+ Chapter 1 Exploring Data

68 

Loading....

Loading....

Loading....

Loading....

Loading....

Full text

(1)

+

Chapter 1

Exploring Data

Introduction:

Data Analysis: Making Sense of

Data

1.1

Analyzing Categorical Data

1.2

Displaying Quantitative Data with Graphs

(2)

+

Introduction

Data Analysis: Making Sense of Data

After this section, you should be able to…

 DEFINE “Individuals” and “Variables”

 DISTINGUISH between “Categorical” and “Quantitative” variables

 DEFINE “Distribution”

 DESCRIBE the idea behind “Inference”

(3)

+

Data

Analysi

s

Statistics

is the science of data.

Data Analysis

is the process of

organizing

,

displaying

,

summarizing

, and

asking questions

about data.

Definitions:

Individuals – objects (people, animals, things)

described by a set of data

Variable - any characteristic of an individual

Categorical Variable

– places an individual into one of several groups or categories.

Quantitative Variable

– takes numerical values for which it makes sense to find an average.

(4)

+

Read Example : Census at school ( P-3)

Now do Exercise 3 on P- 7

Answers:

a) Individuals: AP Stat students who completed a questionnaire on the first day of class.

b) Categorical: gender, handedness, favorite type of music Quantitative: height( inches), amount of time the students expects to spend on HW (mins) , the total value of coin(cents) c) Female, right-handed, 58 inches tall, spends 60

mins on HW, prefers alternative music, had 76 cents in her pocket.

(5)

+

Do Activity : Hiring Discrimination – It just won’t fly ……

Follow the directions on Page 5

Perform 5 repetitions of your simulation.

Turn in your results to your teacher.

(6)

+

A variable generally takes on many different values.

In data analysis, we are interested in how often a

variable takes on each value.

Definition:

Distribution –

tells us what values a variable

takes and how often it takes those values

2009 Fuel Economy Guide

MODEL MPG 1 2 3 4 5 6 7 8 9 Acura RL 22 Audi A6 Quattro 23 Bentley Arnage 14 BMW 5281 28 Buick Lacrosse 28 Cadillac CTS 25 Chevrolet Malibu 33 Chrysler Sebring 30 Dodge Avenger 30

2009 Fuel Economy Guide

MODEL MPG <new> 9 10 11 12 13 14 15 16 17 Dodge Avenger 30 Hyundai Elantra 33 Jaguar XF 25 Kia Optima 32 Lexus GS 350 26 Lincolon MKZ 28 Mazda 6 29 Mercedes-Benz E350 24 Mercury Milan 29

2009 Fuel Economy Guide

MODEL MPG <new> 16 17 18 19 20 21 22 23 24 Mercedes-Benz E350 24 Mercury Milan 29 Mitsubishi Galant 27 Nissan Maxima 26 Rolls Royce Phantom 18 Saturn Aura 33 Toyota Camry 31 Volkswagen Passat 29 Volvo S80 25 Variable of Interest: MPG Dotplot of MPG Distribution Example

(7)

+

2009 Fuel Economy Guide

MODEL MPG <new> 9 10 11 12 13 14 15 16 17 Dodge Avenger 30 Hyundai Elantra 33 Jaguar XF 25 Kia Optima 32 Lexus GS 350 26 Lincolon MKZ 28 Mazda 6 29 Mercedes-Benz E350 24 Mercury Milan 29

2009 Fuel Economy Guide

MODEL MPG <new> 16 17 18 19 20 21 22 23 24 Mercedes-Benz E350 24 Mercury Milan 29 Mitsubishi Galant 27 Nissan Maxima 26 Rolls Royce Phantom 18 Saturn Aura 33 Toyota Camry 31 Volkswagen Passat 29 Volvo S80 25 2009 Fuel Economy Guide

MODEL MPG 1 2 3 4 5 6 7 8 9 Acura RL 22 Audi A6 Quattro 23 Bentley Arnage 14 BMW 5281 28 Buick Lacrosse 28 Cadillac CTS 25 Chevrolet Malibu 33 Chrysler Sebring 30 Dodge Avenger 30 Add numerical summaries

Data

Analysi

s

Examine each variable by itself.

Then study

relationships among the variables.

