6 = 56

DOTPLOTSQuantitative Data

Dot Plots

• What is a dot plot? A dot plot is a plot that displays a dot for each value in a data set along a number line. If there are multiple occurrences of a specific value, then the dots will be stacked vertically.

Dotplots

• A dotplot is a simple display. It just places a dot along an axis for each case in the data.

• The dotplot to the right shows Kentucky Derby winning times, plotting each race as its own dot.

• You might see a dotplot displayed horizontally or vertically.

Dot Plot Example:

• The following data shows the length of 50 movies in minutes. Construct a dot plot for the data.

• 64, 64, 69, 70, 71, 71, 71, 72, 73, 73, 74, 74, 74, 74, 75, 75, 75, 75, 75, 75, 76, 76, 76, 77, 77, 78, 78, 79, 79, 80, 80, 81, 81, 81, 82, 82, 82, 83, 83, 83, 84, 86, 88, 89, 89, 90, 90, 92, 94, 120

Figure 2-5

Dot Plots – Your Turn

The following frequency distribution shows the number of defectives observed by a quality control officer over a 30 day period. Construct a dot plot for the data.

Dot Plots – Solution

Ogive - Cumulative Frequency Curve

Cumulative Frequency and the Ogive• Histogram displays the distribution of a quantitative variable.

It tells little about the relative standing (percentile, quartile, etc.) of an individual observation.

• For this information, we use a Cumulative Frequency graph, called an Ogive (pronounced O-JIVE).

• The Pth percentile of a distribution is a value such that P% of the data fall at or below it.

Cumulative Frequency

• What is a cumulative frequency for a class? The cumulative frequency for a specific class in a frequency table is the sum of the frequencies for all values at or below the given class.

Cumulative Frequency

Constructing an Ogive

1. Make a frequency table and add a cumulative frequency column.

2. To fill in the cumulative frequency column, add the counts in the frequency column that fall in or below the current class interval.

3. Label and scale the axes and title the graph. Horizontal axis “classes” and vertical axis “cumulative frequency or relative cumulative frequency”.

4. Begin the ogive at zero on the vertical axis and lower boundary of the first class on the horizontal axis. Then graph each additional Upper class boundary vs. cumulative frequency for that class.

Ogive

• A line graph that depicts cumulative frequencies.

• Used to Find Quartiles and Percentiles.

Example: Cumulative Frequency Curve

• The frequencies of the scores of 80 students in a test are given in the following table. Complete the corresponding cumulative frequency table.

• A suitable table is as follows:

Example continued• The information provided by a cumulative frequency table

can be displayed in graphical form by plotting the cumulative frequencies given in the table against the upper class boundaries, and joining these points with a smooth.

• The cumulative frequency curve corresponding to the data is as follows:

Your Turn:• The results obtained by 200 students in a mathematics

test are given in the following table.

Draw a cumulative frequency curve and use it to estimate

a) The median mark

b) The number of students who scored less than 22 marks

c) The pass mark if 120 students passed the test

d) The min. mark required to obtain an A grade if 10% of the students received an A grade.

Solution• The required cumulative frequency curve is as follows:

a) The median mark: median mark is 26

b) The number of students who scored less than 22 marks: approximately 69 students scored less than 22 marks

c) The pass mark if 120 students passed the test: pass mark is 28

d) The min. mark required to obtain an A grade if 10% of the students received an A grade: min. mark required for an A is 38

Percentiles

• Explanation of the term – percentiles: Percentiles are numerical values that divide an ordered data set into 100 groups of values with at most 1% of the data values in each group.

• The kth percentile is the number that falls above k% of the data.

Percentiles

• Explanation of the term – kth

percentile: the kth percentile for an ordered array of numerical data is a numerical value Pk (say) such that k% of the data values are smaller than or equal to Pk, and at most (100 – k)% of the data values are larger than Pk.

• The idea of the kth percentile is illustrated on the next slide.

Percentile Corresponding to a Given Data Value

• The percentile corresponding to a given data value, say x, in a set is obtained by using the following formula.

