SlideShare a Scribd company logo
1 of 5
Download to read offline
Ch2.7_DataMeasuresCentralTendency.notebook                                                           September 19, 2011



                                              Warm Up
                                                                             y
                                                                       6



               Graph                                                   5

                                                                       4

                                                                       3



               y < x ­ 4                 ­6   ­5   ­4   ­3   ­2   ­1
                                                                       2

                                                                       1

                                                                         0       1   2   3   4   5    6
                                                                                                          x


                                                                       ­1

                                                                       ­2

                                                                       ­3

                                                                       ­4

                                                                       ­5

                                                                       ­6




           Chapter 2.7
           Data and Measures of Central Tendency




                                                                                                                          1
Ch2.7_DataMeasuresCentralTendency.notebook           September 19, 2011



                            Vocab
           Statistics ­ the study of data

           Population ­ large (huuuuuuuuge) group of data

           Sample ­ smaller group from the population




                    Frequency Table
               A way to compile and organize data
                       # of pets
                                         Frequency
                       at home
                       0
                       1
                       2
                       3 or more



                                                                          2
Ch2.7_DataMeasuresCentralTendency.notebook           September 19, 2011


                Fill in the Frequency Table with the 
                the given test scores in red
           Score Interval   Frequency
                                             90 83 68
           90 ­ 100
                                             34 85 76
           80 ­ 89                           79 89 80
           70 ­ 79                           77 59 91
                                             81 72 64
           60 ­ 69
                                             98 96 62
           50 ­ 59
           49 or lower



                 Measures of Central Tendency
            Mean ­ average of data

            Median ­ middle number (when arranged in order)

            Mode ­ most frequent value




                                                                          3
Ch2.7_DataMeasuresCentralTendency.notebook                          September 19, 2011



                          Example
               Find the mean, median, and mode of the following fourteen 
               numbers

                 4, 6, 2, 8, 9, 4, 6, 4, 5, 7, 5, 8, 3, 2
               Mean (average) = 
                                     Sum of numbers  
                                               14


             Median (middle #) = 
                  2, 2, 3, 4, 4, 4, 5, 5, 6, 6, 7, 8, 8, 9

             Mode (most common) = 




                           Example
          Find the mean, median, and mode of the following ACT scores
                    22, 28, 30, 27, 25, 31, 25, 26, 19
           Mean (average) = 




         Median (middle #) = 




          Mode (most common) = 

                                                                                         4
Ch2.7_DataMeasuresCentralTendency.notebook   September 19, 2011




                           Page 84
                           1 ­ 14 all




                                                                  5

More Related Content

What's hot (10)

10 ch ken black solution
10 ch ken black solution10 ch ken black solution
10 ch ken black solution
 
set analysis QLIKSENSE
set analysis QLIKSENSEset analysis QLIKSENSE
set analysis QLIKSENSE
 
14 ch ken black solution
14 ch ken black solution14 ch ken black solution
14 ch ken black solution
 
08 ch ken black solution
08 ch ken black solution08 ch ken black solution
08 ch ken black solution
 
7 qc tools
7 qc tools7 qc tools
7 qc tools
 
07 ch ken black solution
07 ch ken black solution07 ch ken black solution
07 ch ken black solution
 
09 ch ken black solution
09 ch ken black solution09 ch ken black solution
09 ch ken black solution
 
13 ch ken black solution
13 ch ken black solution13 ch ken black solution
13 ch ken black solution
 
Gbs1
Gbs1Gbs1
Gbs1
 
Lesson (chapter 4)
Lesson (chapter 4)Lesson (chapter 4)
Lesson (chapter 4)
 

Similar to Ch2.7 Data Measures of Central Tendency

Lesson 6 measures of central tendency
Lesson 6 measures of central tendencyLesson 6 measures of central tendency
Lesson 6 measures of central tendency
nurun2010
 
Ch17 lab r_verdu103: Entry level statistics exercise (descriptives)
Ch17 lab r_verdu103: Entry level statistics exercise (descriptives)Ch17 lab r_verdu103: Entry level statistics exercise (descriptives)
Ch17 lab r_verdu103: Entry level statistics exercise (descriptives)
Sherri Gunder
 
Math investigation using metacognition
Math investigation using metacognitionMath investigation using metacognition
Math investigation using metacognition
Carlo Magno
 
9 basic seven tools of quality
9 basic seven tools of quality9 basic seven tools of quality
9 basic seven tools of quality
goldenbull268
 
Statistical Methods
Statistical MethodsStatistical Methods
Statistical Methods
guest2137aa
 
Statistical Methods
Statistical MethodsStatistical Methods
Statistical Methods
guest9fa52
 
Lesson 2
Lesson 2Lesson 2
Lesson 2
jwheat
 
03 ch ken black solution
03 ch ken black solution03 ch ken black solution
03 ch ken black solution
Krunal Shah
 