Start with a graph or graphs

(8)

+

Data

Analysi

s

From Data Analysis to Inference

Population

Sample

Collect data from a

representative Sample...

Perform Data

Analysis, keeping probability in mind…

Make an Inference

(9)

+

Inference:

Inference

is the process of making a

conclusion about a population based on

a sample set of data.

(10)

+

Introduction

Data Analysis: Making Sense of Data

In this section, we learned that…

 A dataset contains information on individuals.

 For each individual, data give values for one or more variables.

 Variables can be categorical or quantitative.

 The distribution of a variable describes what values it takes and how often it takes them.

Inference is the process of making a conclusion about a population based on a sample set of data.

(11)
(12)

+

Looking Ahead…

We’ll learn how to analyze categorical data.

Bar Graphs

Pie Charts

Two-Way Tables

Conditional Distributions

We’ll also learn how to organize a statistical problem.

(13)

+

Section 1.1

Analyzing Categorical Data

After this section, you should be able to…

 CONSTRUCT and INTERPRET bar graphs and pie charts

 RECOGNIZE “good” and “bad” graphs

 CONSTRUCT and INTERPRET two-way tables

 DESCRIBE relationships between two categorical variables

 ORGANIZE statistical problems

(14)

+

An

alyzi

ng

Ca

tegorical

Da

ta

Categorical Variables

place individuals into one of

several groups or categories

 The values of a categorical variable are labels for the

different categories

 The distribution of a categorical variable lists the count or

percent of individuals who fall into each category.

Frequency Table

Format Count of Stations

Adult Contemporary 1556 Adult Standards 1196 Contemporary Hit 569 Country 2066 News/Talk 2179 Oldies 1060 Religious 2014 Rock 869 Spanish Language 750 Other Formats 1579 Total 13838

Relative Frequency Table

Format Percent of Stations

Adult Contemporary 11.2 Adult Standards 8.6 Contemporary Hit 4.1 Country 14.9 News/Talk 15.7 Oldies 7.7 Religious 14.6 Rock 6.3 Spanish Language 5.4 Other Formats 11.4 Total 99.9 Example, page 8 Count Percent Variable Values

(15)

+

An

alyzi

ng

Ca

tegorical

Da

ta

Displaying categorical data

Frequency tables can be difficult to read. Sometimes

is is easier to analyze a distribution by displaying it

with a

bar graph

or

pie chart

.

Frequency Table

Format Count of Stations

Adult Contemporary 1556 Adult Standards 1196 Contemporary Hit 569 Country 2066 News/Talk 2179 Oldies 1060 Religious 2014 Rock 869 Spanish Language 750 Other Formats 1579 Total 13838

Relative Frequency Table

Format Percent of Stations

Adult Contemporary 11.2 Adult Standards 8.6 Contemporary Hit 4.1 Country 14.9 News/Talk 15.7 Oldies 7.7 Religious 14.6 Rock 6.3 Spanish Language 5.4 Other Formats 11.4 Total 99.9

(16)

+

An

alyzi

ng

Ca

tegorical

Da

ta

Bar graphs compare several quantities by comparing

the heights of bars that represent those quantities.

Our eyes react to the

area

of the bars as well as

height. Be sure to make your bars equally wide.

Avoid the temptation to replace the bars with pictures

for greater appeal…this can be misleading!

Graphs: Good and Bad

Alternate Example

This ad for DIRECTV has multiple problems. How many can you point out?

(17)

+

Example: Bar graph

What personal media do you own?

Here are the percents of 15-18 year olds who own the following personal media devices.

a) Make a well-labeled bar graph. What do you see?

b) Would it be appropriate to make a pie chart for these data? Why or why not?

Device % who won

Cell Phone 85

MP3 player 83

Handheld Video game player

41

Laptop 38

(18)

+

An

alyzi

ng

Ca

tegorical

Da

ta

Two-Way Tables and Marginal Distributions

When a dataset involves two categorical variables, we begin by

examining the counts or percents in various categories for one

of the variables.

Definition:

Two-way Table – describes two categorical

variables, organizing counts according to a row

variable and a column variable.

Young adults by gender and chance of getting rich Female Male Total Almost no chance 96 98 194 Some chance, but probably not 426 286 712 A 50-50 chance 696 720 1416

A good chance 663 758 1421

Almost certain 486 597 1083

Total 2367 2459 4826

Example, p. 12

What are the variables described by this two-way table?

