The document discusses methods for organizing and presenting both qualitative and quantitative data, including frequency tables, bar charts, pie charts, and different types of frequency distributions. It provides examples of how to construct a frequency table by determining the number of classes, class intervals, and class limits based on a set of data. It also describes how to create histograms, frequency polygons, and cumulative frequency distributions to graphically display a frequency distribution and highlights key terms such as class frequency, class interval, and relative frequency.
This document discusses methods for organizing and presenting qualitative and quantitative data, including:
1. Organizing qualitative data into frequency tables and presenting them as bar charts or pie charts.
2. Organizing quantitative data into frequency distributions by grouping data into classes and showing the number of observations in each class. Frequency distributions can be presented as histograms, frequency polygons, or cumulative frequency distributions.
3. An example is provided of constructing a frequency distribution table by determining the number of classes, class interval, class limits, and tallying data into classes using vehicle selling prices. Relative frequency distributions are also discussed.
The document discusses organizing and presenting data through descriptive statistics. It covers types of data, constructing frequency distribution tables, calculating relative frequencies and percentages, and using graphical methods like bar graphs, pie charts, histograms and polygons to summarize categorical and quantitative data. Examples are provided to demonstrate how to organize data into frequency distributions and calculate relative frequencies to graph the results.
This document discusses methods for organizing and presenting data through frequency tables, distributions, and graphs. It covers creating frequency tables to organize qualitative and quantitative data. Frequency distributions group quantitative data into classes with class limits, frequencies, and midpoints. These distributions can be presented as histograms, frequency polygons, or cumulative frequency distributions. The document provides an example using data on vehicle selling prices to demonstrate constructing a frequency table and distribution, calculating relative frequencies, and graphing the results as a histogram.
This document discusses methods for organizing and presenting data through frequency tables, distributions, and graphs. It covers:
- Creating frequency tables to organize qualitative and quantitative data by grouping it into categories and counting observations in each.
- Presenting frequency tables visually through bar charts and pie charts.
- Forming frequency distributions by dividing a range of quantitative data into class intervals and counting observations in each.
- Graphically displaying frequency distributions through histograms, which use bars to show class frequencies, and frequency polygons, which connect class midpoints and frequencies with line segments.
The document provides examples and explanations for creating different types of data displays, including stem-and-leaf plots, frequency tables, histograms, and cumulative frequency tables. It includes sample data sets and step-by-step instructions for making each type of display. Key terms defined include stem, leaf, frequency, interval, and cumulative frequency.
This document discusses frequency distribution and methods for presenting grouped data. It defines key terms like class interval, class frequency, and class midpoint. It also provides steps for constructing a frequency distribution, including determining the number of classes and class interval. Examples are given to illustrate a frequency distribution table, relative frequency distribution, and different types of graphs - histograms, frequency polygons, cumulative frequency curves, line graphs, bar charts and pie charts - that can be used to present grouped quantitative data.
The document discusses methods for organizing and presenting both qualitative and quantitative data, including frequency tables, bar charts, pie charts, and different types of frequency distributions. It provides examples of how to construct a frequency table by determining the number of classes, class intervals, and class limits based on a set of data. It also describes how to create histograms, frequency polygons, and cumulative frequency distributions to graphically display a frequency distribution and highlights key terms such as class frequency, class interval, and relative frequency.
This document discusses methods for organizing and presenting qualitative and quantitative data, including:
1. Organizing qualitative data into frequency tables and presenting them as bar charts or pie charts.
2. Organizing quantitative data into frequency distributions by grouping data into classes and showing the number of observations in each class. Frequency distributions can be presented as histograms, frequency polygons, or cumulative frequency distributions.
3. An example is provided of constructing a frequency distribution table by determining the number of classes, class interval, class limits, and tallying data into classes using vehicle selling prices. Relative frequency distributions are also discussed.
The document discusses organizing and presenting data through descriptive statistics. It covers types of data, constructing frequency distribution tables, calculating relative frequencies and percentages, and using graphical methods like bar graphs, pie charts, histograms and polygons to summarize categorical and quantitative data. Examples are provided to demonstrate how to organize data into frequency distributions and calculate relative frequencies to graph the results.
