SlideShare a Scribd company logo
1 of 24
Basic Descriptive
    Statistics
       Mr. Siko
    Clarkston HS
Why?
 Descriptive statistics do just that:
  Describe Data!
 What we’ll cover in this slidecast
    – Mean (average)
    – Median
    – Mode
    – Range
Mean
 Fancy Formula        What this means: add
µ = ΣX/N                up all your data, then
                        divide by the number
                        of data points
Mean
Sample data:     How to calculate:
98cm
76cm             98+76+82+54+90 =
82cm               400cm
54cm
90cm             400cm/5 = 80cm
Median
 The median is the middle data point in a
  set
 To determine the median, sort the data
  from smallest to largest and find the
  middle data point
Median
Sample data:      Rearranged Data:
98cm              54cm
76cm              76cm
82cm              82cm
54cm              90cm
90cm              98cm
Median
 If there is an even number of data, there
  will be two middle points.
 To find the median, take the average of
  those two data.
Median
Sample Data:      Rearranged Data:
4ml               2ml
8ml               4ml
12ml              8ml
2ml               12ml

                   4 + 8 = 12ml
                   12/2 = 6ml
Mode
 The mode is the most frequently occurring
  data point.
 To find the mode, arrange the data from
  smallest to largest, and then determine
  which amount occurs most often.
Mode
Sample Data:     Rearranged Data:
20g 23g          20g 20g 20g
30g 30g          22g
22g 27g          23g 23g 23g 23g
25g 20g          24g
23g 24g          25g 25g
23g 25g          27g
20g 23g          30g 30g
Range
 The range is the distance between the
  smallest and largest data point.
 To calculate, determine the smallest data
  point and the largest data point, then
  subtract the smallest from the largest.
Range
Sample data:     Rearranged Data:
98cm             54cm
76cm             76cm
82cm             82cm
54cm             90cm
90cm             98cm


                 98cm – 54cm = 44cm
Recap
   Mean, Median, Mode, and Range
    “describe” the data.
Acknowledgements
American Chemical Society. (2006).
 Chemistry in the community: ChemCom
 (5th ed). New York: W.H. Freeman
Basic Descriptive Statistics
Basic Descriptive Statistics
Basic Descriptive Statistics
Basic Descriptive Statistics
Basic Descriptive Statistics
Basic Descriptive Statistics
Basic Descriptive Statistics
Basic Descriptive Statistics
Basic Descriptive Statistics
Basic Descriptive Statistics

More Related Content

What's hot

Descriptive statistics
Descriptive statisticsDescriptive statistics
Descriptive statistics
kemdoby
 
Statistical inference
Statistical inferenceStatistical inference
Statistical inference
Jags Jagdish
 

What's hot (20)

Descriptive statistics
Descriptive statisticsDescriptive statistics
Descriptive statistics
 
Descriptive Statistics
Descriptive StatisticsDescriptive Statistics
Descriptive Statistics
 
Descriptive statistics
Descriptive statisticsDescriptive statistics
Descriptive statistics
 
Descriptive Statistics
Descriptive StatisticsDescriptive Statistics
Descriptive Statistics
 
Data Analysis and Statistics
Data Analysis and StatisticsData Analysis and Statistics
Data Analysis and Statistics
 
Concept of Inferential statistics
Concept of Inferential statisticsConcept of Inferential statistics
Concept of Inferential statistics
 
Basic statistics
Basic statisticsBasic statistics
Basic statistics
 
Measures of central tendancy
Measures of central tendancy Measures of central tendancy
Measures of central tendancy
 
Introduction to statistics
Introduction to statisticsIntroduction to statistics
Introduction to statistics
 
1.2 types of data
1.2 types of data1.2 types of data
1.2 types of data
 
Descriptive statistics
Descriptive statisticsDescriptive statistics
Descriptive statistics
 
Descriptive statistics
Descriptive statisticsDescriptive statistics
Descriptive statistics
 
Descriptive statistics -review(2)
Descriptive statistics -review(2)Descriptive statistics -review(2)
Descriptive statistics -review(2)
 
Statistical inference
Statistical inferenceStatistical inference
Statistical inference
 
Descriptive statistics
Descriptive statisticsDescriptive statistics
Descriptive statistics
 
Basic Statistics in 1 hour.pptx
Basic Statistics in 1 hour.pptxBasic Statistics in 1 hour.pptx
Basic Statistics in 1 hour.pptx
 
Choosing a statistical test
Choosing a statistical testChoosing a statistical test
Choosing a statistical test
 
Descriptive statistics
Descriptive statisticsDescriptive statistics
Descriptive statistics
 
Introduction to Descriptive Statistics
Introduction to Descriptive StatisticsIntroduction to Descriptive Statistics
Introduction to Descriptive Statistics
 
Introduction to Statistics
Introduction to StatisticsIntroduction to Statistics
Introduction to Statistics
 

Similar to Basic Descriptive Statistics

Chapter 3 260110 044503
Chapter 3 260110 044503Chapter 3 260110 044503
Chapter 3 260110 044503
guest25d353
 
Statistical Methods
Statistical MethodsStatistical Methods
Statistical Methods
guest2137aa
 
Statistical Methods
Statistical MethodsStatistical Methods
Statistical Methods
guest9fa52
 
3.1-3.2 Measures of Central Tendency
3.1-3.2 Measures of Central Tendency3.1-3.2 Measures of Central Tendency
3.1-3.2 Measures of Central Tendency
mlong24
 
Dr digs central tendency
Dr digs central tendencyDr digs central tendency
Dr digs central tendency
drdig
 
A General Manger of Harley-Davidson has to decide on the size of a.docx
A General Manger of Harley-Davidson has to decide on the size of a.docxA General Manger of Harley-Davidson has to decide on the size of a.docx
A General Manger of Harley-Davidson has to decide on the size of a.docx
evonnehoggarth79783
 