How many young

(19)

+

An

alyzi

ng

Ca

tegorical

Da

ta

Two-Way Tables and Marginal Distributions

Definition:

The Marginal Distribution of one of the categorical variables in a two-way table of counts is the distribution of values of that

variable among all individuals described by the table.

Note: Percents are often more informative than counts,

especially when comparing groups of different sizes.

To examine a marginal distribution,

1)Use the data in the table to calculate the marginal distribution (in percents) of the row or column totals. 2)Make a graph to display the marginal distribution.

(20)

+

Young adults by gender and chance of getting rich Female Male Total

Almost no chance 96 98 194

Some chance, but probably not 426 286 712

A 50-50 chance 696 720 1416 A good chance 663 758 1421 Almost certain 486 597 1083 Total 2367 2459 4826

An

alyzi

ng

Ca

tegorical

Da

ta

Two-Way Tables and Marginal Distributions

Response Percent Almost no chance 194/4826 = 4.0% Some chance 712/4826 = 14.8% A 50-50 chance 1416/4826 = 29.3% A good chance 1421/4826 = 29.4% Almost certain 1083/4826 = 22.4% Example, p. 13

Examine the marginal

distribution of chance of getting rich. 0 5 10 15 20 25 30 35

Almost none Some chance 50-50 chance Good chance Almost

certain

P

erc

ent

Survey Response

(21)

+

Relationships Between Categorical Variables

 Marginal distributions tell us nothing about the relationship

between two variables.

Definition:

A Conditional Distribution of a variable describes the values of that variable among individuals who have a specific value of another variable.

To examine or compare conditional distributions,

1)Select the row(s) or column(s) of interest.

2)Use the data in the table to calculate the conditional distribution (in percents) of the row(s) or column(s). 3)Make a graph to display the conditional distribution.

• Use a side-by-side bar graph or segmented bar

(22)

+

Young adults by gender and chance of getting rich Female Male Total

Almost no chance 96 98 194

Some chance, but probably not 426 286 712

A 50-50 chance 696 720 1416

A good chance 663 758 1421

Almost certain 486 597 1083

Total 2367 2459 4826

Two-Way Tables and Conditional Distributions

Response Male Almost no chance 98/2459 = 4.0% Some chance 286/2459 = 11.6% A 50-50 chance 720/2459 = 29.3% A good chance 758/2459 = 30.8% Almost certain 597/2459 = 24.3% Example, p. 15

Calculate the conditional distribution of opinion among males.

Examine the relationship between gender and opinion. 0 5 10 15 20 25 30 35 Almost no chance

Some chance50-50 chanceGood chance Almost

certain Pe rce n t Opinion

Chance of being wealthy by age 30

Males Female 96/2367 = 4.1% 426/2367 = 18.0% 696/2367 = 29.4% 663/2367 = 28.0% 486/2367 = 20.5% 0 5 10 15 20 25 30 35 Almost no chance Some chance 50-50 chance Good chance Almost certain Pe rce n t Opinion

Chance of being wealthy by age 30

Males Females 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Males Females P erce nt Opinion

Chance of being wealthy by age 30

Almost certain Good chance 50-50 chance Some chance Almost no chance

(23)

+

An

alyzi

ng

Ca

tegorical

Da

ta

Organizing a Statistical Problem

 As you learn more about statistics, you will be asked to solve

more complex problems.

 Here is a four-step process you can follow.

State: What’s the question that you’re trying to answer? Plan: How will you go about answering the question? What statistical techniques does this problem call for?

Do: Make graphs and carry out needed calculations.

Conclude: Give your practical conclusion in the setting of the real-world problem.

(24)

+

Do : P- 25 # 26.

State: Do the data support the idea that people who get angry easily tend to have more heart disease?

Plan: We suspect that people with different anger levels will have different rates of CHD, so we will compare the conditional

distributions of CHD for each anger level.

Do: We will display the conditional distributions in a table to compare the rate of CHD occurrence for each of the 3 anger levels

Conclude: The conditional distributions show that while CHD occurrence is quite small overall, the percent of the population with CHD does increase as eth anger level increases.