%100

or at

setdatainvaluesofNumber

xbelowvaluesofNumberPercentile

• Example: The shoe sizes, in whole numbers, for a sample of 12 male students in a statistics class were as follows: 13, 11, 10, 13, 11, 10, 8, 12, 9, 9, 8, and 9.

• What is the percentile rank for a shoe size of 12?


• Solution: First, we need to arrange the values from smallest to largest.

• The ordered array is given below: 8, 8, 9, 9, 9, 10, 10, 11, 11, 12, 13, 13.

• Observe that the number of values at or below the value of 12 is 10.


• Solution (continued): The total number of values in the data set is 12.

• Thus, using the formula, the corresponding percentile is:


The value of 12 corresponds to approximately the 83rd percentile.

The value of 12 corresponds to approximately the 83rd percentile.

• Assume that we want to determine what data value falls at some general percentile Pk.

• The following steps will enable you to find a general percentile Pk for a data set.

• Step 1: Order the data set from smallest to largest.

• Step 2: Compute the position c of the percentile. To compute the value of c, use the following formula:

Procedure for Finding a Data Value for a Given Percentile


•Step 1: If c is not a whole number, round up to the next whole number.

• Locate this position in the ordered set.

• The value in this location is the required percentile.

•Step 1: If c is not a whole number, round up to the next whole number.




•Step 2: If c is a whole number.



•Step 2: If c is a whole number.



• Example: The data given below represents the 19 countries with the largest numbers of total Olympic medals – excluding the United States, which had 101 medals – for the 1996 Atlanta games. Find the 65th percentile for the data set.

• 63, 65, 50, 37, 35, 41, 25, 23, 27, 21, 17, 17, 20, 19, 22, 15, 15, 15, 15.


• Solution: First, we need to arrange the data set in order. The ordered set is: .

• 15, 15, 15, 15, 17, 17, 19, 20, 21, 22, 23, 25, 27, 35, 37, 41, 50, 63, 65.

• Next, compute the position of the percentile.

• Here n = 19, k = 65.• Thus, c = (19 65)/100 = 12.35.• We need to round up to a value 13.


• Solution (continued): Thus, the 13th value in the ordered data set will correspond to the 65th percentile.

• That is P65 = 27.• Question: Why does a percentile

measure relative position?


Display of the 65th Percentile along with the data values.

Question: Why does a percentile measure Relative Position?

Question: Why does a percentile measure Relative Position?

• Referring to the diagram, observe that the value of 27 is such that at most 65% of the data values are smaller than 27 and at most 35% of the values are larger than 27.

•This shows that the percentile value of 27 is a measure of location.

•Thus, the percentile gives us an idea of the relative position of a value in an ordered data set.

• Deciles and quartiles are special percentiles.

• Deciles divide an ordered data set into 10 equal parts.

• Quartiles divide the ordered data set into 4 equal parts.

• We usually denote the deciles by D1, D2, D3, … , D9.

• We usually denote the quartiles by Q1, Q2, and Q3.

Special Percentiles – Deciles and Quartiles

Quick Tip:

• There are 9 deciles and 3 quartiles.

• Q1 = first quartile = P25• Q2 = second quartile = P50• Q3 = third quartile = P75• D1 = first decile = P10• D2 = second decile = P20 . . .• D9 = ninth decile = P90

Think Before You Draw, Again

• Remember the “Make a picture” rule? • Now that we have options for data

displays, you need to Think carefully about which type of display to make.

• Before making a stem-and-leaf display, a histogram, or a dotplot, check the• Quantitative Data Condition: The data

are values of a quantitative variable whose units are known.

Shape, Center, and Spread

• When describing a distribution, make sure to always tell about three things: shape, center, and spread…

• Actually you should comment on four things when describing a distribution. The three above and any deviations from the shape.

• These deviations from the shape are called ‘outliers’ and will be discussed later.

What is the Shape of the Distribution?

1. Does the histogram have a single, central hump or several separated humps?

2. Is the histogram symmetric?

3. Do any unusual features stick out?

Humps

1. Does the histogram have a single, central hump or several separated bumps?

• Humps in a histogram are called modes or peaks.