This document discusses methods for organizing and presenting data through frequency tables, distributions, and graphs. It covers creating frequency tables to organize qualitative and quantitative data. Frequency distributions group quantitative data into classes with class limits, frequencies, and midpoints. These distributions can be presented as histograms, frequency polygons, or cumulative frequency distributions. The document provides an example using data on vehicle selling prices to demonstrate constructing a frequency table and distribution, calculating relative frequencies, and graphing the results as a histogram.
This document discusses methods for organizing and presenting data through frequency tables, distributions, and graphs. It covers:
- Creating frequency tables to organize qualitative and quantitative data by grouping it into categories and counting observations in each.
- Presenting frequency tables visually through bar charts and pie charts.
- Forming frequency distributions by dividing a range of quantitative data into class intervals and counting observations in each.
- Graphically displaying frequency distributions through histograms, which use bars to show class frequencies, and frequency polygons, which connect class midpoints and frequencies with line segments.
The document provides examples and explanations for creating different types of data displays, including stem-and-leaf plots, frequency tables, histograms, and cumulative frequency tables. It includes sample data sets and step-by-step instructions for making each type of display. Key terms defined include stem, leaf, frequency, interval, and cumulative frequency.
This document discusses frequency distribution and methods for presenting grouped data. It defines key terms like class interval, class frequency, and class midpoint. It also provides steps for constructing a frequency distribution, including determining the number of classes and class interval. Examples are given to illustrate a frequency distribution table, relative frequency distribution, and different types of graphs - histograms, frequency polygons, cumulative frequency curves, line graphs, bar charts and pie charts - that can be used to present grouped quantitative data.
This document discusses methods for organizing and presenting qualitative and quantitative data using frequency tables, charts, and graphs. It covers:
1. Creating frequency tables to organize qualitative and quantitative data, and presenting qualitative data as bar charts or pie charts.
2. Constructing frequency distributions to organize quantitative data into class intervals and determining class frequencies, and presenting quantitative data using histograms, frequency polygons, and cumulative frequency polygons.
3. An example of creating a frequency table and histogram based on sales price data from 80 vehicles to compare typical selling prices on dealer lots.
Frequency Tables, Frequency Distributions, and Graphic PresentationConflagratioNal Jahid
This document provides an overview of key concepts for describing data through frequency tables, distributions, and graphs. It defines important terms like frequency table, distribution, class, interval and discusses how to organize both qualitative and quantitative data. Guidelines for data collection are provided. Examples are given to demonstrate how to construct frequency tables and distributions and convert them to relative frequencies. Finally, different types of graphs for presenting frequency distributions are described, including histograms, polygons and cumulative distributions.
This document discusses different types of graphs and distributions that can be used to organize and represent data. It explains frequency distributions, histograms, frequency polygons, ogives, relative frequency graphs, Pareto charts, time series graphs, pie charts, and stem-and-leaf plots. Rules for constructing frequency distributions and examples of each type of graph are provided.
This document discusses different types of graphs and distributions that can be used to organize and represent data. It covers frequency distributions, histograms, frequency polygons, ogives, relative frequency graphs, Pareto charts, time series graphs, pie charts, and stem-and-leaf plots. Rules for constructing frequency distributions are provided, such as having between 5-20 classes and equal class widths. Examples are given to illustrate each type of graph or distribution.
This chapter discusses descriptive statistics including organizing and graphing qualitative and quantitative data, measures of central tendency, and measures of dispersion. It covers frequency distributions, histograms, polygons, measures of central tendency (mean, median, mode), measures of dispersion (range, variance, standard deviation), skewness, and cumulative frequency distributions. The objectives are to describe and interpret graphical displays of data, compute various statistical measures, and identify shapes of distributions.