(25)

+

Section 1.1

Analyzing Categorical Data

In this section, we learned that…

 The distribution of a categorical variable lists the categories and gives the count or percent of individuals that fall into each category.

Pie charts and bar graphs display the distribution of a categorical variable.

 A two-way table of counts organizes data about two categorical variables.

 The row-totals and column-totals in a two-way table give the marginal distributions of the two individual variables.

 There are two sets of conditional distributions for a two-way table.

(26)

+

Section 1.1

Analyzing Categorical Data

In this section, we learned that…

 We can use a side-by-side bar graph or a segmented bar graph

to display conditional distributions.

 To describe the association between the row and column variables,

compare an appropriate set of conditional distributions.

 Even a strong association between two categorical variables can be

influenced by other variables lurking in the background.

 You can organize many problems using the four steps state, plan,

do, and conclude.

(27)

+

Looking Ahead…

We’ll learn how to display quantitative data.

Dotplots

Stemplots

Histograms

We’ll also learn how to describe and compare

distributions of quantitative data.

(28)

+

Section 1.2

Displaying Quantitative Data with Graphs

After this section, you should be able to…

 CONSTRUCT and INTERPRET dotplots, stemplots, and histograms

 DESCRIBE the shape of a distribution

 COMPARE distributions

 USE histograms wisely

(29)

+

1)Draw a horizontal axis (a number line) and label it with the variable name.

2)Scale the axis from the minimum to the maximum value. 3)Mark a dot above the location on the horizontal axis

corresponding to each data value.

Di

spl

ayin

g

Quantitative Data

Dotplots

 One of the simplest graphs to construct and interpret is a

dotplot. Each data value is shown as a dot above its location on a number line.

How to Make a Dotplot

Number of Goals Scored Per Game by the 2004 US Women’s Soccer Team

3 0 2 7 8 2 4 3 5 1 1 4 5 3 1 1 3 3 3 2 1 2 2 2 4 3 5 6 1 5 5 1 1 5

(30)

+

Examining the Distribution of a Quantitative Variable

 The purpose of a graph is to help us understand the data. After

you make a graph, always ask, “What do I see?”

In any graph, look for the overall pattern and for striking

departures from that pattern.

Describe the overall pattern of a distribution by its: •Shape

Center

Spread

Outliers

Note individual values that fall outside the overall pattern.

These departures are called outliers.

How to Examine the Distribution of a Quantitative Variable

Di

spl

ayin

g

Quantitative Data

Don’t forget your

SOCS!

(31)

+

Examine this data

 The table and dotplot below displays the Environmental

Protection Agency’s estimates of highway gas mileage in miles per gallon (MPG) for a sample of 24 model year 2009 midsize cars.

Di

spl

ayin

g

Quantitative Data

Describe the shape, center, and spread of

the distribution. Are there any outliers?

2009 Fuel Economy Guide

MODEL MPG 1 2 3 4 5 6 7 8 9 Acura RL 22 Audi A6 Quattro 23 Bentley Arnage 14 BMW 5281 28 Buick Lacrosse 28 Cadillac CTS 25 Chevrolet Malibu 33 Chrysler Sebring 30 Dodge Avenger 30

2009 Fuel Economy Guide

MODEL MPG <new> 9 10 11 12 13 14 15 16 17 Dodge Avenger 30 Hyundai Elantra 33 Jaguar XF 25 Kia Optima 32 Lexus GS 350 26 Lincolon MKZ 28 Mazda 6 29 Mercedes-Benz E350 24 Mercury Milan 29

2009 Fuel Economy Guide

MODEL MPG <new> 16 17 18 19 20 21 22 23 24 Mercedes-Benz E350 24 Mercury Milan 29 Mitsubishi Galant 27 Nissan Maxima 26 Rolls Royce Phantom 18 Saturn Aura 33 Toyota Camry 31 Volkswagen Passat 29 Volvo S80 25

(32)

+

Di

spl

ayin

g

Quantitative Data

Describing Shape

 When you describe a distribution’s shape, concentrate on

the main features. Look for rough symmetry or clear

skewness.

Definitions:

A distribution is roughly symmetric if the right and left sides of the graph are approximately mirror images of each other.

