chapter 2 250110 083240
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TRANSCRIPT
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Chapter 2Chapter 2
Frequency DistributionsFrequency Distributions
and Graphsand Graphs
Reference: Allan G. Bluman (2007) Elementary Statistics: A Step-by Step Approach. New York : McGraw Hill
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ObjectivesObjectives Organize data using frequency distributions. Represent data in frequency distributions
graphically using histograms, frequency polygons, and ogives.
Represent data using Pareto charts, time series graphs, and pie graphs.
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Organizing DataOrganizing Data When data are collected in original form,
they are called raw dataraw data. When the raw data is organized into a
frequency distributionfrequency distribution, the frequencyfrequency will be the number of values in a specific class of the distribution.
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Organizing DataOrganizing Data A frequency distributionfrequency distribution is the organizing of raw data in
table form, using classes and frequencies
3 types: Categorical frequency distribution Grouped frequency distribution Ungrouped frequency distribution
No. of data values contained in a specific class
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Types of Frequency Types of Frequency DistributionsDistributions
Categorical frequency distributionsCategorical frequency distributions -- can be used for data that can be placed in specific categories, such as nominal- or ordinal-level data.
Examples -Examples - political affiliation, religious affiliation, blood type etc.
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Categorical Frequency Distribution - Categorical Frequency Distribution - Example
Step 1:Step 1: Construct a frequency distribution table.
Step 2:Step 2: Tally the data and place the results in the 2nd column.
Step 3:Step 3: Count the tallies & place the results in the 3rd column.
A
Class
B
Tally
C
Frequency
D
Percent
A
B
O
AB
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Categorical Frequency Distribution - Categorical Frequency Distribution - Example
Step 4:Step 4: Find the percentage of values in each class by using the formula:% = f/n x 100% where f = frequency of the class AND
n = total number of values
Step 5:Step 5: Find the totals for the 3rd and 4th column.
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Blood Type Frequency Distribution - Blood Type Frequency Distribution - Example
Class Tally Frequency Percent
A 5 20
B 7 28
O 9 36
AB 4 16
Total 25 100
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Grouped Frequency DistributionsGrouped Frequency Distributions
Grouped frequency distributions -Grouped frequency distributions - can be used when the range of values in the data set is very large. The data must be grouped into classes that are more than one unit in width.
Examples -Examples - the life of boat batteries in hours.
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Lifetimes of Boat BatteriesLifetimes of Boat Batteries - Example
Class
limits
Class boundaries Tally Frequency
Cumulative frequency
24 – 30 23.5 – 30.5 ||| 3 3
31 – 37 30.5 – 37.5 | 1 4
38 – 44 37.5 – 44.5 |||| 5 9
45 – 51 44.5 – 51.5 |||| |||| 9 18
52 – 58 51.5 – 58.5 |||| | 6 24
59 – 65 58.5 – 65.5 | 1 25
25
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Terms Associated with a Terms Associated with a Grouped Frequency DistributionGrouped Frequency Distribution
Class limits represent the smallest and largest data values that can be included in a class.
In the lifetimes of boat batteries example, the values 24 and 30 of the first class are the class class limitslimits.
The lower classlower class limit is 24 and the upper classupper class limit is 30.
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The class boundariesclass boundaries are used to separate the classes so that there are no gaps in the frequency distribution.
Class limits should have the same decimal place value as the data, but the class boundaries should have one additional place value and end in a 5.
Terms Associated with a Grouped Terms Associated with a Grouped Frequency DistributionFrequency Distribution
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The class widthclass width for a class in a frequency distribution is found by subtracting the lower (or upper) class limit of one class minus the lower (or upper) class limit of the previous class.
Terms Associated with a Terms Associated with a Grouped Frequency DistributionGrouped Frequency Distribution
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Guidelines for Constructing Guidelines for Constructing a Frequency Distributiona Frequency Distribution
There should be between 5 and 20 classes.