Chapter 3 Numerical Descriptions of Data 75 Chapter 3.docx
Chapter 3 Numerical Descriptions of Data 75 Chapter 3.docxChapter 3 Numerical Descriptions of Data 75 Chapter 3.docx
Chapter 3 Numerical Descriptions of Data 75 Chapter 3.docx
walterl4
 
CABT Math 8 measures of central tendency and dispersion
CABT Math 8   measures of central tendency and dispersionCABT Math 8   measures of central tendency and dispersion
CABT Math 8 measures of central tendency and dispersion
Gilbert Joseph Abueg
 
Talk data sciencemeetup
Talk data sciencemeetupTalk data sciencemeetup
Talk data sciencemeetup
datasciencenl
 

Similar to Ch2.7 Data Measures of Central Tendency (20)

Engineering Data Analysis-ProfCharlton
Engineering Data  Analysis-ProfCharltonEngineering Data  Analysis-ProfCharlton
Engineering Data Analysis-ProfCharlton
 
Quality control tools
Quality control toolsQuality control tools
Quality control tools
 
Sherri Collie
Sherri CollieSherri Collie
Sherri Collie
 
Lesson 6 measures of central tendency
Lesson 6 measures of central tendencyLesson 6 measures of central tendency
Lesson 6 measures of central tendency
 
Z And T Tests
Z And T TestsZ And T Tests
Z And T Tests
 
P5 ungrouped data
P5 ungrouped dataP5 ungrouped data
P5 ungrouped data
 
Grouping data discrete
Grouping data discreteGrouping data discrete
Grouping data discrete
 
Ch17 lab r_verdu103: Entry level statistics exercise (descriptives)
Ch17 lab r_verdu103: Entry level statistics exercise (descriptives)Ch17 lab r_verdu103: Entry level statistics exercise (descriptives)
Ch17 lab r_verdu103: Entry level statistics exercise (descriptives)
 
Math investigation using metacognition
Math investigation using metacognitionMath investigation using metacognition
Math investigation using metacognition
 
9 basic seven tools of quality
9 basic seven tools of quality9 basic seven tools of quality
9 basic seven tools of quality
 
Statistical Methods
Statistical MethodsStatistical Methods
Statistical Methods
 
Statistical Methods
Statistical MethodsStatistical Methods
Statistical Methods
 
Lesson 2
Lesson 2Lesson 2
Lesson 2
 
03 ch ken black solution
03 ch ken black solution03 ch ken black solution
03 ch ken black solution
 
Chapter 3 Numerical Descriptions of Data 75 Chapter 3.docx
Chapter 3 Numerical Descriptions of Data 75 Chapter 3.docxChapter 3 Numerical Descriptions of Data 75 Chapter 3.docx
Chapter 3 Numerical Descriptions of Data 75 Chapter 3.docx
 
CABT Math 8 measures of central tendency and dispersion
CABT Math 8   measures of central tendency and dispersionCABT Math 8   measures of central tendency and dispersion
CABT Math 8 measures of central tendency and dispersion
 
CHAPTER 7.pptx
CHAPTER 7.pptxCHAPTER 7.pptx
CHAPTER 7.pptx
 
Talk data sciencemeetup
Talk data sciencemeetupTalk data sciencemeetup
Talk data sciencemeetup
 
Lecture 1 Descriptives.pptx
Lecture 1 Descriptives.pptxLecture 1 Descriptives.pptx
Lecture 1 Descriptives.pptx
 
First term notes 2020 econs ss2 1
First term notes 2020 econs ss2 1First term notes 2020 econs ss2 1
First term notes 2020 econs ss2 1
 

More from mdicken

Intermediate Algebra: 10.5
Intermediate Algebra: 10.5Intermediate Algebra: 10.5
Intermediate Algebra: 10.5
mdicken
 
Intermediate Algebra: 10.4
Intermediate Algebra: 10.4Intermediate Algebra: 10.4
Intermediate Algebra: 10.4
mdicken
 
Intermediate Algebra: 10.4 10.6 review
Intermediate Algebra: 10.4 10.6 reviewIntermediate Algebra: 10.4 10.6 review
Intermediate Algebra: 10.4 10.6 review
mdicken
 
Intermediate Algebra: 10.3
Intermediate Algebra: 10.3Intermediate Algebra: 10.3
Intermediate Algebra: 10.3
mdicken
 
Intermediate Algebra: 10.2
Intermediate Algebra: 10.2Intermediate Algebra: 10.2
Intermediate Algebra: 10.2
mdicken
 
Intermediate Algebra: 10.1
Intermediate Algebra: 10.1Intermediate Algebra: 10.1
Intermediate Algebra: 10.1
mdicken
 