A distribution is skewed to the right (right-skewed) if the right side of the graph (containing the half of the observations with larger values) is much longer than the left side.

It is skewed to the left (left-skewed) if the left side of the graph is much longer than the right side.

(33)

+

You Do: Smart Phone Battery Life

Here is the estimated battery life for each of 9 different smart phones (in minutes) according to

http://cellphones.toptenreviews.com/smartphones/.

Make a dot plot.

* Then describe the shape, center, and spread. of the distribution. Are there any outliers?

Smart Phone Battery Life

(minutes) Apple iPhone 300 Motorola Droid 385 Palm Pre 300 Blackberry Bold 360 Blackberry Storm 330 Motorola Cliq 360 Samsung Moment 330 Blackberry Tour 300 HTC Droid 460

(34)

+

Solution:

300 300 300 330 330 360 360 385 460

Shape: There is a peak at 300 and the distribution has a long tail to the right (skewed to the right).

Center: The middle value is 330 minutes.

Spread: The range is 460 – 300 = 160 minutes.

Outliers: There is one phone with an unusually long battery life, the HTC Droid at 460 minutes.

BatteryLife (minutes)

300 340 380 420 460

(35)

+

Comparing Distributions

Some of the most interesting statistics questions

involve comparing two or more groups.

Always discuss shape, center, spread, and

possible outliers whenever you compare

distributions of a quantitative variable.

Example, page 32

Compare the distributions of

household size for these

two countries. Don’t forget

your SOCS!

( Compare each

characteristic)

P lac e U .K S ou th A fri ca

(36)

+

1)Separate each observation into a stem (all but the final

digit) and a leaf (the final digit).

2)Write all possible stems from the smallest to the largest in a vertical column and draw a vertical line to the right of the

column.

3)Write each leaf in the row to the right of its stem.

4)Arrange the leaves in increasing order out from the stem. 5)Provide a key that explains in context what the stems and leaves represent.

Di

spl

ayin

g

Quantitative Data

Stemplots (Stem-and-Leaf Plots)

 Another simple graphical display for small data sets is a

stemplot. Stemplots give us a quick picture of the distribution while including the actual numerical values.

(37)

+

Di

spl

ayin

g

Quantitative Data

Stemplots (Stem-and-Leaf Plots)

 These data represent the responses of 20 female AP

Statistics students to the question, “How many pairs of shoes do you have?” Construct a stemplot.

50 26 26 31 57 19 24 22 23 38 13 50 13 34 23 30 49 13 15 51 Stems 1 2 3 4 5 Add leaves 1 93335 2 664233 3 1840 4 9 5 0701 Order leaves 1 33359 2 233466 3 0148 4 9 5 0017 Add a key Key: 4|9 represents a female student who reported having 49 pairs of shoes.

(38)

+

Splitting Stems and Back-to-Back Stemplots

 When data values are “bunched up”, we can get a better picture of

the distribution by splitting stems.

 Two distributions of the same quantitative variable can be

compared using a back-to-back stemplot with common stems.

50 26 26 31 57 19 24 22 23 38 13 50 13 34 23 30 49 13 15 51 0 0 1 1 2 2 3 3 4 4 5 5 Key: 4|9 represents a student who reported having 49 pairs of shoes. Females 14 7 6 5 12 38 8 7 10 10 10 11 4 5 22 7 5 10 35 7 Males 0 4 0 555677778 1 0000124 1 2 2 2 3 3 58 4 4 5 5 Females 333 95 4332 66 410 8 9 100 7 Males “split stems”

(39)

+

From both sides, compare and write about the center , spread ,

shape and possible outliers.

Do CYU : P- 34 .

Check Your Understanding, page 34:

1. In general, it appears that females have more pairs of shoes than

males. The median report for the males was 9 pairs while the female median was 26 pairs. The females also have a larger range of 57-13=44 in comparison to the range of 38-4=34 for the males .

Finally, both males and females have distributions that are skewed to

the right, though the distribution for the males is more heavily skewed as evidenced by the three likely outliers at 22, 35 and 38. The females do not have any likely outliers.

2. b

3. b

(40)

+

1)Divide the range of data into classes of equal width.

2)Find the count (frequency) or percent (relative frequency) of

individuals in each class.

