Chap 04 - Describing Data_Displaying and Exploring Data.pdf

Describing Data:
Displaying and
Exploring Data
Chapter 04

◼ Chapter 02 ⇒ descriptive statistics ⇒ transform raw or
ungrouped data into a meaningful form.
 Organization of data into a frequency distribution.
 Presentation of frequency distribution in graphic form as a
histogram or a frequency polygon.
 Visualization of where data tends to cluster, the largest
and the smallest values, and general shape of the data.
◼ Chapter 03 ⇒ computed several measures of location,
such as mean and median.
 Reporting of a typical value in the set of observations.
 Computation of several measures of dispersion, such as
range and standard deviation.
 Describing variation or spread in a set of observations.
Review of Descriptive Statistics
4-2

◼ Dot plot
◼ Stem-and-leaf display
◼ Measures of position
◼ Box plot
◼ Coefficient of skewness
◼ Scatterplot
◼ Contingency table
Content
4-3
◼ Providing additional insight into where the values are
concentrated as well as the general shape of the data.
◼ Consideration of bivariate data:
 Two variables for each individual or observation selected.
 Examples:
◼ Number of studying hours and the points earned on an
examination.
◼ Whether a sampled product is acceptable or not and the
shift on which it is manufactured.

Disadvantages of
Frequency Distribution
4-4

Disadvantages of Frequency
Distribution
◼ Two disadvantages to organizing the data
into a frequency distribution:
❑The exact identity of each value is lost.
❑Difficult to tell how the values within each
class are distributed.
4-5

Dot Plots
◼ A dot plot groups the data as little as
possible and the identity of an individual
observation is not lost.
◼ To develop a dot plot, each observation is
simply displayed as a dot along a horizontal
number line indicating the possible values of
the data.
◼ If there are identical observations or the
observations are too close to be shown
individually, the dots are “piled” on top of each
other.
4-6

Stem-and-Leaf
◼ Stem-and-leaf display is a statistical technique
to present a set of data.
Each numerical value is divided into two
parts.
The leading digit(s) becomes the stem and
the trailing digit the leaf.
The stems are located along the vertical
axis, and the leaf values are stacked
against each other along the horizontal
axis.
4-8

Stem-and-Leaf
EXAMPLE
Listed in Table 4–1 is the number of 30-second radio
advertising spots purchased by each of the 45 members
of the Greater Buffalo Automobile Dealers Association last
year. Organize the data into a stem-and-leaf display.
Around what values do the number of advertising spots
tend to cluster? What is the fewest number of spots
purchased by a dealer? The largest number purchased?
4-9

Stem-and-Leaf
EXAMPLE
Listed in Table 4–1 is the number of 30-second radio advertising spots
purchased by each of the 45 members of the Greater Buffalo Automobile
Dealers Association last year. Organize the data into a stem-and-leaf
display. Around what values do the number of advertising spots tend to
cluster? What is the fewest number of spots purchased by a dealer? The
largest number purchased?
4-10

Quartiles and Percentiles
◼ The standard deviation is the most widely used
measure of dispersion.
◼ Alternative ways of describing spread of data
include determining the location of values that
divide a set of observations into equal parts.
◼ These measures include quartiles and
percentiles.
4-11

Percentiles and Quartiles
◼ To formalize the computational procedure, let Lp
refer to the location of a desired percentile. So, if
we wanted to find the 33rd percentile we would use
L33 and if we wanted the median, the 50th
percentile, then L50.
◼ The number of observations is n, so if we want to
locate the median, its position is at (n + 1)/2, or we
could write this as (n + 1)(P/100), where P is the
desired percentile.
4-12

Percentiles - Example
EXAMPLE
Listed below are the commissions earned last
month by a sample of 15 brokers at XYZ
Securities Ltd.
$2,038 $1,758 $1,721 $1,637 $2,097 $2,047
$2,205 $1,787 $2,287 $1,940 $2,311 $2,054
$2,406 $1,471 $1,460
Locate the median, the first quartile, and the
third quartile for the commissions earned.
4-13

EXAMPLE
Listed below are the commissions earned last month by a
sample of 15 brokers at XYZ Securities Ltd.
$2,038 $1,758 $1,721 $1,637 $2,097 $2,047 $2,205 $1,787
$2,287 $1,940 $2,311 $2,054 $2,406 $1,471 $1,460
Locate the median, the first quartile, and the third quartile
for the commissions earned.
Step 1: Organize the data from lowest to largest value.
$1,460 $1,471 $1,637 $1,721 $1,758 $1,787 $1,940 $2,038
$2,047 $2,054 $2,097 $2,205 $2,287 $2,311 $2,406
Step 2: Compute the first and third quartiles.
Locate L25 and L75 using:
4-14

Step 1: Organize the data from lowest to largest value
$1,460 $1,471 $1,637 $1,721 $1,758 $1,787 $1,940 $2,038 $2,047
$2,054 $2,097 $2,205 $2,287 $2,311 $2,406
Step 2: Compute the first and third quartiles. Locate L25 and L75 using:
𝐿25 = (15 + 1)
25
100
= 4 𝐿75 = (15 + 1)
75
100
= 12
Therefore, the first and third quartiles are located at the 4th
and 12th positions, respectively
𝐿25 = $1,721
𝐿75 = $2,205
4-15

