1. Essentials of Statistics 4th Edition
Lecture Slides
Essentials of Statistics 4th Edition
by Mario F. Triola
Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

2. Chapter 2 Summarizing and Graphing Data
2-1 Overview
2-2 Frequency Distributions
2-3 Histograms
2-4 Statistical Graphics
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3. Created by Tom Wegleitner, Centreville, Virginia
Section 2-1
Overview
Created by Tom Wegleitner, Centreville, Virginia
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4. Important Characteristics of Data
Overview
Important Characteristics of Data
1. Center: A representative or average value that indicates where the middle of the data set is located.
2. Variation: A measure of the amount that the values vary among themselves.
3. Distribution: The nature or shape of the distribution of data (such as bell-shaped, uniform, or skewed).
4. Outliers: Sample values that lie very far away from the vast majority of other sample values.
5. Time: Changing characteristics of the data over time.
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5. Frequency Distributions
Section 2-2
Frequency Distributions
Created by Tom Wegleitner, Centreville, Virginia
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6. Key Concept
When working with large data sets, it is often helpful to organize and summarize data by constructing a table called a frequency distribution, defined later. Because computer software and calculators can generate frequency distributions, the details of constructing them are not as important as what they tell us about data sets.
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7. Definition Frequency Distribution (or Frequency Table)
lists data values (either individually or by groups of intervals), along with their corresponding frequencies or counts
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8. Frequency Distribution Ages of Best Actresses Frequency Distribution
Original Data
Frequency Distribution
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9. Frequency Distributions
Definitions
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10. Lower Class Limits Lower Class Limits
are the smallest numbers that can actually belong to different classes
Lower Class
Limits
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11. Upper Class Limits Upper Class Limits
are the largest numbers that can actually belong to different classes
Upper Class
Limits
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12. Class Boundaries Class Boundaries
are the numbers used to separate classes, but without the gaps created by class limits
Editor: Substitute Table 2-2
Class
Boundaries
20.5
30.5
40.5
50.5
60.5
70.5
80.5
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13. Class Midpoints Class Midpoints
can be found by adding the lower class limit to the upper class limit and dividing the sum by two
Class
Midpoints
25.5
35.5
45.5
55.5
65.5
75.5
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14. Class Width Class Width
is the difference between two consecutive lower class limits or two consecutive lower class boundaries
Editor: Substitute Table 2-2
Class
Width 10
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15. Reasons for Constructing Frequency Distributions
1. Large data sets can be summarized.
2. We can gain some insight into the nature of data.
3. We have a basis for constructing important graphs.
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16. Constructing A Frequency Distribution
1. Decide on the number of classes (should be between 5 and 20).
2. Calculate (round up).
class width 
(maximum value) – (minimum value)
number of classes
3. Starting point: Begin by choosing a lower limit of the first class.
Using the lower limit of the first class and class width, proceed to list the lower class limits.
5. List the lower class limits in a vertical column and proceed to enter the upper class limits.
6. Go through the data set putting a tally in the appropriate class for each data value.
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17. Relative Frequency Distribution
includes the same class limits as a frequency distribution, but relative frequencies are used instead of actual frequencies
relative frequency =
class frequency
sum of all frequencies
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18. Relative Frequency Distribution
28/76 = 37%
30/76 = 39%
etc.
Total Frequency = 76
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19. Cumulative Frequency Distribution
Frequencies
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20. Frequency Tables
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21. Critical Thinking Interpreting Frequency Distributions
In later chapters, there will be frequent reference to data with a normal distribution. One key characteristic of a normal distribution is that it has a “bell” shape.
The frequencies start low, then increase to some maximum frequency, then decrease to a low frequency.
The distribution should be approximately symmetric.
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22. Recap In this Section we have discussed
Important characteristics of data
Frequency distributions
Procedures for constructing frequency distributions
Relative frequency distributions
Cumulative frequency distributions
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23. Created by Tom Wegleitner, Centreville, Virginia
Section 2-3
Histograms
Created by Tom Wegleitner, Centreville, Virginia
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24. Key Concept
A histogram is an important type of graph that portrays the nature of the distribution.
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25. Histogram
A bar graph in which the horizontal scale represents the classes of data values and the vertical scale represents the frequencies
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26. Relative Frequency Histogram
Has the same shape and horizontal scale as a histogram, but the vertical scale is marked with relative frequencies instead of actual frequencies
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27. Critical Thinking Interpreting Histograms
One key characteristic of a normal distribution is that it has a “bell” shape. The histogram below illustrates this.
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28. Recap In this Section we have discussed Histograms
Relative Frequency Histograms
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29. Created by Tom Wegleitner, Centreville, Virginia
Section 2-4
Statistical Graphics
Created by Tom Wegleitner, Centreville, Virginia
Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley.

30. Key Concept
This section presents other graphs beyond histograms commonly used in statistical analysis.
The main objective is to understand a data set by using a suitable graph that is effective in revealing some important characteristic.
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31. Frequency Polygon
Uses line segments connected to points directly above class midpoint values
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32. Relative Frequency Polygon
Uses relative frequencies (proportions or percentages) for the vertical scale.
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33. Insert figure 2-6 from page 58
Ogive
A line graph that depicts cumulative frequencies
Insert figure 2-6 from page 58
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34. Dot Plot
Consists of a graph in which each data value is plotted as a point (or dot) along a scale of values
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35. Stemplot (or Stem-and-Leaf Plot)
Represents data by separating each value into two parts: the stem (such as the leftmost digit) and the leaf (such as the rightmost digit)
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36. Multiple Bar Graph Median Income of Males and Females
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37. Pareto Chart
A bar graph for qualitative data, with the bars arranged in order according to frequencies
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38. Pie Chart A graph depicting qualitative data as slices of a pie
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39. Scatter Plot (or Scatter Diagram)
A plot of paired (x,y) data with a horizontal x-axis and a vertical y-axis
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40. Time-Series Graph
Data that have been collected at different points in time
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41. Other Graphs
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42. Recap
In this section we have discussed graphs that are pictures of distributions.
Keep in mind that a graph is a tool for describing, exploring and comparing data.
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43. Critical Thinking: Bad Graphs
Section 2-5
Critical Thinking: Bad Graphs
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44. Key Concept Some graphs are bad in the sense that they contain errors.
Some are bad because they are technically correct, but misleading.
It is important to develop the ability to recognize bad graphs and identify exactly how they are misleading.
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45. Nonzero Axis
Are misleading because one or both of the axes begin at some value other than zero, so that differences are exaggerated.
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46. Annual Incomes of Groups with Different Education Levels
Bars have same width, too busy, too difficult to understand.
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47. Annual Incomes of Groups with Different Education Levels
Misleading. Depicts one-dimensional data with three-dimensional boxes. Last box is 64 times as large as first box, but income is only 4 times as large.
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48. Annual Incomes of Groups with Different Education Levels
Fair, objective, unencumbered by distracting features.
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