Soumettre la recherche
Mettre en ligne
Triola 11 chapter 2
•
Télécharger en tant que PPT, PDF
•
1 j'aime
•
1,491 vues
B
babygirl5810
Suivre
Formation
Technologie
Voyages
Signaler
Partager
Signaler
Partager
1 sur 56
Télécharger maintenant
Recommandé
Triola 11 chapter 3
Triola 11 chapter 3
babygirl5810
Triola ed 11 chapter 1
Triola ed 11 chapter 1
babygirl5810
Chapter2 biostatistics
Chapter2 biostatistics
Dr Ghaiath Hussein
Learning to Rank - From pairwise approach to listwise
Learning to Rank - From pairwise approach to listwise
Hasan H Topcu
Decision tree
Decision tree
Ami_Surati
Decision tree
Decision tree
Tilani Gunawardena PhD(UNIBAS), BSc(Pera), FHEA(UK), CEng, MIESL
Machine Learning 3 - Decision Tree Learning
Machine Learning 3 - Decision Tree Learning
butest
Data mining
Data mining
Behnaz Motavali
Recommandé
Triola 11 chapter 3
Triola 11 chapter 3
babygirl5810
Triola ed 11 chapter 1
Triola ed 11 chapter 1
babygirl5810
Chapter2 biostatistics
Chapter2 biostatistics
Dr Ghaiath Hussein
Learning to Rank - From pairwise approach to listwise
Learning to Rank - From pairwise approach to listwise
Hasan H Topcu
Decision tree
Decision tree
Ami_Surati
Decision tree
Decision tree
Tilani Gunawardena PhD(UNIBAS), BSc(Pera), FHEA(UK), CEng, MIESL
Machine Learning 3 - Decision Tree Learning
Machine Learning 3 - Decision Tree Learning
butest
Data mining
Data mining
Behnaz Motavali
Arabic text categorization algorithm using vector evaluation method
Arabic text categorization algorithm using vector evaluation method
ijcsit
Farthest Neighbor Approach for Finding Initial Centroids in K- Means
Farthest Neighbor Approach for Finding Initial Centroids in K- Means
Waqas Tariq
From decision trees to random forests
From decision trees to random forests
Viet-Trung TRAN
Decision Tree
Decision Tree
Konkuk University, Korea
slides
slides
butest
Tree net and_randomforests_2009
Tree net and_randomforests_2009
Matthew Magistrado
Lecture 2: Preliminaries (Understanding and Preprocessing data)
Lecture 2: Preliminaries (Understanding and Preprocessing data)
Marina Santini
NLP - Sentiment Analysis
NLP - Sentiment Analysis
Rupak Roy
Ch 9-1.Machine Learning: Symbol-based
Ch 9-1.Machine Learning: Symbol-based
butest
Ai inductive bias and knowledge
Ai inductive bias and knowledge
Dexter Montesclaros
Summarization using ntc approach based on keyword extraction for discussion f...
Summarization using ntc approach based on keyword extraction for discussion f...