Box Plots
◼ A box plot is a graphical display, based on
quartiles, that helps us picture a set of data.
◼ To construct a box plot, we need only five
statistics:
 Minimum value,
 Q1 (the first quartile),
 Median,
 Q3 (the third quartile), and
 Maximum value.
4-17

Boxplot - Example
Step 1: Create an appropriate scale along
horizontal axis.
Step 2: Draw a box that starts at Q1 (15 mins)
and ends at Q3 (22 mins).
Inside the box we place a vertical line to
represent the median (18 mins).
Step 3: Extend horizontal lines from the box out
to minimum value (13 mins) and maximum value
(30 mins).
4-19

Boxplot - Example
Step 1: Create an appropriate scale along horizontal axis.
Step 2: Draw a box that starts at Q1 (15 mins) and ends at Q3 (22
mins).
Inside the box we place a vertical line to represent the median (18
mins).
Step 3: Extend horizontal lines from the box out to minimum value
(13 mins) and maximum value (30 mins).
4-20

Skewness
◼ Another characteristic of a set of data is the shape.
◼ Commonly observed shapes: symmetric,
positively skewed, negatively skewed.
4-21

Skewness
4-22
◼ The coefficient of skewness can range from -3
up to 3.
A value near -3, indicates considerable
negative skewness.
A value such as 1.63 indicates moderate
positive skewness.
A value of 0, which will occur when the mean
and median are equal, indicates the
distribution is symmetrical and that there is
no skewness present.

Kurtosis
4-23
◼ Like skewness, kurtosis is a statistical measure
that is used to describe distribution.
◼ Kurtosis refers to a measure of the degree to
which a given distribution is more or less
‘peaked’, relative to the normal distribution.

Kurtosis
4-25
◼ Leptokurtic
More peaked than the normal distribution.
The higher peak results from clustering of
data points along the X-axis.
The coefficient of kurtosis is usually found to
be more than 3.

Kurtosis
4-26
◼ Platykurtic
 Has extremely dispersed points along the X-axis
resulting to a lower peak when compared to the
normal distribution.
 The distribution’s shape is wide and flat.
 The points are less clustered around the mean.
 The coefficient of kurtosis is usually less than 3.
◼ Mesokurtic
 Has a curve similar to that of the normal distribution.

Scatter Diagram
Describing Relationship between Two Variables:
◼ When we study the relationship between two
variables, we refer to the data as bivariate.
◼ One graphical technique we use to show the
relationship between variables is called a scatter
diagram.
◼ To draw a scatter diagram, we need two variables.
◼ We scale one variable along the horizontal axis (X-
axis) of a graph and the other variable along the
vertical axis (Y-axis).
4-27

Describing Relationship between Two
Variables – Scatter Diagram Examples
4-28

4-29

4-30

Variables – Scatter Diagram Example
In Chapter 2, data from Auto USA was presented. We
gathered information concerning several variables,
including the profit earned from the sale of 180
vehicles sold last month. In addition to the amount
of profit on each sale, one of the other variables is
the age of the purchaser.
Is there a relationship between the profit earned on a
vehicle sale and the age of the purchaser?
Would it be reasonable to conclude that the more
expensive vehicles are purchased by older
buyers?
4-31

4-32

◼ The scatter diagram
shows a rather weak
positive relationship
between the two
variables.
◼ It does not appear
there is much
relationship
between the vehicle
profit and the age of
the buyer.
4-33

Contingency Tables
◼ A scatter diagram requires that both of the
variables be at least interval scale.
◼ What if we wish to study the relationship
between two variables when one or both are
nominal or ordinal scale?
◼ In this case, we tally the results in a
contingency table.
4-34

Contingency Tables
4-35
Examples:
1. Students at a university are classified by
gender and class rank.
2. A product is classified as acceptable or
unacceptable and by the shift (day, afternoon,
or night) on which it is manufactured.

Contingency Tables – An Example
There are four dealerships in the Apple wood Auto
group.
Suppose we want to compare the profit earned on
each vehicle sold by the particular dealership. To
put it another way, is there a relationship between the
amount of profit earned and the dealership? The
table next slide is the cross-tabulation of the raw
data of the two variables.
4-36

There are four dealerships in the Apple wood Auto group.
Suppose we want to compare the profit earned on each vehicle sold
by the particular dealership. To put it another way, is there a
relationship between the amount of profit earned and the dealership?
The table below is the cross-tabulation of the raw data of the two
variables.
4-37

From the contingency table, we observe the following:
1. From the Total column on the right, 90 of the 180 cars
sold had a profit above the median and half below. From
the definition of the median this is expected.
4-38

From the contingency table, we observe the following:
2. For the Kane dealership 25 out of the 52, or 48 percent, of
the cars sold were sold for a profit more than the median.
3. The percent profits above the median for the other
dealerships are 50 percent for Olean, 42 percent for Sheffield,
and 60 percent for Tionesta.
4-39

LO1 Construct and interpret a dot plot.
LO2 Construct and describe a stem-and-leaf display.
LO3 Identify and compute measures of position.
LO4 Construct and analyze a box plot.
LO5 Compute and describe the coefficient of skewness.
LO6 Create and interpret a scatterplot.
LO7 Develop and explain a contingency table.
Learning Objectives
4-40

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