This document discusses frequency distributions and methods for graphically presenting frequency distribution data. It defines a frequency distribution as a tabulation or grouping of data into categories showing the number of observations in each group. The document outlines the parts of a frequency table as class limits, class size, class boundaries, and class marks. It then provides steps for constructing a frequency distribution table from a set of data. Finally, it discusses histograms and frequency polygons as methods for graphically presenting frequency distribution data, and provides examples of how to construct these graphs in Excel.
This document discusses frequency distributions and how to construct them from raw data. It provides examples of creating stem-and-leaf displays, frequency tables, relative frequency tables, and cumulative frequency tables from various data sets. Key concepts covered include class width, class boundaries, tallying data, and calculating relative frequencies and percentages. Overall, the document serves as a tutorial on how to organize and summarize data using various types of frequency distributions.
This document discusses methods for organizing and presenting data through frequency tables, distributions, and graphs. It covers:
- Creating frequency tables to organize qualitative and quantitative data
- Presenting frequency tables as bar charts or pie charts
- Organizing quantitative data into a frequency distribution by grouping it into classes and counting observations in each class
- Graphically presenting frequency distributions as histograms, frequency polygons, or cumulative frequency distributions
This document provides an overview of descriptive statistics methods for summarizing and presenting qualitative and quantitative data through tables, graphs, and distributions. It discusses frequency distributions, bar charts, pie charts, histograms, and crosstabulations to summarize one or two variables. Frequency distributions organize raw data into classes to find patterns. Bar charts, pie charts, and histograms visually display frequency distributions. Crosstabulations summarize relationships between two variables.
Probability and statistics (frequency distributions)Don Bosco BSIT
- Frequency distributions organize and summarize data by grouping it into classes and counting the frequency of observations in each class. They can be presented in tables or graphically.
- Common graphical representations include histograms, relative frequency histograms, frequency polygons, and ogives.
- Categorical frequency distributions are used for nominal or ordinal data by tallying the frequency of observations in each category.
This chapter discusses how to organize and present both qualitative and quantitative data using frequency tables, bar charts, pie charts, histograms, frequency polygons, and cumulative frequency distributions. It provides examples of how to construct frequency tables by determining the number of classes, class width, and class limits. It also explains how to convert frequency distributions to relative frequency distributions and how to represent the distributions graphically.
This document provides instructions and examples for creating stem-and-leaf plots, frequency tables, histograms, and cumulative frequency tables from data sets. It includes step-by-step explanations and examples of how to organize and summarize data using these graphical representations. Key terms like stem, leaf, frequency, interval, and cumulative frequency are also defined. Quiz problems at the end ask the reader to apply the methods by creating a stem-and-leaf plot, frequency table, and histogram from sample data sets.
Graphs, charts, and tables ppt @ bec domsBabasab Patil
This document discusses various methods for organizing and presenting quantitative data, including frequency distributions, histograms, stem-and-leaf diagrams, pie charts, bar charts, line charts, scatter plots, and strategies for grouping continuous data into classes. Key topics covered include constructing frequency distributions, interpreting relative frequencies, guidelines for determining class widths and intervals, and using graphs and charts to visualize categorical and multivariate data.
1. The document discusses various methods for summarizing categorical and quantitative data through tables and graphs, including frequency distributions, relative frequency distributions, bar charts, pie charts, dot plots, histograms, and ogives.
2. An example using data on customer ratings from a hotel illustrates frequency distributions and pie charts.
3. Another example using costs of auto parts demonstrates frequency distributions, histograms, and ogives.
Chapter 2: Frequency Distribution and GraphsMong Mara
This document discusses different types of graphs and charts that can be used to represent frequency distributions of data, including histograms, frequency polygons, ogives, bar charts, pie charts, and stem-and-leaf plots. It provides examples of how to construct each graph or chart using sample data sets and discusses key aspects of each type such as class intervals, relative frequencies, and ordering of data. Guidelines are given for determining the optimal number of classes and class widths for grouped data. Exercises at the end provide practice applying these techniques to additional data sets.
Descriptive statistics can summarize and graphically present data. Tabular presentations display data in a grid, with tables showing frequencies of categories. Graphical presentations include bar graphs to show frequencies, pie charts to show proportions, and line graphs to show trends over time. Frequency distributions organize raw data into meaningful patterns for analysis by specifying class intervals and calculating frequencies and cumulative frequencies.