3)Label and scale your axes and draw the histogram. The height of the bar equals its frequency. Adjacent bars should touch, unless a class contains no individuals.

Histograms

 Quantitative variables often take many values. A graph of the

distribution may be clearer if nearby values are grouped together.

 The most common graph of the distribution of one

quantitative variable is a histogram.

(41)

+

Making a Histogram

 The table on page 35 presents data on the percent of

residents from each state who were born outside of the U.S.

Di

spl

ayin

g

Quantitative Data

Example, page 35 Frequency Table Class Count 0 to <5 20 5 to <10 13 10 to <15 9 15 to <20 5 20 to <25 2 25 to <30 1 Total 50

Percent of foreign-born residents

Number

o

(42)

+

1)Don’t confuse histograms and bar graphs.

2)Use percents instead of counts on the vertical axis when comparing distributions with different numbers of

observations.

3)Just because a graph looks nice, it’s not necessarily a meaningful display of data.

( P- 40: Read the example)

Using Histograms Wisely

 Here are several cautions based on common mistakes

students make when using histograms. Cautions

(43)

+

Try Check your understanding: P – 39

Many people………..

(Also put these #s in the calculator.)

Solution:

2. The distribution is roughly symmetric and bell-shaped. The median IQ appears to be between 110 and 120 and the IQ’s vary from 80 to 150. There do not appear to be any outliers.

(44)

+

In this section, we learned that…

 You can use a dotplot, stemplot, or histogram to show the distribution of a quantitative variable.

 When examining any graph, look for an overall pattern and for notable

departures from that pattern. Describe the shape, center, spread, and any outliers. Don’t forget your SOCS!

 Some distributions have simple shapes, such as symmetric or skewed. The number of modes (major peaks) is another aspect of overall shape.

 When comparing distributions, be sure to discuss shape, center, spread, and possible outliers.

 Histograms are for quantitative data, bar graphs are for categorical data. Use relative frequency histograms when comparing data sets of different sizes.

Summary

Section 1.2

(45)

+

(46)

+

Looking Ahead…

We’ll learn how to describe quantitative data with

numbers.

Mean and Standard Deviation

Median and Interquartile Range

Five-number Summary and Boxplots

Identifying Outliers

We’ll also learn how to calculate numerical summaries

with technology and how to choose appropriate

measures of center and spread.

(47)

+

Section 1.3

Describing Quantitative Data with Numbers

After this section, you should be able to…

 MEASURE center with the mean and median

 MEASURE spread with standard deviation and interquartile range

 IDENTIFY outliers

 CONSTRUCT a boxplot using the five-number summary

 CALCULATE numerical summaries with technology

(48)

+

De

scribi

ng

Quantitative Data

Measuring Center: The Mean

 The most common measure of center is the ordinary

arithmetic average, or mean.

Definition:

To find the mean (pronounced “x-bar”) of a set of observations, add

their values and divide by the number of observations. If the n

observations are x1, x2, x3, …, xn, their mean is:



x



x

sum of observations

n

x

1

x

2

...

x

n

n

In mathematics, the capital Greek letter Σis short for “add them all up.” Therefore, the formula for the mean can be written in more compact notation:



x

x

i

(49)

+

De

scribi

ng

Quantitative Data

Measuring Center: The Median

 Another common measure of center is the median. In

section 1.2, we learned that the median describes the midpoint of a distribution.

Definition:

The median M is the midpoint of a distribution, the number such that

half of the observations are smaller and the other half are larger. To find the median of a distribution:

1)Arrange all observations from smallest to largest.

2)If the number of observations n is odd, the median M is the center observation in the ordered list.

3)If the number of observations n is even, the median M is the average of the two center observations in the ordered list.

(50)

+

De

scribi

ng

Quantitative Data

Measuring Center

 Use the data below to calculate the mean and median of the

commuting times (in minutes) of 20 randomly selected New York workers. Example, page 53 10 30 5 25 40 20 10 15 30 20 15 20 85 15 65 15 60 60 40 45



x

10

30

5

25

...

40

45

20

31.25 minutes

0 5 1 005555 2 0005 3 00 4 005 5 6 005 7 8 5 Key: 4|5 represents a New York worker who reported a 45-minute travel time to work.