• A histogram with one main peak is dubbed unimodal; histograms with two peaks are bimodal; histograms with three or more peaks are called multimodal.

Humps (cont.)

• A bimodal histogram has two apparent peaks:

Humps (cont.)

• A histogram that doesn’t appear to have any mode and in which all the bars are approximately the same height is called uniform:

Uniform or Rectangular Distribution

• A distribution in which every class has equal frequency. A uniform distribution is symmetrical with the added property that the bars are the same height.

Symmetry

2. Is the histogram symmetric?• If you can fold the histogram along a vertical line

through the middle and have the edges match pretty closely, the histogram is symmetric.

Symmetrical Distribution

• In a symmetrical distribution, the data values are evenly distributed on both sides of the mean.

• When the distribution is unimodal, the mean, the median, and the mode are all equal to one another and are located at the center of the distribution.

Symmetrical Distribution

Symmetry (cont.)• The (usually) thinner ends of a distribution are called

the tails. If one tail stretches out farther than the other, the histogram is said to be skewed to the side of the longer tail.

• In the figure below, the histogram on the left is said to be skewed left, while the histogram on the right is said to be skewed right.

Skewed Right Distribution

• In a skewed right distribution, most of the data values fall to the left of the mean, and the “tail” of the distribution is to the right.

• The mean is to the right of the median and the mode is to the left of the median.

Skewed Right Distribution

Skewed Right

Skewed Left Distribution

• In a skewed left distribution, most of the data values fall to the right of the mean, and the “tail” of the distribution is to the left.

• The mean is to the left of the median and the mode is to the right of the median.

Skewed Left Distribution

Skewed Left

Anything Unusual?

3. Do any unusual features stick out?• Sometimes it’s the unusual features

that tell us something interesting or exciting about the data.

• You should always mention any stragglers, or outliers, that stand off away from the body of the distribution.

• Are there any gaps in the distribution? If so, we might have data from more than one group.

Anything Unusual? (cont.)

• The following histogram has outliers—there are three cities in the leftmost bar:

Deviations from the Overall Pattern

• Outliers – An individual observation that falls outside the overall pattern of the distribution. Extreme Values – either high or low.

• Causes:

1. Data Mistake

2. Special nature of some observations

Alaska Florida

Outliers

An important kind of deviation is an outlier. Outliers are

observations that lie outside the overall pattern of a

distribution. Always look for outliers and try to explain them.

The overall pattern is fairly

symmetrical except for two

states clearly not belonging

to the main trend. Alaska

and Florida have unusual

representation of the

elderly in their population.

A large gap in the

distribution is typically a

sign of an outlier.

Other Common Terms

• Peak – high bar• Valley – between 2 peaks• Gap – no data

Numerical Data Properties

Central Tendency (center)

Variation (spread)

Shape

Examples – Describing DistributionsIt’s often a good idea to think about what the distribution of a data set might look like

before we collect the data. What do you think the distribution of each of the following data sets will look like? Be sure to discuss its shape. Where do you think the center might be? How spread out do you think the values will be?

1. Number of Miles run by Saturday morning joggers at a park.• Roughly symmetric, slightly skewed right. Center around 3 miles. Few over 10

miles.

2. Hours spent by U.S. adults watching football on Thanksgiving Day.• Bimodal. Center between 1 and 2 hours. Many people watch no football, others

watch most of one or more games. Probably only a few values over 5 hours.

3. Amount of winnings of all people playing a particular state’s lottery last week.• Strongly skewed to the right, with almost everyone at $0, a few small prizes, with

the winner an outlier.

4. Ages of the faculty members at your school.• Fairly symmetric, somewhat uniform, perhaps slightly skewed to the right. Center

in the 40’s. Few ages below 25 or above 70.

5. Last digit of phone numbers on your campus.• Uniform, symmetric. Center near 5. Roughly equal counts for each digit 0-9.

Where is the Center of the Distribution?

• If you had to pick a single number to describe all the data what would you pick?

• It’s easy to find the center when a histogram is unimodal and symmetric—it’s right in the middle.

• On the other hand, it’s not so easy to find the center of a skewed histogram or a histogram with more than one mode.