This slideshow describes about type of data, its tabular and graphical representation by various ways. It is slideshow is useful for bio statisticians and students.
Data organization and presentation (statistics for research)Harve Abella
The document discusses various methods of presenting data, including textual, tabular, and graphical displays. It provides examples and definitions of key terms used in data presentation, such as frequency distribution tables, class intervals, class boundaries, class marks, and cumulative frequencies. The document also outlines steps for constructing a frequency distribution table, including determining the number of classes, range, class size, and class limits.
This document discusses methods for organizing and presenting qualitative and quantitative data using frequency tables, charts, and graphs. It covers:
1. Creating frequency tables to organize qualitative and quantitative data, and presenting qualitative data as bar charts or pie charts.
2. Constructing frequency distributions to organize quantitative data into class intervals and determining class frequencies, and presenting quantitative data using histograms, frequency polygons, and cumulative frequency polygons.
3. An example of creating a frequency table and histogram based on sales price data from 80 vehicles to compare typical selling prices on dealer lots.
Frequency Tables, Frequency Distributions, and Graphic PresentationConflagratioNal Jahid
This document provides an overview of key concepts for describing data through frequency tables, distributions, and graphs. It defines important terms like frequency table, distribution, class, interval and discusses how to organize both qualitative and quantitative data. Guidelines for data collection are provided. Examples are given to demonstrate how to construct frequency tables and distributions and convert them to relative frequencies. Finally, different types of graphs for presenting frequency distributions are described, including histograms, polygons and cumulative distributions.
This document discusses different types of graphs and distributions that can be used to organize and represent data. It explains frequency distributions, histograms, frequency polygons, ogives, relative frequency graphs, Pareto charts, time series graphs, pie charts, and stem-and-leaf plots. Rules for constructing frequency distributions and examples of each type of graph are provided.
This document discusses different types of graphs and distributions that can be used to organize and represent data. It covers frequency distributions, histograms, frequency polygons, ogives, relative frequency graphs, Pareto charts, time series graphs, pie charts, and stem-and-leaf plots. Rules for constructing frequency distributions are provided, such as having between 5-20 classes and equal class widths. Examples are given to illustrate each type of graph or distribution.
This chapter discusses descriptive statistics including organizing and graphing qualitative and quantitative data, measures of central tendency, and measures of dispersion. It covers frequency distributions, histograms, polygons, measures of central tendency (mean, median, mode), measures of dispersion (range, variance, standard deviation), skewness, and cumulative frequency distributions. The objectives are to describe and interpret graphical displays of data, compute various statistical measures, and identify shapes of distributions.
This document discusses frequency distributions and methods for graphically presenting frequency distribution data. It defines a frequency distribution as a tabulation or grouping of data into categories showing the number of observations in each group. The document outlines the parts of a frequency table as class limits, class size, class boundaries, and class marks. It then provides steps for constructing a frequency distribution table from a set of data. Finally, it discusses histograms and frequency polygons as methods for graphically presenting frequency distribution data, and provides examples of how to construct these graphs in Excel.
This document discusses frequency distributions and how to construct them from raw data. It provides examples of creating stem-and-leaf displays, frequency tables, relative frequency tables, and cumulative frequency tables from various data sets. Key concepts covered include class width, class boundaries, tallying data, and calculating relative frequencies and percentages. Overall, the document serves as a tutorial on how to organize and summarize data using various types of frequency distributions.
This document discusses methods for organizing and presenting data through frequency tables, distributions, and graphs. It covers:
- Creating frequency tables to organize qualitative and quantitative data
- Presenting frequency tables as bar charts or pie charts
- Organizing quantitative data into a frequency distribution by grouping it into classes and counting observations in each class
- Graphically presenting frequency distributions as histograms, frequency polygons, or cumulative frequency distributions
This document provides an overview of descriptive statistics methods for summarizing and presenting qualitative and quantitative data through tables, graphs, and distributions. It discusses frequency distributions, bar charts, pie charts, histograms, and crosstabulations to summarize one or two variables. Frequency distributions organize raw data into classes to find patterns. Bar charts, pie charts, and histograms visually display frequency distributions. Crosstabulations summarize relationships between two variables.