The class width should be an odd number. To ensure the midpoint of each class has the
same place value as the data The classes must be mutually exclusive.
Non-overlapping class limits
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Guidelines for Constructing Guidelines for Constructing a Frequency Distributiona Frequency Distribution
The classes must be continuous. No gaps in a frequency distribution. A class must be included even though there’s
no value in it. The classes must be exhaustive.
Enough classes to accommodate all the data.
The class must be equal in width. To avoid distorted view of the data Exception occurs when a distribution has a
class that’s open- ended.
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Open-Ended Distribution
Age Frequency
10 – 20 3
21 – 31 6
32 – 42 4
43 – 53 10
54 and above 8
Minutes Frequency
Below 100 16
110 – 114 24
115 – 119 38
120 – 124 14
125 – 129 5
Example 1Example 2
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Procedure for Constructing Procedure for Constructing a Grouped Frequency Distributiona Grouped Frequency Distribution
Find the highest and lowest value. Find the range. Select the number of classes desired. Find the width by dividing the range by
the number of classes and rounding up.
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Select a starting point (usually the lowest value); add the width to get the lower limits.
Find the upper class limits. Find the boundaries. Tally the data, find the frequencies,
and find the cumulative frequency.
Procedure for Constructing Procedure for Constructing a Grouped Frequency Distributiona Grouped Frequency Distribution
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In a survey of 20 patients who smoked, the following data were obtained. Each value represents the number of cigarettes the patient smoked per day. Construct a frequency distribution using six classes. (The data is given on the next slide.)
Grouped Frequency Distribution - Grouped Frequency Distribution - Example
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10 8 6 14
22 13 17 19
11 9 18 14
13 12 15 15
5 11 16 11
Grouped Frequency Distribution - Grouped Frequency Distribution - Example
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Step 1:Step 1: Find the highest and lowest values: H = 22 and L = 5.
Step 2:Step 2: Find the range: R = H – L = 22 – 5 = 17.
Step 3:Step 3: Select the number of classes desired. In this case it is equal to 6.
Grouped Frequency Distribution - Grouped Frequency Distribution - Example
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Step 4:Step 4: Find the class width by dividing the range by the number of classes. Width = 17/6 = 2.83. This value is rounded up to 3.
Grouped Frequency Distribution - Grouped Frequency Distribution - Example
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Step 5:Step 5: Select a starting point for the lowest class limit. For convenience, this value is chosen to be 5, the smallest data value. The lower class limits will be 5, 8, 11, 14, 17, and 20.
Grouped Frequency Distribution - Grouped Frequency Distribution - Example
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Step 6:Step 6: The upper class limits will be 7, 10, 13, 16, 19, and 22. For example, the upper limit for the first class is computed as 8 - 1, etc.
Grouped Frequency Distribution - Grouped Frequency Distribution - Example
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Step 7:Step 7: Find the class boundaries by subtracting 0.5 from each lower class limit and adding 0.5 to the upper class limit.
Grouped Frequency Distribution - Grouped Frequency Distribution - Example
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Step 8:Step 8: Tally the data, write the numerical values for the tallies in the frequency column, and find the cumulative frequencies.
The grouped frequency distribution is shown on the next slide.
Grouped Frequency Distribution - Grouped Frequency Distribution - Example
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Class Limits Class Boundaries Frequency Cumulative Frequency
05 to 07 4.5 - 7.5 2 2
08 to 10 7.5 - 10.5 3 5
11 to 13 10.5 - 13.5 6 11
14 to 16 13.5 - 16.5 5 16
17 to 19 16.5 - 19.5 3 1920 to 22 19.5 - 22.5 1 20
Note: The dash “-” represents “to”.
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Ungrouped Frequency Ungrouped Frequency DistributionsDistributions
Ungrouped frequency distributions -Ungrouped frequency distributions - can be used for data that can be enumerated and when the range of values in the data set is not large.