eSAT Publishing House
Athifah procedia technology_2013
Athifah procedia technology_2013
Nong Tiun
Intelligent Systems - Predictive Analytics Project
Intelligent Systems - Predictive Analytics Project
Shreya Chakrabarti
019# mean, median, mode
019# mean, median, mode
Abdul ghafoor
Introduction to Machine Learning Aristotelis Tsirigos
Introduction to Machine Learning Aristotelis Tsirigos
butest
Tweets Classification using Naive Bayes and SVM
Tweets Classification using Naive Bayes and SVM
Trilok Sharma
Part 1
Part 1
butest
Profile Analysis of Users in Data Analytics Domain
Profile Analysis of Users in Data Analytics Domain
Drjabez
Comparative study of classification algorithm for text based categorization
Comparative study of classification algorithm for text based categorization
eSAT Journals
INDEPENDENT AND DEPENDENT EVENTS
INDEPENDENT AND DEPENDENT EVENTS
nirabmedhi91
Independent and dependent events notes
Independent and dependent events notes
RDemolina
Independent or Dependant Probability
Independent or Dependant Probability
Linda Williams
Contenu connexe
Tendances
Arabic text categorization algorithm using vector evaluation method
Arabic text categorization algorithm using vector evaluation method
ijcsit
Farthest Neighbor Approach for Finding Initial Centroids in K- Means
Farthest Neighbor Approach for Finding Initial Centroids in K- Means
Waqas Tariq
From decision trees to random forests
From decision trees to random forests
Viet-Trung TRAN
Decision Tree
Decision Tree
Konkuk University, Korea
slides
slides
butest
Tree net and_randomforests_2009
Tree net and_randomforests_2009
Matthew Magistrado
Lecture 2: Preliminaries (Understanding and Preprocessing data)
Lecture 2: Preliminaries (Understanding and Preprocessing data)
Marina Santini
NLP - Sentiment Analysis
NLP - Sentiment Analysis
Rupak Roy
Ch 9-1.Machine Learning: Symbol-based
Ch 9-1.Machine Learning: Symbol-based
butest
Ai inductive bias and knowledge
Ai inductive bias and knowledge
Dexter Montesclaros
Summarization using ntc approach based on keyword extraction for discussion f...
Summarization using ntc approach based on keyword extraction for discussion f...
eSAT Publishing House
Athifah procedia technology_2013
Athifah procedia technology_2013
Nong Tiun
Intelligent Systems - Predictive Analytics Project
Intelligent Systems - Predictive Analytics Project
Shreya Chakrabarti
019# mean, median, mode
019# mean, median, mode
Abdul ghafoor
Introduction to Machine Learning Aristotelis Tsirigos
Introduction to Machine Learning Aristotelis Tsirigos
butest
Tweets Classification using Naive Bayes and SVM
Tweets Classification using Naive Bayes and SVM
Trilok Sharma
Part 1
Part 1
butest
Profile Analysis of Users in Data Analytics Domain
Profile Analysis of Users in Data Analytics Domain
Drjabez
Comparative study of classification algorithm for text based categorization
Comparative study of classification algorithm for text based categorization
eSAT Journals
Tendances
(19)
Arabic text categorization algorithm using vector evaluation method
Arabic text categorization algorithm using vector evaluation method
Farthest Neighbor Approach for Finding Initial Centroids in K- Means
Farthest Neighbor Approach for Finding Initial Centroids in K- Means
From decision trees to random forests
From decision trees to random forests
Decision Tree
Decision Tree
slides
slides
Tree net and_randomforests_2009
Tree net and_randomforests_2009
Lecture 2: Preliminaries (Understanding and Preprocessing data)
Lecture 2: Preliminaries (Understanding and Preprocessing data)
NLP - Sentiment Analysis
NLP - Sentiment Analysis
Ch 9-1.Machine Learning: Symbol-based
Ch 9-1.Machine Learning: Symbol-based
Ai inductive bias and knowledge
Ai inductive bias and knowledge
Summarization using ntc approach based on keyword extraction for discussion f...
Summarization using ntc approach based on keyword extraction for discussion f...