Probability and statistics (frequency distributions)Don Bosco BSIT
- Frequency distributions organize and summarize data by grouping it into classes and counting the frequency of observations in each class. They can be presented in tables or graphically.
- Common graphical representations include histograms, relative frequency histograms, frequency polygons, and ogives.
- Categorical frequency distributions are used for nominal or ordinal data by tallying the frequency of observations in each category.
This chapter discusses how to organize and present both qualitative and quantitative data using frequency tables, bar charts, pie charts, histograms, frequency polygons, and cumulative frequency distributions. It provides examples of how to construct frequency tables by determining the number of classes, class width, and class limits. It also explains how to convert frequency distributions to relative frequency distributions and how to represent the distributions graphically.
This document provides instructions and examples for creating stem-and-leaf plots, frequency tables, histograms, and cumulative frequency tables from data sets. It includes step-by-step explanations and examples of how to organize and summarize data using these graphical representations. Key terms like stem, leaf, frequency, interval, and cumulative frequency are also defined. Quiz problems at the end ask the reader to apply the methods by creating a stem-and-leaf plot, frequency table, and histogram from sample data sets.
Graphs, charts, and tables ppt @ bec domsBabasab Patil
This document discusses various methods for organizing and presenting quantitative data, including frequency distributions, histograms, stem-and-leaf diagrams, pie charts, bar charts, line charts, scatter plots, and strategies for grouping continuous data into classes. Key topics covered include constructing frequency distributions, interpreting relative frequencies, guidelines for determining class widths and intervals, and using graphs and charts to visualize categorical and multivariate data.
1. The document discusses various methods for summarizing categorical and quantitative data through tables and graphs, including frequency distributions, relative frequency distributions, bar charts, pie charts, dot plots, histograms, and ogives.
2. An example using data on customer ratings from a hotel illustrates frequency distributions and pie charts.
3. Another example using costs of auto parts demonstrates frequency distributions, histograms, and ogives.
Chapter 2: Frequency Distribution and GraphsMong Mara
This document discusses different types of graphs and charts that can be used to represent frequency distributions of data, including histograms, frequency polygons, ogives, bar charts, pie charts, and stem-and-leaf plots. It provides examples of how to construct each graph or chart using sample data sets and discusses key aspects of each type such as class intervals, relative frequencies, and ordering of data. Guidelines are given for determining the optimal number of classes and class widths for grouped data. Exercises at the end provide practice applying these techniques to additional data sets.
Descriptive statistics can summarize and graphically present data. Tabular presentations display data in a grid, with tables showing frequencies of categories. Graphical presentations include bar graphs to show frequencies, pie charts to show proportions, and line graphs to show trends over time. Frequency distributions organize raw data into meaningful patterns for analysis by specifying class intervals and calculating frequencies and cumulative frequencies.
This slideshow describes about type of data, its tabular and graphical representation by various ways. It is slideshow is useful for bio statisticians and students.
Data organization and presentation (statistics for research)Harve Abella
The document discusses various methods of presenting data, including textual, tabular, and graphical displays. It provides examples and definitions of key terms used in data presentation, such as frequency distribution tables, class intervals, class boundaries, class marks, and cumulative frequencies. The document also outlines steps for constructing a frequency distribution table, including determining the number of classes, range, class size, and class limits.
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DATA ANALYSIS FOR BUSINESS ch02-Discriptive Statistics_Tabular and Graphical Methods.ppt
1. 1
1
Chapter 2
Descriptive Statistics: Tabular and
Graphical Methods
Graphically Summarizing Qualitative Data
Graphically Summarizing Quantitative Data
Stem-and-leaf Display
Misleading Graphs and Charts
2. 2
2.1 Graphically Summarizing Qualitative Data
With qualitative data, names identify the different
categories
This data can be summarized using a frequency
distribution
Frequency distribution: A table that summarizes
the number (or frequency) of items in each of
several non-overlapping classes.