Measures of Central Tendency

• A measure of central tendency for a collection of data values is a number that is meant to convey the idea of centralness for the data set.

• The most commonly used measures of central tendency for sample data are the: mean, median, and mode.

The Mean

• Explanation of the term – mean: The mean of a set of numerical (data) values is the (arithmetic) average for the set of values.

• NOTE: When computing the value of the mean, the data values can be population values or sample values.

• Hence we can compute either the population mean or the sample mean

The Mean

• Explanation of the term – population mean: If the numerical values are from an entire population, then the mean of these values is called the population mean.

• NOTATION: The population mean is usually denoted by the Greek letter µ (read as “mu”).

The Mean

• Explanation of the term – sample mean: If the numerical values are from a sample, then the mean of these values is called the sample mean.

• NOTATION: The sample mean is usually denoted by (read as “x-bar”).

x

The Mean -- Example

• Example: What is the mean of the following 11 sample values?

3 8 6 14 0 -4 0 12 -7 0 -10

The Mean -- Example (Continued)

• Solution:

2 11

)10(0)7(120)4(014683

x

The Mean• Nonresistant – The mean is sensitive to the influence of

extreme values and/or outliers. Skewed distributions pull the mean away from the center towards the longer tail.

• The mean is located at the balancing point of the histogram. For a skewed distribution, is not a good measure of center.

The Mean

• Nonresistant – Example

• Example – Data: {1,2,3,4,5,6,7}• The mean is 4• Add an outlier {1,2,3,4,5,6,7,50}• New median is 9.75 – large affect

Quick Tip:

• When a data set has a large number of values, we sometimes summarize it as a frequency table. The frequencies represent the number of times each value occurs.

• When the mean is calculated from a frequency table it is often an approximation, because the raw data is sometimes not known.

Calculating Means

• TI-83/84 1-Var Stats• Using raw data• Using Frequency table data

Calculating Means on TI-83/84

Raw Data: 548, 405, 375, 400, 475, 450, 412

375, 364, 492, 482, 384, 490, 492

490, 435, 390, 500, 400, 491, 945

435, 848, 792, 700, 572, 739, 572


Note: The (ungrouped) classes are the observed values themselves.


• Grouped Frequency Table Data:

Class Limits Frequency

350 to < 450450 to < 550 550 to < 650650 to < 750750 to < 850850 to < 950

11102221

The Median• Explanation of the term –

median: The median of a set of numerical (data) values is that numerical value in the middle when the data set is arranged in order.

• NOTE: When computing the value of the median, the data values can be population values or sample values.

• Hence we can compute either the population median or the sample median.

Center of a Distribution -- Median

• The median is the value with exactly half the data values below it and half above it.• It is the middle data

value (once the data values have been ordered) that divides the histogram into two equal areas

• It has the same unitsas the data

Quick Tip:

• When the number of values in the data set is odd, the median will be the middle value in the ordered array.

• When the number of values in the data set is even, the median will be the average of the two middle values in the ordered array.

The Median -- Example

• Example: What is the median for the following sample values?

3 8 6 14 0 -4 2 12 -7 -1 -10

The Median -- Example (Continued)

• Solution: First of all, we need to arrange the data set in order. The ordered set is:

-10 -7 -4 -1 0 2 3 6 8 12 14

6th value

The Median -- Example (Continued)

• Solution (Continued): Since the number of values is odd, the median will be found in the 6th position in the ordered set (To find; data number divided by 2 and round up, 11/2 = 5.5⇒6).

• Thus, the value of the median is 2.

The Median -- Example

• Example: Find the median age for the following eight college students.

23 19 32 25 26 22 24 20

The Median – Example (continued)

• Example: First we have to order the values as shown below.

19 20 22 23 24 25 26 32

The Median – Example (continued)

• Example: Since there is an even number of ages, the median will be the average of the two middle values (To find; data number divided by 2, that number and the next are the two middle numbers, 8/2 = 4⇒4th & 5th are the middle numbers).

• Thus, median = (23 + 24)/2 = 23.5.

The MedianThe median is the midpoint of a distribution—the number such

that half of the observations are smaller and half are larger.