Examples -Examples - number of miles your instructors have to travel from home to campus, number of girls in a 4-child family etc.
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Number of Miles TraveledNumber of Miles Traveled - Example
Class Frequency
5 24
10 16
15 10
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To organize the data in a meaningful, intelligible way.
To enable the reader to determine the nature or shape of distribution.
To facilitate computational procedures for measures of average and spread.
To enable the researcher to draw charts and graphs for the presentation of data.
To enable the reader to make comparisons among different data sets.
Reasons for constructing a frequency Reasons for constructing a frequency distributiondistribution
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Histograms, Frequency Histograms, Frequency Polygons, and OgivesPolygons, and Ogives
The three most commonly used The three most commonly used graphs in research are:graphs in research are:
The histogram. The frequency polygon. The cumulative frequency graph, or
ogive (pronounced o-jive).
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The histogramhistogram is a graph that displays the data by using vertical bars of various heights to represent the frequencies.
Histograms, Frequency Histograms, Frequency Polygons, and OgivesPolygons, and Ogives
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Example of a HistogramExample of a Histogram
2017141185
6
5
4
3
2
1
0
Number of Cigarettes Smoked per Day
Freq
uenc
y
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A frequency polygonfrequency polygon is a graph that displays the data by using lines that connect points plotted for frequencies at the midpoint of classes. The frequencies represent the heights of the midpoints.
Histograms, Frequency Histograms, Frequency Polygons, and OgivesPolygons, and Ogives
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Example of a Frequency PolygonExample of a Frequency Polygon
Frequency Polygon
262320171411852
6
5
4
3
2
1
0
Number of Cigarettes Smoked per Day
Fre
quen
cy
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A cumulative frequency graphcumulative frequency graph or ogiveogive is a graph that represents the cumulative frequencies for the classes in a frequency distribution.
Histograms, Frequency Histograms, Frequency Polygons, and OgivesPolygons, and Ogives
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Example of an OgiveExample of an Ogive
262320171411852
20
10
0
Number of Cigarettes Smoked per Day
Cum
ulat
ive
Fre
quen
cyOgive
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Other Types of GraphsOther Types of Graphs
Pareto charts -Pareto charts - a Pareto chart is used to represent a frequency distribution for a categorical variable.
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Other Types of GraphsOther Types of Graphs- Pareto Chart- Pareto Chart
When constructing a Pareto chart - Make the bars the same width. Arrange the data from largest to
smallest according to frequencies. Make the units that are used for the
frequency equal in size.
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Example of a Pareto ChartExample of a Pareto Chart
Homicide
RobberyRape
Assault
13 29 34164 5.412.114.268.3
100.0 94.6 82.5 68.3
250
200
150
100
50
0
100
80
60
40
20
0
Defect
CountPercentCum %
Pe r
cent
Co u
nt
Enforcement Officers in U.S. National Parks During 1995. Pareto Chart for the number of Crimes Investigated by Law
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Other Types of GraphsOther Types of Graphs
Time series graph -Time series graph - A time series graph represents data that occur over a specific period of time.
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Other Types of Graphs Other Types of Graphs - Time Series Graph- Time Series Graph
19941993199219911990
89
87
85
83
81
79
77
75
Year
Rid
ersh
ip (
in m
illio
ns)PORT AUTHORITY TRANSIT RIDERSHIP
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Other Types of GraphsOther Types of Graphs
Pie graph -Pie graph - A pie graph is a circle that is divided into sections or wedges according to the percentage of frequencies in each category of the distribution.
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Other Types of Graphs Other Types of Graphs - Pie Graph- Pie Graph
Robbery (29, 12.1%)
Rape (34, 14.2%)
Assaults (164, 68.3%)
Homicide (13, 5.4%)
Pie Chart of the Number of Crimes Investigated byLaw Enforcement Officers In U.S. National Parks During 1995