3. 2-3
Describing Pizza Preferences
A business entrepreneur plans to open a pizza restaurant
in a college town, and wishes to study the pizza
preferences of the college students.
Table 2.1 lists pizza preferences of 50 college students
Table 2.1 does not reveal much useful information
Table 2.1
Example 2.1
4. 4
A frequency distribution is a
useful summary
The frequency distribution
shows us how the
preferences are distributed
among the six restaurants.
Papa’s John’s is the most popular restaurant.
Papa’s John’s is roughly twice as popular of the next three
runners up – Bruno’s, Little Caesars, and Will’s.
Pizza Hut and Domino’s are the least
preferred restaurants
Table 2.2
5. 5
Relative Frequency and Percent Frequency
Relative frequency summarizes the proportion (or fraction)
of items in each class
If the data set consists of n observations,
Multiply times 100 to obtain the percent frequency.
Table 2.3
6. 2-6
Bar Charts and Pie Charts
Bar chart: A vertical or horizontal rectangle
represents the frequency for each category
Height can be frequency, relative frequency, or
percent frequency
Pie chart: A circle divided into slices where
the size of each slice represents its relative
frequency or percent frequency
7. 2-7
Excel Bar and Pie Chart of the Pizza
Preference Data
Figures 2.1 and 2.2
8. 8
Exercise 2.1
Jeep Model
Frequency Relative
Frequency
Percent
Frequency
Commander 71 0.2829 28.29%
Grand Cherokee 70 0.2789 27.89%
Liberty 80 0.3187 31.78%
Wrangler 30 0.1195 11.95%
251 1.0000 100.00%
Table 2.4
Table 2.4 is the frequency distribution of
vehicles sold in 2006 by the Greater
Cincinnati Jeep dealers.
Please find the relative frequency and
percent frequency.
10. 2-10
2.2 Graphically Summarizing Quantitative
Data
Often need to summarize and describe the shape of
the distribution of a population or sample of
measurements.
Summarize quantitative data by using
frequency distribution:
a list of data classes with the count or “frequency” of values
that belong to each class
“Classify and count”
The frequency distribution is a table
histogram:
a picture of the frequency distribution
11. 11
11
Constructing the frequency distribution
Steps in making a frequency distribution:
1. Determine the number of classes K
2. Determine the class length
3. Form non-overlapping classes of equal width
4. Tally and count the number of measurements in
each class
5. Graph the histogram
12. 12
12
Example 2.2
The Payment Time Case: Reducing
Payment Times
In order to assess the effectiveness of the system, the
consulting firm will study the payment times for invoices
processed during the first three months of the system’s
operation.
During this period, 7,823 invoices are processed using
the new system. To study the payment times of these
invoices, the consulting firm numbers the invoices from
0001 to 7823 and uses random numbers to select a
random sample of 65 invoices. The resulting 65 payment
times are given in Table 2.5
14. 14
14
Group all of the n data into K number of classes
K is the smallest whole number for which
2K n
In Examples 2.2 , n = 65
For K = 6, 26 = 64, < n
For K = 7, 27 = 128, > n
So use K = 7 classes
Step1: The number of classes K
15. 15
15
Class length L is the step size from one to the next
In Examples 2.2, The Payment Time Case, the largest
value is 29 days and the smallest value is 10 days, so
Arbitrarily round the class length up to 3 days/class
K
L
value
smallest
-
value
Largest
days/class
7143
2
classes
7
days
19
classes
7
days
10
-
29
.
L
Step2: Class Length L
16. 16
The classes start on the smallest data value. This is the lower
boundary of the first class. The upper boundary of the first
class is smallest value +L.
• In the example 2.2, the lower boundary of the first class is 10, the
upper boundary of the first class is 10+3=13. So the first class -10
days and less than 13 days (10≤n<13)- includes 10,11,and 12 days.
The lower boundary of the second class is the upper boundary of
the first class. The upper boundary of the second class is adding
L to this lower boundary.