Athifah procedia technology_2013
Athifah procedia technology_2013
Intelligent Systems - Predictive Analytics Project
Intelligent Systems - Predictive Analytics Project
019# mean, median, mode
019# mean, median, mode
Introduction to Machine Learning Aristotelis Tsirigos
Introduction to Machine Learning Aristotelis Tsirigos
Tweets Classification using Naive Bayes and SVM
Tweets Classification using Naive Bayes and SVM
Part 1
Part 1
Profile Analysis of Users in Data Analytics Domain
Profile Analysis of Users in Data Analytics Domain
Comparative study of classification algorithm for text based categorization
Comparative study of classification algorithm for text based categorization
En vedette
INDEPENDENT AND DEPENDENT EVENTS
INDEPENDENT AND DEPENDENT EVENTS
nirabmedhi91
Independent and dependent events notes
Independent and dependent events notes
RDemolina
Independent or Dependant Probability
Independent or Dependant Probability
Linda Williams
Independent and Dependent Events
Independent and Dependent Events
ctybishop
Probability - Independent & Dependent Events
Probability - Independent & Dependent Events
Bitsy Griffin
Triola t11 chapter4
Triola t11 chapter4
babygirl5810
Discrete Probability Distributions
Discrete Probability Distributions
mandalina landy
Discrete and continuous probability distributions ppt @ bec doms
Discrete and continuous probability distributions ppt @ bec doms
Babasab Patil
Probability Distributions
Probability Distributions
rishi.indian
Basic Concept Of Probability
Basic Concept Of Probability
guest45a926
Probability concept and Probability distribution
Probability concept and Probability distribution
Southern Range, Berhampur, Odisha
PROBABILITY
PROBABILITY
VIV13
Probability Powerpoint
Probability Powerpoint
spike2904
En vedette
(13)
INDEPENDENT AND DEPENDENT EVENTS
INDEPENDENT AND DEPENDENT EVENTS
Independent and dependent events notes
Independent and dependent events notes
Independent or Dependant Probability
Independent or Dependant Probability
Independent and Dependent Events
Independent and Dependent Events
Probability - Independent & Dependent Events
Probability - Independent & Dependent Events
Triola t11 chapter4
Triola t11 chapter4
Discrete Probability Distributions
Discrete Probability Distributions
Discrete and continuous probability distributions ppt @ bec doms
Discrete and continuous probability distributions ppt @ bec doms
Probability Distributions
Probability Distributions
Basic Concept Of Probability
Basic Concept Of Probability
Probability concept and Probability distribution
Probability concept and Probability distribution
PROBABILITY
PROBABILITY
Probability Powerpoint
Probability Powerpoint
Similaire à Triola 11 chapter 2
Stat11t chapter2
Stat11t chapter2
raylenepotter
Section2.2
Section2.2
professorjgordon
Aron chpt 1 ed
Aron chpt 1 ed
Sandra Nicks
Chapter 3 Section 4.ppt
Chapter 3 Section 4.ppt
ManoloTaquire
Chapter 2 Section 3.ppt
Chapter 2 Section 3.ppt
ManoloTaquire
Research Method for Business chapter 12
Research Method for Business chapter 12
Mazhar Poohlah
business and economics statics principles
business and economics statics principles
devvpillpersonal
MemFunc.doc
MemFunc.doc
butest
Data analysis techniques
Data analysis techniques
Choge2
4 CREATING GRAPHS A PICTURE REALLY IS WORTH A THOUSAND WORDS4 M.docx
4 CREATING GRAPHS A PICTURE REALLY IS WORTH A THOUSAND WORDS4 M.docx
gilbertkpeters11344
Intro to Data Analysis and Descriptive Statistics FD 502 Presentation.pdf
Intro to Data Analysis and Descriptive Statistics FD 502 Presentation.pdf
VerliePayotLamsen
An Introduction to SPSS
An Introduction to SPSS
Rayman Soe
Math 221 Massive Success / snaptutorial.com
Math 221 Massive Success / snaptutorial.com
Stephenson164
Stat11t chapter3
Stat11t chapter3
raylenepotter
Descriptive Statistics and Data Visualization
Descriptive Statistics and Data Visualization
Douglas Joubert
Data Representations
Data Representations
bujols
Chapter 1 Section 3.ppt
Chapter 1 Section 3.ppt
ManoloTaquire
STATISTICSINFORMED DECISIONS USING DATAFifth EditionChapte.docx