1. Sort observations from smallest to largest.n = number of observations

______________________________

1 1 0.62 2 1.23 3 1.64 4 1.95 5 1.56 6 2.17 7 2.38 8 2.39 9 2.5

10 10 2.811 11 2.912 3.313 3.414 1 3.615 2 3.716 3 3.817 4 3.918 5 4.119 6 4.220 7 4.521 8 4.722 9 4.923 10 5.324 11 5.6

n = 24 n/2 = 12 &13

Median = (3.3+3.4) /2 = 3.35

3. If n is even, the median is the mean of the two center observations

1 1 0.62 2 1.23 3 1.64 4 1.95 5 1.56 6 2.17 7 2.38 8 2.39 9 2.5

10 10 2.811 11 2.912 12 3.313 3.414 1 3.615 2 3.716 3 3.817 4 3.918 5 4.119 6 4.220 7 4.521 8 4.722 9 4.923 10 5.324 11 5.625 12 6.1

n = 25 n/2 = 25/2 = 12.5=13 Median = 3.4

2. If n is odd, the median is observation n/2 (round up) down the list

The Median

• Resistant – The median is said to be resistant, because extreme values and/or outliers have little effect on the median.

• Example – Data: {1,2,3,4,5,6,7}• The median is 4• Add an outlier {1,2,3,4,5,6,7,50}• New median is 4.5 – very little affect

The Mode

• Explanation of the term – mode: The mode of a set of numerical (data) values is the most frequently occurring value in the data set.

Quick Tip:

• If all the elements in the data set have the same frequency of occurrence, then the data set is said to have no mode.

Example of data set with no mode.

Quick Tip:

• If the data set has one value that occurs more frequently than the rest of the values, then the data set is said to be unimodal.

Example ofA UnimodalData set.

Example ofA UnimodalData set.

Quick Tip:

• If two data values in the set are tied for the highest frequency of occurrence, then the data set is said to be bimodal.

Example of a bimodal set of data.

Example of a bimodal set of data.

Summary Measures of Center

How Spread Out is the Distribution?

• Variation matters, and Statistics is about variation.

• Are the values of the distribution tightly clustered around the center or more spread out?

• Always report a measure of spread along with a measure of center when describing a distribution numerically.

Measures of Spread

• A measure of variability for a collection of data values is a number that is meant to convey the idea of spread for the data set.

• The most commonly used measures of variability for sample data are the: range interquartile range variance or standard

deviation

Spread: Home on the Range

• The range of the data is the difference between the maximum and minimum values:

Range = max – min• A disadvantage of the range is that a

single extreme value can make it very large and, thus, not representative of the data overall.

Range

• The range is affected by outliers (large or small values relative to the rest of the data set).

• The range does not utilize all the information in the data set only the largest and smallest values.

• Thus it is not a very useful measure of spread or variation.

Spread: The Interquartile Range

• A better way to describe the spread of a set of data might be to ignore the extremes and concentrate on the middle of the data.

• The interquartile range (IQR) lets us ignore extreme data values and concentrate on the middle of the data.

• To find the IQR, we first need to know what quartiles are…

Spread: The Interquartile Range (cont.)

• Quartiles divide the data into four equal sections. • One quarter of the data lies below the

lower quartile, Q1• One quarter of the data lies above the

upper quartile, Q3.• The quartiles border the middle half of

the data.

• The difference between the quartiles is the interquartile range (IQR), so

IQR = upper quartile – lower quartile

Finding Quartiles1. Order the Data

2. Find the median, this divides the data into a lower and upper half (the median itself is in neither half).

3. Q1 is then the median of the lower half.

4. Q3 is the median of the upper half.

5. Example

Even dataQ1=27, M=39, Q3=50.5

IQR = 50.5 – 27 = 23.5

Odd dataQ1=35, M=46, Q3=54

IQR = 54 – 35 = 19

The Interquartile Range

• The following depicts the idea of the interquartile range.

IQR = Q3 - Q1

Spread: The Interquartile Range (cont.)