M

20

25

2

22.5 minutes

(51)

+

Comparing the Mean and the Median

 The mean and median measure center in different ways, and

both are useful.

Don’t confuse the “average” value of a variable (the mean) with its

“typical” value, which we might describe by the median.

The mean and median of a roughly symmetric distribution are close together.

If the distribution is exactly symmetric, the mean and median are exactly the same.

In a skewed distribution, the mean is usually farther out in the long tail than is the median.

Comparing the Mean and the Median

De

scribi

ng

(52)

+

De

scribi

ng

Quantitative Data

Measuring Spread: The Interquartile Range (

IQR

)

 A measure of center alone can be misleading.

 A useful numerical description of a distribution requires both a

measure of center and a measure of spread.

To calculate the quartiles:

1)Arrange the observations in increasing order and locate the

median M.

2)The first quartile Q1 is the median of the observations

located to the left of the median in the ordered list.

3)The third quartile Q3 is the median of the observations

located to the right of the median in the ordered list. The interquartile range (IQR) is defined as:

IQR = Q

3

– Q

1

(53)
(54)

+

5 10 10 15 15 15 15 20 20 20 25 30 30 40 40 45 60 60 65 85

De

scribi

ng

Quantitative Data

Find and Interpret the IQR

Example, page 57

10 30 5 25 40 20 10 15 30 20 15 20 85 15 65 15 60 60 40 45

Travel times to work for 20 randomly selected New Yorkers

5 10 10 15 15 15 15 20 20 20 25 30 30 40 40 45 60 60 65 85 M = 22.5 Q3= 42.5 Q1 = 15

IQR

=

Q

3

– Q

1

=

42.5

15

= 27.5 minutes

Interpretation: The range of the middle half of travel times for the New Yorkers in the sample is 27.5 minutes.

(55)

+

De

scribi

ng

Quantitative Data

Identifying Outliers

 In addition to serving as a measure of spread, the

interquartile range (IQR) is used as part of a rule of thumb for identifying outliers.

Definition:

The 1.5 x IQR Rule for Outliers

Call an observation an outlier if it falls more than 1.5 x IQR above the third quartile or below the first quartile.

Example, page 57

In the New York travel time data, we found Q1=15 minutes, Q3=42.5 minutes, and IQR=27.5 minutes. For these data, 1.5 x IQR = 1.5(27.5) = 41.25

Q1 - 1.5 x IQR = 15 – 41.25 = -26.25

Q3+ 1.5 x IQR = 42.5 + 41.25 = 83.75

Any travel time shorter than -26.25 minutes or longer than 83.75 minutes is considered an outlier.

0 5 1 005555 2 0005 3 00 4 005 5 6 005 7 8 5

(56)

+

The Five-Number Summary

 The minimum and maximum values alone tell us little about

the distribution as a whole. Likewise, the median and quartiles tell us little about the tails of a distribution.

 To get a quick summary of both center and spread, combine

all five numbers.

De

scr

ib

ing Quantit

ati

ve

Data

Definition:

The five-number summary of a distribution consists of the smallest observation, the first quartile, the median, the third quartile, and the largest observation, written in order from smallest to largest.

Minimum Q1 M Q3 Maximum

(57)

+

Calculator:

Command to get 5 number summary:

Put all #s in the list.

Stat -> Edit-> List L1-> Put the numbers

Now, for the command for 5 number summary,

Stat -> Calc -> choose 1:1-VarStats L1

(58)

+

Boxplots (Box-and-Whisker Plots)

 The five-number summary divides the distribution roughly into

quarters. This leads to a new way to display quantitative data, the boxplot.

•Draw and label a number line that includes the

range of the distribution.

•Draw a central box from

Q

1

to

Q

3

.

•Note the median

M

inside the box.

•Extend lines (whiskers) from the box out to the

minimum and maximum values that are not outliers.

How to Make a Boxplot

De

scribi

ng

(59)

+

De

scribi

ng

Quantitative Data

Construct a Boxplot

 Consider our NY travel times data. Construct a boxplot.