Intermediate Algebra: Ch. 10.1 10.3
Intermediate Algebra: Ch. 10.1 10.3Intermediate Algebra: Ch. 10.1 10.3
Intermediate Algebra: Ch. 10.1 10.3
mdicken
 
Intermediate Algebra: Presentation Rubric
Intermediate Algebra: Presentation RubricIntermediate Algebra: Presentation Rubric
Intermediate Algebra: Presentation Rubric
mdicken
 
Intermediate Algebra: Ch.9.1 Percents and Probabilities
Intermediate Algebra: Ch.9.1 Percents and ProbabilitiesIntermediate Algebra: Ch.9.1 Percents and Probabilities
Intermediate Algebra: Ch.9.1 Percents and Probabilities
mdicken
 
Ch.14.4 Graph Manipulations
Ch.14.4 Graph ManipulationsCh.14.4 Graph Manipulations
Ch.14.4 Graph Manipulations
mdicken
 
Ch.8.8 Profits
Ch.8.8 ProfitsCh.8.8 Profits
Ch.8.8 Profits
mdicken
 
Ch.8.7 Reflections
Ch.8.7 ReflectionsCh.8.7 Reflections
Ch.8.7 Reflections
mdicken
 
Ch.8.6 Matrix Multiplication
Ch.8.6 Matrix MultiplicationCh.8.6 Matrix Multiplication
Ch.8.6 Matrix Multiplication
mdicken
 
Ch.8.5 Matrix Operations
Ch.8.5 Matrix OperationsCh.8.5 Matrix Operations
Ch.8.5 Matrix Operations
mdicken
 
Ch.14.3 Graphing Functions
Ch.14.3 Graphing FunctionsCh.14.3 Graphing Functions
Ch.14.3 Graphing Functions
mdicken
 
Ch14.2 Book Examples
Ch14.2 Book ExamplesCh14.2 Book Examples
Ch14.2 Book Examples
mdicken
 
Ch.14.1 Trigonometric Functions
Ch.14.1 Trigonometric FunctionsCh.14.1 Trigonometric Functions
Ch.14.1 Trigonometric Functions
mdicken
 
Ch 12 Review Key
Ch 12 Review KeyCh 12 Review Key
Ch 12 Review Key
mdicken
 
Ch.12.5 Quadratic Formula
Ch.12.5 Quadratic FormulaCh.12.5 Quadratic Formula
Ch.12.5 Quadratic Formula
mdicken
 

More from mdicken (20)

Intermediate Algebra: 10.5
Intermediate Algebra: 10.5Intermediate Algebra: 10.5
Intermediate Algebra: 10.5
 
Intermediate Algebra: 10.4
Intermediate Algebra: 10.4Intermediate Algebra: 10.4
Intermediate Algebra: 10.4
 
Intermediate Algebra: 10.4 10.6 review
Intermediate Algebra: 10.4 10.6 reviewIntermediate Algebra: 10.4 10.6 review
Intermediate Algebra: 10.4 10.6 review
 
Intermediate Algebra: 10.3
Intermediate Algebra: 10.3Intermediate Algebra: 10.3
Intermediate Algebra: 10.3
 
Intermediate Algebra: 10.2
Intermediate Algebra: 10.2Intermediate Algebra: 10.2
Intermediate Algebra: 10.2
 
Intermediate Algebra: 10.1
Intermediate Algebra: 10.1Intermediate Algebra: 10.1
Intermediate Algebra: 10.1
 
Intermediate Algebra: Ch. 10.1 10.3
Intermediate Algebra: Ch. 10.1 10.3Intermediate Algebra: Ch. 10.1 10.3
Intermediate Algebra: Ch. 10.1 10.3
 
10.6
10.610.6
10.6
 
Intermediate Algebra: Presentation Rubric
Intermediate Algebra: Presentation RubricIntermediate Algebra: Presentation Rubric
Intermediate Algebra: Presentation Rubric
 
Intermediate Algebra: Ch.9.1 Percents and Probabilities
Intermediate Algebra: Ch.9.1 Percents and ProbabilitiesIntermediate Algebra: Ch.9.1 Percents and Probabilities
Intermediate Algebra: Ch.9.1 Percents and Probabilities
 
Ch.14.4 Graph Manipulations
Ch.14.4 Graph ManipulationsCh.14.4 Graph Manipulations
Ch.14.4 Graph Manipulations
 
Ch.8.8 Profits
Ch.8.8 ProfitsCh.8.8 Profits
Ch.8.8 Profits
 
Ch.8.7 Reflections
Ch.8.7 ReflectionsCh.8.7 Reflections
Ch.8.7 Reflections
 
Ch.8.6 Matrix Multiplication
Ch.8.6 Matrix MultiplicationCh.8.6 Matrix Multiplication
Ch.8.6 Matrix Multiplication
 