In the example 2.2, the second class-13 days and less than 16 days
(13≤n<16)- -includes 13,14, and 15 days.
And so on
Step 3: Form non-overlapping class of equal width
(Define the boundaries of classes)
17. 17
17
Classes (days) Tally Frequency
10 < 13 ||| 3
13 < 16 |||| 14
16 < 19 ||| 23
19 < 22 || 12
22 < 25 ||| 8
25 < 28 |||| 4
28 < 31 | 1
65
||||
||||
||||
|||| ||||
||||
||||
||||
||||
Check: All frequencies must sum to n
Step 4: Tallies and Frequencies
Table 2.6
18. 18
Step 5: Graph the histogram
Show the frequency distribution in a histogram
Figure 2.5
19. 19
A graph in which rectangles represent the
classes
The base of the rectangle represents the class
length
The height of the rectangle represents
the frequency in a frequency histogram, or
the relative frequency in a relative frequency
histogram
Histogram
20. 20
The relative frequency of a class is the proportion or
fraction of data that is contained in that class
Calculated by dividing the class frequency by the total
number of data values
For example:
Relative frequency may be expressed as either a
decimal or percent (percent frequency distribution)
A relative frequency distribution is a list of all the data
classes and their associated relative frequencies
Relative Frequency, Percent Frequency
Classes (days) Frequency Relative Frequency Percent Frequency
10 < 13 3 3/65 = 0.0462 4.62%
13 < 15 14 14/65 = 0.2154 21.54
… … …
21. 21
21
Classes (days) Frequency Relative Frequency
10 < 13 3 3/65 = 0.0462
13 < 16 14 14/65 = 0.2154
16 < 19 23 0.3538
19 < 22 12 0.1846
22 < 25 8 0.1231
25 < 28 4 0.0615
28 < 31 1 0.0154
65 1.0000
Check: All relative frequencies must sum to 1
Relative Frequency: Example 2.2
Table 2.7
22. 22
22
Relative Frequency Histogram
Example 2.2: The Payment Times Case
Figure 2.6
The tail on the right appears to be longer than the tail on
the left. We say: the distribution is skewed to the right.
23. 23
Remarks
The procedure introduced is not the only way to
construct a histogram.
e.g. it is not necessary to
set the lower boundary of
the 1st class equal to the
smallest measurement.
Sometimes it is desirable to let the nature of the
problem determine the histogram classes.
e.g. 10-year lengths for ages of the residents in a city
Sometimes histogram with unequal class
lengths is better. e.g. open-ended classes
Figure 2.7
25. 25
25
Skewness(偏度)
Skewed distributions are not symmetrical about their
center. Rather, they are lop-sided with a longer tail on
one side or the other.
• A population is distributed according to its relative
frequency curve
• The skew is the side with the longer tail
Right Skewed
Left Skewed Symmetric
Figure 2.9
26. 26
Frequency Polygons
Plot a point above each class midpoint at a height
equal to the frequency of the class
Useful when comparing two or more distributions
Table 2.8
Example 2.3 Comparing Two Grade Distribution
32 63 69 85 91
45 64 69 86 92
50 64 72 87 92
56 65 76 87 93
58 66 78 88 93
60 67 81 89 94
61 67 83 90 96
61 68 83 90 98
Scores for Statistics Exam 1
(in increasing order)
Classes Frequency Percent
Frequency
28. 2-28
Cumulative Distributions
Another way to summarize a distribution is to
construct a cumulative distribution
To do this, use the same number of classes, class
lengths, and class boundaries used for the
frequency distribution
Rather than a count, we record the number of
measurements that are less than the upper
boundary of that class
In other words, a running total
30. 2-30
Ogive
Ogive: A graph of a cumulative distribution