• The lower and upper quartiles are the 25th and 75th percentiles of the data, so…

• The IQR contains the middle 50% of the values of the distribution, as shown in figure:

M = median = 3.4

Q1= first quartile = 2.2

Q3= third quartile = 4.35

1 1 0.62 2 1.23 3 1.64 4 1.95 5 1.56 6 2.17 7 2.38 1 2.39 2 2.5

10 3 2.811 4 2.912 5 3.313 3.414 1 3.615 2 3.716 3 3.817 4 3.918 5 4.119 6 4.220 7 4.521 1 4.722 2 4.923 3 5.324 4 5.625 5 6.1

Example IQR

The first quartile, Q1, is the value in

the sample that has 25% of the data

at or below it.

The third quartile, Q3, is the value in

the sample that has 75% of the data

at or below it.

IQR=Q3-Q1

=4.35-2.2 =2.15

Your Turn:

• The following scores for a statistics 10-point quiz were reported. What is the value of the interquartile range?

7 8 9 6 8 0 9 9 9

0 0 7 10 9 8 5 7 9

Solution: IQR = 3

Calculator - IQR

• TI-83 Solution: The following shows the descriptive statistics output.

Interquartile range = Q3 – Q1 = 9 – 6 = 3.Interquartile range = Q3 – Q1 = 9 – 6 = 3.

5-Number Summary

• The 5-number summary of a distribution reports its median, quartiles, and extremes (maximum and minimum)

• The 5-number summary for the recent tsunami earthquake Magnitudes looks like this:

• Obtain 5-number summary from 1-Var Stats

What About Spread? The Standard Deviation

• A more powerful measure of spread than the IQR is the standard deviation, which takes into account how far each data value is from the mean.

• A deviation is the distance that a data value is from the mean. • Since adding all deviations together

would total zero, we square each deviation and find an average of sorts for the deviations.

What About Spread? The Standard Deviation (cont.)

• The variance, notated by s2, is found by summing the squared deviations and (almost) averaging them:

• Used to calculate Standard Deviation.• The variance will play a role later in our

study, but it is problematic as a measure of spread - it is measured in squared units - serious disadvantage!

2

2

1

y ys

n

What About Spread? The Standard Deviation (cont.)

• The standard deviation, s, is just the square root of the variance and is measured in the same units as the original data.

2

1

y ys

n

Procedure for Calculating the Standard Deviation using Formula

1. Compute the mean .

2. Subtract the mean from each individual value to get a list of the deviations from the mean .

3. Square each of the differences to produce the square of the deviations from the mean .

4. Add all of the squares of the deviations from the mean to get .

5. Divide the sum by . [variance]

6. Find the square root of the result.

x

x x

2x x

2x x

2x x 1n

Example:

• Find the standard deviation of the Mulberry Bank customer waiting times. Those times (in minutes) are 1, 3, 14.

Calculating Standard Deviation on the TI-83/84

• Use 1-Var Stats• Sx is the sample standard deviation

• σx is the population standard deviation

Properties of Standard Deviation

• Measures spread about the mean and should only be used to describe the spread of a distribution when the mean is used to describe the center (ie. symmetrical distributions).

• The value of s is positive. It is zero only when all of the data values are the same number. Larger values of s indicate greater amounts of variation.

• Nonresistant, s can increase dramatically due to extreme values or outliers.

• The units of s are the same as the units of the original data. One reason s is preferred to s2.

Thinking About Variation

• Since Statistics is about variation, spread is an important fundamental concept of Statistics.

• Measures of spread help us talk about what we don’t know.

• When the data values are tightly clustered around the center of the distribution, the IQR and standard deviation will be small.

• When the data values are scattered far from the center, the IQR and standard deviation will be large.

Summarizing Symmetric Distributions -- The Mean• When we have symmetric data, there is an

alternative other than the median.• If we want to calculate a number, we can

average the data.• We use the Greek letter sigma to mean

“sum” and write:

The formula says that to find the mean, we add up all the values of the variable and divide by the number of data values, n.

yTotaly

n n

Summarizing Symmetric Distributions -- The Mean (cont.)

• The mean feels like the center because it is the point where the histogram balances:

Mean or Median?