Ch.8.5 Matrix Operations
Ch.8.5 Matrix OperationsCh.8.5 Matrix Operations
Ch.8.5 Matrix Operations
 
Ch.14.3 Graphing Functions
Ch.14.3 Graphing FunctionsCh.14.3 Graphing Functions
Ch.14.3 Graphing Functions
 
Ch14.2 Book Examples
Ch14.2 Book ExamplesCh14.2 Book Examples
Ch14.2 Book Examples
 
Ch.14.1 Trigonometric Functions
Ch.14.1 Trigonometric FunctionsCh.14.1 Trigonometric Functions
Ch.14.1 Trigonometric Functions
 
Ch 12 Review Key
Ch 12 Review KeyCh 12 Review Key
Ch 12 Review Key
 
Ch.12.5 Quadratic Formula
Ch.12.5 Quadratic FormulaCh.12.5 Quadratic Formula
Ch.12.5 Quadratic Formula
 

Recently uploaded

Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Safe Software
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Victor Rentea
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
panagenda
 

Recently uploaded (20)

Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
Ransomware_Q4_2023. The report. [EN].pdf
Ransomware_Q4_2023. The report. [EN].pdfRansomware_Q4_2023. The report. [EN].pdf
Ransomware_Q4_2023. The report. [EN].pdf
 
[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf[BuildWithAI] Introduction to Gemini.pdf
[BuildWithAI] Introduction to Gemini.pdf
 
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
Apidays New York 2024 - Passkeys: Developing APIs to enable passwordless auth...
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
ICT role in 21st century education and its challenges
ICT role in 21st century education and its challengesICT role in 21st century education and its challenges
ICT role in 21st century education and its challenges
 
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
 
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
Apidays New York 2024 - APIs in 2030: The Risk of Technological Sleepwalk by ...
 
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
Web Form Automation for Bonterra Impact Management (fka Social Solutions Apri...
 
Cyberprint. Dark Pink Apt Group [EN].pdf
Cyberprint. Dark Pink Apt Group [EN].pdfCyberprint. Dark Pink Apt Group [EN].pdf
Cyberprint. Dark Pink Apt Group [EN].pdf
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
 
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemkeProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
 
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdfRising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
Rising Above_ Dubai Floods and the Fortitude of Dubai International Airport.pdf
 
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
 
Why Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire businessWhy Teams call analytics are critical to your entire business
Why Teams call analytics are critical to your entire business
 
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ..."I see eyes in my soup": How Delivery Hero implemented the safety system for ...
"I see eyes in my soup": How Delivery Hero implemented the safety system for ...
 
AWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of TerraformAWS Community Day CPH - Three problems of Terraform
AWS Community Day CPH - Three problems of Terraform
 

Ch2.7 Data Measures of Central Tendency

  • 1. Ch2.7_DataMeasuresCentralTendency.notebook September 19, 2011 Warm Up y 6 Graph 5 4 3 y < x ­ 4 ­6 ­5 ­4 ­3 ­2 ­1 2 1 0 1 2 3 4 5 6 x ­1 ­2 ­3 ­4 ­5 ­6 Chapter 2.7 Data and Measures of Central Tendency 1
  • 2. Ch2.7_DataMeasuresCentralTendency.notebook September 19, 2011 Vocab Statistics ­ the study of data Population ­ large (huuuuuuuuge) group of data Sample ­ smaller group from the population Frequency Table A way to compile and organize data   # of pets   Frequency   at home   0   1   2   3 or more 2
  • 3. Ch2.7_DataMeasuresCentralTendency.notebook September 19, 2011 Fill in the Frequency Table with the  the given test scores in red   Score Interval   Frequency 90 83 68   90 ­ 100 34 85 76   80 ­ 89 79 89 80   70 ­ 79 77 59 91 81 72 64   60 ­ 69 98 96 62   50 ­ 59   49 or lower Measures of Central Tendency Mean ­ average of data Median ­ middle number (when arranged in order) Mode ­ most frequent value 3
  • 4. Ch2.7_DataMeasuresCentralTendency.notebook September 19, 2011 Example Find the mean, median, and mode of the following fourteen  numbers 4, 6, 2, 8, 9, 4, 6, 4, 5, 7, 5, 8, 3, 2 Mean (average) =    Sum of numbers               14 Median (middle #) =  2, 2, 3, 4, 4, 4, 5, 5, 6, 6, 7, 8, 8, 9 Mode (most common) =  Example Find the mean, median, and mode of the following ACT scores 22, 28, 30, 27, 25, 31, 25, 26, 19 Mean (average) =  Median (middle #) =  Mode (most common) =  4
  • 5. Ch2.7_DataMeasuresCentralTendency.notebook September 19, 2011 Page 84 1 ­ 14 all 5