STATISTICSINFORMED DECISIONS USING DATAFifth EditionChapte.docx
rafaelaj1
Intoduction to statistics
Intoduction to statistics
SachinKumar1799
Introduction to Modeling
Introduction to Modeling
JMP software from SAS
Similaire à Triola 11 chapter 2
(20)
Stat11t chapter2
Stat11t chapter2
Section2.2
Section2.2
Aron chpt 1 ed
Aron chpt 1 ed
Chapter 3 Section 4.ppt
Chapter 3 Section 4.ppt
Chapter 2 Section 3.ppt
Chapter 2 Section 3.ppt
Research Method for Business chapter 12
Research Method for Business chapter 12
business and economics statics principles
business and economics statics principles
MemFunc.doc
MemFunc.doc
Data analysis techniques
Data analysis techniques
4 CREATING GRAPHS A PICTURE REALLY IS WORTH A THOUSAND WORDS4 M.docx
4 CREATING GRAPHS A PICTURE REALLY IS WORTH A THOUSAND WORDS4 M.docx
Intro to Data Analysis and Descriptive Statistics FD 502 Presentation.pdf
Intro to Data Analysis and Descriptive Statistics FD 502 Presentation.pdf
An Introduction to SPSS
An Introduction to SPSS
Math 221 Massive Success / snaptutorial.com
Math 221 Massive Success / snaptutorial.com
Stat11t chapter3
Stat11t chapter3
Descriptive Statistics and Data Visualization
Descriptive Statistics and Data Visualization
Data Representations
Data Representations
Chapter 1 Section 3.ppt
Chapter 1 Section 3.ppt
STATISTICSINFORMED DECISIONS USING DATAFifth EditionChapte.docx
STATISTICSINFORMED DECISIONS USING DATAFifth EditionChapte.docx
Intoduction to statistics
Intoduction to statistics
Introduction to Modeling
Introduction to Modeling
Dernier
Sociology 101 Demonstration of Learning Exhibit
Sociology 101 Demonstration of Learning Exhibit
jbellavia9
Understanding Accommodations and Modifications
Understanding Accommodations and Modifications
MJDuyan
TỔNG ÔN TẬP THI VÀO LỚP 10 MÔN TIẾNG ANH NĂM HỌC 2023 - 2024 CÓ ĐÁP ÁN (NGỮ Â...
TỔNG ÔN TẬP THI VÀO LỚP 10 MÔN TIẾNG ANH NĂM HỌC 2023 - 2024 CÓ ĐÁP ÁN (NGỮ Â...
Nguyen Thanh Tu Collection
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
kauryashika82
How to Give a Domain for a Field in Odoo 17
How to Give a Domain for a Field in Odoo 17
Celine George
The basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptx
heathfieldcps1
This PowerPoint helps students to consider the concept of infinity.
This PowerPoint helps students to consider the concept of infinity.
christianmathematics
Food safety_Challenges food safety laboratories_.pdf
Food safety_Challenges food safety laboratories_.pdf
Sherif Taha
Holdier Curriculum Vitae (April 2024).pdf
Holdier Curriculum Vitae (April 2024).pdf
agholdier
1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdf
QucHHunhnh
Accessible Digital Futures project (20/03/2024)
Accessible Digital Futures project (20/03/2024)
Jisc
SOC 101 Demonstration of Learning Presentation
SOC 101 Demonstration of Learning Presentation
camerronhm
SKILL OF INTRODUCING THE LESSON MICRO SKILLS.pptx
SKILL OF INTRODUCING THE LESSON MICRO SKILLS.pptx
Amanpreet Kaur
1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdf
QucHHunhnh
Making communications land - Are they received and understood as intended? we...
Making communications land - Are they received and understood as intended? we...
Association for Project Management
Unit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptx
VishalSingh1417
Asian American Pacific Islander Month DDSD 2024.pptx
Asian American Pacific Islander Month DDSD 2024.pptx
David Douglas School District
Spellings Wk 3 English CAPS CARES Please Practise
Spellings Wk 3 English CAPS CARES Please Practise
AnaAcapella
ComPTIA Overview | Comptia Security+ Book SY0-701
ComPTIA Overview | Comptia Security+ Book SY0-701
bronxfugly43
microwave assisted reaction. General introduction
microwave assisted reaction. General introduction
Maksud Ahmed
Dernier
(20)
Sociology 101 Demonstration of Learning Exhibit
Sociology 101 Demonstration of Learning Exhibit
Understanding Accommodations and Modifications
Understanding Accommodations and Modifications
TỔNG ÔN TẬP THI VÀO LỚP 10 MÔN TIẾNG ANH NĂM HỌC 2023 - 2024 CÓ ĐÁP ÁN (NGỮ Â...