• Because the median considers only the order of values, it is resistant to values that are extraordinarily large or small; it simply notes that they are one of the “big ones” or “small ones” and ignores their distance from center.

• To choose between the mean and median, start by looking at the data. If the histogram is symmetric and there are no outliers, use the mean.

• However, if the histogram is skewed or with outliers, you are better off with the median.

Mean and median for skewed distributions

Mean and median for a symmetric distribution

Left skew Right skew

MeanMedian

Mean Median

MeanMedian

Comparing the mean and the median

•The mean and the median are the same only if the distribution is symmetrical.

•The median is a measure of center that is resistant to skew and outliers. The

mean is not.

The median, on the other hand,

is only slightly pulled to the right

by the outliers (from 3.4 to 3.6).

The mean is pulled to the

right a lot by the outliers

(from 3.4 to 4.2).

P

erc

en

t o

f p

eo

ple

dyi

ng

Mean and Median of a Distribution with Outliers

3.4x

Without the outliers

4.2x

With the outliers

Example

• Observed mean =2.28, median=3, mode=3.1

• What is the shape of the distribution and why?

Example

Solution: Skewed Left

Right-SkewedLeft-Skewed Symmetric

Mean = Median = ModeMean Median Mode Mode Median Mean

Conclusion – Mean or Median?

• Mean – use with symmetrical distributions (no outliers), because it is nonresistant.

• Median – use with skewed distribution or distribution with outliers, because it is resistant.

Tell -- Draw a Picture

• When telling about quantitative variables, start by making a histogram or stem-and-leaf display and discuss the shape of the distribution.

Tell -- Shape, Center, and Spread

• Next, always report the shape of its distribution, along with a center and a spread.• If the shape is skewed, report the

median and IQR.• If the shape is symmetric, report the

mean and standard deviation and possibly the median and IQR as well.

Tell -- What About Unusual Features?

• If there are multiple modes, try to understand why. If you identify a reason for the separate modes, it may be good to split the data into two groups.

• If there are any clear outliers and you are reporting the mean and standard deviation, report them with the outliers present and with the outliers removed. The differences may be quite revealing.• Note: The median and IQR are not

likely to be affected by the outliers.

What Can Go Wrong?

• Don’t make a histogram of a categorical variable—bar charts or pie charts should be used for categorical data.

• Don’t look for shape, center, and spread of a bar chart.

What Can Go Wrong? (cont.)

• Don’t use bars in every display—save them for histograms and bar charts.

• Below is a badly drawn plot and the proper histogram for the number of juvenile bald eagles sighted in a collection of weeks:


• Choose a bin width appropriate to the data.• Changing the bin width changes the appearance of the

histogram:


• Don’t forget to do a reality check – don’t let the calculator do the thinking for you.

• Don’t forget to sort the values before finding the median or percentiles.

• Don’t worry about small differences when using different methods.

• Don’t compute numerical summaries of a categorical variable.

• Don’t report too many decimal places.• Don’t round in the middle of a calculation.• Watch out for multiple modes• Beware of outliers• Make a picture … make a picture . . . make a picture !!!

What have we learned?

• We’ve learned how to make a picture for quantitative data to help us see the story the data have to Tell.

• We can display the distribution of quantitative data with a histogram, stem-and-leaf display, or dotplot.

• We’ve learned how to summarize distributions of quantitative variables numerically.• Measures of center for a distribution include the median

and mean.• Measures of spread include the range, IQR, and standard

deviation.• Use the median and IQR when the distribution is skewed.

Use the mean and standard deviation if the distribution is symmetric.

What have we learned? (cont.)

• We’ve learned to Think about the type of variable we are summarizing.• All methods of this chapter assume the

data are quantitative.• The Quantitative Data Condition

serves as a check that the data are, in fact, quantitative.

Assignment

• Exercises pg. 72 – 79: #5 - 18, 30 - 33, 43, 44, 48

• Read Ch-4, pg. 44 - 71

Chapter 4 Displaying and Summarizing Quantitative Data.

Documents

Transcript of Chapter 4 Displaying and Summarizing Quantitative Data.