Plot a point above each upper class boundary at
height of cumulative frequency
Connect points with line segments
Can also be drawn using:
Cumulative relative frequencies
Cumulative percent frequencies
Figure 2.14
31. 2-31
2.3 Stem-and-Leaf Displays
Purpose is to see the overall pattern of the
data, by grouping the data into classes
the variation from class to class
the amount of data in each class
the distribution of the data within each class
Best for small to moderately sized data
distributions
33. 33
33
The stem-and-leaf display of car mileages:
29 8
30 13455677888
31 0012334444455667778899
32 011123344557788
33 03
29 + 0.8 = 29.8
33 + 0.0 = 33.0
33 + 0.3 = 33.3
Figure 2.15
Stem unit =1, Leaf unit =0.1
34. 34
34
Splitting The Stems
There are no rules that dictate the number of stem
values, so we can split the stems as needed
Starred classes (*) extend from 0.0 to 0.4
Unstarred classes extend from 0.5 to 0.9
29 8
30 * 1 3 4
30 5 5 6 7 7 8 8 8
31 * 0 0 1 2 3 3 4 4 4 4 4
31 5 5 6 6 7 7 7 8 8 9 9
32 * 0 1 1 1 2 3 3 4 4
32 5 5 7 7 8
33 * 0 3
Figure 2.16
35. 35
35
Looking at the last stem-and-leaf display, the
distribution appears almost “symmetrical” (对称的)
The upper portion of the display…
Stems 29, 30*, 30, and 31*
… is almost a mirror image of the lower portion of
the display
Stems 31, 32*, 32, and 33*
36. 36
36
Constructing a Stem-and-Leaf Display
1. Decide what units will be used for the stems and the
leaves. As a general rule, choose units for the stems so
that there will be somewhere between 5 and 20 stems.
2. Place the stems in a column with the smallest stem at
the top of the column and the largest stem at the
bottom.
3. Enter the leaf for each measurement into the row
corresponding to the proper stem. The leaves should
be single-digit numbers (rounded values).
4. If desired, rearrange the leaves so that they are in
increasing order from left to right.
37. 2-37
Constructing a Stem-and-Leaf Display
It is possible to construct a stem-and-leaf display
from measurements containing any number of digits.
Example 2.5
Table 2.13
Number of DVD players sold
for each of last 12 months
Stem and Leaf plot
for
Players
Sold
stem unit =1000
leaf unit =100
Frequency Stem Leaf
1 13 5
2 14 3 7
3 15 2 7 9
3 16 1 5 7
2 17 1 9
0 18
1 19 0
12
13,502 15,932 14,739
15,249 14,312 17,111
19,010 16,121 16,708
17,886 15,665 16,475
Figure 2.17
38. Back-to-Back Stem-and-Leaf Display
Exam1 Exam2
2 3
3
4
5 4
0 5
8 6 5 5
4 4 3 1 1 0 6 2 3
9 9 8 7 6 5 6 6 7 7
2 7 1 3 4 4 4
8 6 7 5 6 7 7 8
3 3 1 8 0 2 3 4
9 8 7 7 6 5 8 5 6 6 7 7 8 9
4 3 3 2 2 1 0 0 9 0 1 1 2 3 3 4 4
8 6 9 5 7 9
We can construct a Back-
to-Back Stem-and-Leaf
Display if we wish to
compare two distributions.
Conclusion:
Exam 1: two concentrations
of scores (bimodal)
Exam 2: almost single
peaked and somewhat
skewed to the left
Figure 2.18
Example 2.6
39. Description of Quantitative 定量 data
Table and Graph
Stem-and-leaf display (茎叶图)
Frequency distributions (频率分布)
Histogram (直方图)
Dot plot (点图 )
40. 40
40
2.4 Misleading Graphs and Charts
Scale Break
Break the vertical scale to exaggerate effect
Mean Salaries at a Major University, 2002 - 2005
Figure 2.19
41. 41
41
Misleading Graphs and Charts:
Scale Effects
Compress vs. stretch the vertical axis to exaggerate or minimize
the effect
Mean Salary Increases at a Major University, 2002 - 2005
Figure 2.20
42. 42
Chapter Summary
Frequency distribution
Bar chart and pie chart
Histogram
Shape of the distribution
Stem-and-leaf display
Misleading graphs and charts