TỔNG ÔN TẬP THI VÀO LỚP 10 MÔN TIẾNG ANH NĂM HỌC 2023 - 2024 CÓ ĐÁP ÁN (NGỮ Â...
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
How to Give a Domain for a Field in Odoo 17
How to Give a Domain for a Field in Odoo 17
The basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptx
This PowerPoint helps students to consider the concept of infinity.
This PowerPoint helps students to consider the concept of infinity.
Food safety_Challenges food safety laboratories_.pdf
Food safety_Challenges food safety laboratories_.pdf
Holdier Curriculum Vitae (April 2024).pdf
Holdier Curriculum Vitae (April 2024).pdf
1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdf
Accessible Digital Futures project (20/03/2024)
Accessible Digital Futures project (20/03/2024)
SOC 101 Demonstration of Learning Presentation
SOC 101 Demonstration of Learning Presentation
SKILL OF INTRODUCING THE LESSON MICRO SKILLS.pptx
SKILL OF INTRODUCING THE LESSON MICRO SKILLS.pptx
1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdf
Making communications land - Are they received and understood as intended? we...
Making communications land - Are they received and understood as intended? we...
Unit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptx
Asian American Pacific Islander Month DDSD 2024.pptx
Asian American Pacific Islander Month DDSD 2024.pptx
Spellings Wk 3 English CAPS CARES Please Practise
Spellings Wk 3 English CAPS CARES Please Practise
ComPTIA Overview | Comptia Security+ Book SY0-701
ComPTIA Overview | Comptia Security+ Book SY0-701
microwave assisted reaction. General introduction
microwave assisted reaction. General introduction
Triola 11 chapter 2
1.
Chapter 2 Summarizing
and Graphing Data Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. 2-1 Review and Preview 2-2 Frequency Distributions 2-3 Histograms 2-4 Statistical Graphics 2-5 Critical Thinking: Bad Graphs
2.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Section 2-1 Review and Preview
3.
4.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Copyright © 2010 Pearson Education Section 2-2 Frequency Distributions Copyright © 2010 Pearson Education
5.
6.
7.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Pulse Rates of Females and Males Original Data Copyright © 2010 Pearson Education
8.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Copyright © 2010 Pearson Education Frequency Distribution Pulse Rates of Females The frequency for a particular class is the number of original values that fall into that class.
9.
Frequency Distributions Copyright
© 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Copyright © 2010 Pearson Education Definitions
10.
11.
12.
13.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Copyright © 2010 Pearson Education Class Midpoints are the values in the middle of the classes and can be found by adding the lower class limit to the upper class Class Midpoints limit and dividing the sum by two 64.5 74.5 84.5 94.5 104.5 114.5 124.5
14.
15.
16.
17.
18.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Copyright © 2010 Pearson Education Relative Frequency Distribution * 12/40 100 = 30% Total Frequency = 40 *
19.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Copyright © 2010 Pearson Education Cumulative Frequency Distribution Cumulative Frequencies
20.
Frequency Tables Copyright
© 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Copyright © 2010 Pearson Education
21.
22.
23.
24.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Copyright © 2010 Pearson Education Section 2-3 Histograms
25.
Key Concept Copyright
© 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Copyright © 2010 Pearson Education We use a visual tool called a histogram to analyze the shape of the distribution of the data.
26.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Copyright © 2010 Pearson Education Histogram A graph consisting of bars of equal width drawn adjacent to each other (without gaps). The horizontal scale represents the classes of quantitative data values and the vertical scale represents the frequencies. The heights of the bars correspond to the frequency values.