Example M = 22.5 Q3= 42.5 Q1 = 15 Min=5 10 30 5 25 40 20 10 15 30 20 15 20 85 15 65 15 60 60 40 45 5 10 10 15 15 15 15 20 20 20 25 30 30 40 40 45 60 60 65 85 Max=85 Recall, this is an outlier by the 1.5 x IQR rule

(60)

+

De

scribi

ng

Quantitative Data

Measuring Spread: The Standard Deviation

 The most common measure of spread looks at how far each

observation is from the mean. This measure is called the

standard deviation. Let’s explore it!

 Consider the following data on the number of pets owned by

a group of 9 children.

1) Calculate the mean.

2) Calculate each deviation.

deviation = observation – mean

= 5



x

deviation: 1 - 5 = -4

(61)

+

De

scribi

ng

Quantitative Data

Measuring Spread: The Standard Deviation

xi (xi-mean) (xi-mean)2 1 1 - 5 = -4 (-4)2 = 16 3 3 - 5 = -2 (-2)2 = 4 4 4 - 5 = -1 (-1)2 = 1 4 4 - 5 = -1 (-1)2 = 1 4 4 - 5 = -1 (-1)2 = 1 5 5 - 5 = 0 (0)2 = 0 7 7 - 5 = 2 (2)2 = 4 8 8 - 5 = 3 (3)2 = 9 9 9 - 5 = 4 (4)2 = 16 Sum=? Sum=?

3) Square each deviation. 4) Find the “average” squared

deviation. Calculate the sum of the squared deviations divided by (n-1)…this is called the

variance.

5) Calculate the square root of the variance…this is the standard deviation.

“average” squared deviation = 52/(9-1) = 6.5 This is the variance.

Standard deviation = square root of variance =





(62)

+

De

scribi

ng

Quantitative Data

Measuring Spread: The Standard Deviation

Definition:

The standard deviation sx measures the average distance of the

observations from their mean. It is calculated by finding an average of the squared distances and then taking the square root. This average squared distance is called the variance.

 variance = sx2 (x1  x ) 2 (x 2  x ) 2 ... (x nx ) 2 n 1  1 n 1 (xix ) 2



standard deviation =

s

x

1

n

1

(

x

i

x

)

2

(63)

+

Choosing Measures of Center and Spread

 We now have a choice between two descriptions for center

and spread

 Mean and Standard Deviation

 Median and Interquartile Range

The median and IQR are usually better than the mean and standard deviation for describing a skewed distribution or a distribution with outliers.

•Use mean and standard deviation only for reasonably symmetric distributions that don’t have outliers.

NOTE: Numerical summaries do not fully describe the shape of a distribution. ALWAYS PLOT YOUR DATA!

Choosing Measures of Center and Spread

De

scribi

ng

(64)

+

In this section, we learned that…

 A numerical summary of a distribution should report at least its

center and spread.

 The mean and median describe the center of a distribution in different ways. The mean is the average and the median is the midpoint of the values.

 When you use the median to indicate the center of a distribution,

describe its spread using the quartiles.

 The interquartile range (IQR) is the range of the middle 50% of the observations: IQR = Q3 – Q1.

Summary

Section 1.3

(65)

+

In this section, we learned that…

 An extreme observation is an outlier if it is smaller than

Q1–(1.5xIQR) or larger than Q3+(1.5xIQR) .

 The five-number summary (min, Q1, M, Q3, max) provides a

quick overall description of distribution and can be pictured using a

boxplot.

 The variance and its square root, the standard deviation are common measures of spread about the mean as center.

 The mean and standard deviation are good descriptions for

symmetric distributions without outliers. The median and IQR are a

better description for skewed distributions.

Summary

Section 1.3

(66)

+

Looking Ahead…

We’ll learn how to model distributions of data…

Describing Location in a Distribution

Normal Distributions

In the next Chapter…

(67)

+

 92 (a) The median is the average of the ranked scores in the middle two positions (the 15th and 16th ranked scores). The median is 87.75.

 Half of the students scored less than 87.75 and half scored more than 87.75.

Q1 is the score one-quarter up the list of ordered scores, 82.

Q3is the score three-quarters up the ordered list of scores, 93.

 IQR = 93-82 =11

 The middle 50% of the scores have a range of 11 points. Any observation above Q3 +1.5 IQR = 93 + 1.5(11) = 109.5 or below Q1-1.5 IQR = 82 -1.5 (11) =

(68)

Figure

Updating...

References

Updating...

Related subjects :