27.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Copyright © 2010 Pearson Education Histogram Basically a graphic version of a frequency distribution.
28.
29.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Copyright © 2010 Pearson Education 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
30.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Copyright © 2010 Pearson Education Objective is not simply to construct a histogram, but rather to understand something about the data. When graphed, a normal distribution has a “bell” shape. Characteristic of the bell shape are Critical Thinking Interpreting Histograms (1) The frequencies increase to a maximum, and then decrease, and (2) symmetry, with the left half of the graph roughly a mirror image of the right half. The histogram on the next slide illustrates this.
31.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Copyright © 2010 Pearson Education Critical Thinking Interpreting Histograms
32.
33.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Section 2-4 Statistical Graphics
34.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Key Concept This section discusses other types of statistical graphs. Our objective is to identify a suitable graph for representing the data set. The graph should be effective in revealing the important characteristics of the data.
35.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Frequency Polygon Uses line segments connected to points directly above class midpoint values
36.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Relative Frequency Polygon Uses relative frequencies (proportions or percentages) for the vertical scale.
37.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Ogive A line graph that depicts cumulative frequencies
38.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Dot Plot Consists of a graph in which each data value is plotted as a point (or dot) along a scale of values. Dots representing equal values are stacked.
39.
Stemplot (or Stem-and-Leaf
Plot) Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Represents quantitative data by separating each value into two parts: the stem (such as the leftmost digit) and the leaf (such as the rightmost digit) Pulse Rates of Females
40.
Bar Graph
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Uses bars of equal width to show frequencies of categories of qualitative data. Vertical scale represents frequencies or relative frequencies. Horizontal scale identifies the different categories of qualitative data. A multiple bar graph has two or more sets of bars, and is used to compare two or more data sets.
41.
Multiple Bar Graph
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Median Income of Males and Females
42.
43.
44.
45.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Time-Series Graph Data that have been collected at different points in time: time-series data
46.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Important Principles Suggested by Edward Tufte For small data sets of 20 values or fewer, use a table instead of a graph. A graph of data should make the viewer focus on the true nature of the data, not on other elements, such as eye-catching but distracting design features. Do not distort data, construct a graph to reveal the true nature of the data. Almost all of the ink in a graph should be used for the data, not the other design elements.
47.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Important Principles Suggested by Edward Tufte Don’t use screening consisting of features such as slanted lines, dots, cross-hatching, because they create the uncomfortable illusion of movement. Don’t use area or volumes for data that are actually one-dimensional in nature. (Don’t use drawings of dollar bills to represent budget amounts for different years.) Never publish pie charts, because they waste ink on nondata components, and they lack appropriate scale.
48.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Car Reliability Data
49.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Recap In this section we saw that graphs are excellent tools for describing, exploring and comparing data. Describing data : Histogram - consider distribution, center, variation, and outliers. Exploring data : features that reveal some useful and/or interesting characteristic of the data set. Comparing data : Construct similar graphs to compare data sets.
50.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Section 2-5 Critical Thinking: Bad Graphs
51.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. 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.
52.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Nonzero Axis Are misleading because one or both of the axes begin at some value other than zero, so that differences are exaggerated.
53.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Pictographs are drawings of objects. Three-dimensional objects - money bags, stacks of coins, army tanks (for army expenditures), people (for population sizes), barrels (for oil production), and houses (for home construction) are commonly used to depict data. These drawings can create false impressions that distort the data. If you double each side of a square, the area does not merely double; it increases by a factor of four;if you double each side of a cube, the volume does not merely double; it increases by a factor of eight. Pictographs using areas and volumes can therefore be very misleading.
54.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Annual Incomes of Groups with Different Education Levels Bars have same width, too busy, too difficult to understand.
55.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. 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.
56.
Copyright © 2010,
2007, 2004 Pearson Education, Inc. All Rights Reserved. Annual Incomes of Groups with Different Education Levels Fair, objective, unencumbered by distracting features.
Télécharger maintenant