Similar to Basic Descriptive Statistics (20)

Measures of Central Tendency.ppt
Measures of Central Tendency.pptMeasures of Central Tendency.ppt
Measures of Central Tendency.ppt
 
Statistics
StatisticsStatistics
Statistics
 
Chapter 3 260110 044503
Chapter 3 260110 044503Chapter 3 260110 044503
Chapter 3 260110 044503
 
Lect 3 background mathematics for Data Mining
Lect 3 background mathematics for Data MiningLect 3 background mathematics for Data Mining
Lect 3 background mathematics for Data Mining
 
Lect 3 background mathematics
Lect 3 background mathematicsLect 3 background mathematics
Lect 3 background mathematics
 
Basics of Stats (2).pptx
Basics of Stats (2).pptxBasics of Stats (2).pptx
Basics of Stats (2).pptx
 
Algebra unit 9.3
Algebra unit 9.3Algebra unit 9.3
Algebra unit 9.3
 
Statistics for 6 Sigma.pptx
Statistics for 6 Sigma.pptxStatistics for 6 Sigma.pptx
Statistics for 6 Sigma.pptx
 
Central Tendency.pptx
Central Tendency.pptxCentral Tendency.pptx
Central Tendency.pptx
 
Lecture 3 & 4 Measure of Central Tendency.pdf
Lecture 3 & 4 Measure of Central Tendency.pdfLecture 3 & 4 Measure of Central Tendency.pdf
Lecture 3 & 4 Measure of Central Tendency.pdf
 
Statistical Methods
Statistical MethodsStatistical Methods
Statistical Methods
 
Statistical Methods
Statistical MethodsStatistical Methods
Statistical Methods
 
3.1-3.2 Measures of Central Tendency
3.1-3.2 Measures of Central Tendency3.1-3.2 Measures of Central Tendency
3.1-3.2 Measures of Central Tendency
 
Dr digs central tendency
Dr digs central tendencyDr digs central tendency
Dr digs central tendency
 
A General Manger of Harley-Davidson has to decide on the size of a.docx
A General Manger of Harley-Davidson has to decide on the size of a.docxA General Manger of Harley-Davidson has to decide on the size of a.docx
A General Manger of Harley-Davidson has to decide on the size of a.docx
 
classroom 2.pptx
classroom 2.pptxclassroom 2.pptx
classroom 2.pptx
 
BA 3 Statistics.ppt
BA 3 Statistics.pptBA 3 Statistics.ppt
BA 3 Statistics.ppt
 
analytical representation of data
 analytical representation of data analytical representation of data
analytical representation of data
 
1.0 Descriptive statistics.pdf
1.0 Descriptive statistics.pdf1.0 Descriptive statistics.pdf
1.0 Descriptive statistics.pdf
 
Normal Distribution
Normal DistributionNormal Distribution
Normal Distribution
 

More from sikojp

Site2013-"Badgering" Preservice Teacher into Learning Issues and Trends in Te...
Site2013-"Badgering" Preservice Teacher into Learning Issues and Trends in Te...Site2013-"Badgering" Preservice Teacher into Learning Issues and Trends in Te...
Site2013-"Badgering" Preservice Teacher into Learning Issues and Trends in Te...
sikojp
 
Populations
PopulationsPopulations
Populations
sikojp
 
Evolution
EvolutionEvolution
Evolution
sikojp
 
Energy flow
Energy flowEnergy flow
Energy flow
sikojp
 
Classification
ClassificationClassification
Classification
sikojp
 
Biomes
BiomesBiomes
Biomes
sikojp
 
Succession
SuccessionSuccession
Succession
sikojp
 

More from sikojp (20)

Aligning Goals and Evaluations MEMSPA2014
Aligning Goals and Evaluations MEMSPA2014Aligning Goals and Evaluations MEMSPA2014
Aligning Goals and Evaluations MEMSPA2014
 
CCS-CMU PD
CCS-CMU PDCCS-CMU PD
CCS-CMU PD
 
IB Biology Blended
IB Biology BlendedIB Biology Blended
IB Biology Blended
 
PowerPoint for Formative Assessment and Game Design
PowerPoint for Formative Assessment and Game DesignPowerPoint for Formative Assessment and Game Design
PowerPoint for Formative Assessment and Game Design
 
Technology for Feedback and Formative Assessment
Technology for Feedback and Formative AssessmentTechnology for Feedback and Formative Assessment
Technology for Feedback and Formative Assessment
 
AECT2012-Design-based research on the use of homemade PowerPoint games
AECT2012-Design-based research on the use of homemade PowerPoint gamesAECT2012-Design-based research on the use of homemade PowerPoint games
AECT2012-Design-based research on the use of homemade PowerPoint games
 
AERA2013-Refining the use of homemade PowerPoint Games in a secondary science...
AERA2013-Refining the use of homemade PowerPoint Games in a secondary science...AERA2013-Refining the use of homemade PowerPoint Games in a secondary science...
AERA2013-Refining the use of homemade PowerPoint Games in a secondary science...
 
SITE2014-Blended Learning from the Perspective of Parents and Students
SITE2014-Blended Learning from the Perspective of Parents and StudentsSITE2014-Blended Learning from the Perspective of Parents and Students
SITE2014-Blended Learning from the Perspective of Parents and Students
 
AERA2014-Parent and Student Perceptions of a Blended Learning Experience
AERA2014-Parent and Student Perceptions of a Blended Learning ExperienceAERA2014-Parent and Student Perceptions of a Blended Learning Experience
AERA2014-Parent and Student Perceptions of a Blended Learning Experience
 
Site2013-"Badgering" Preservice Teacher into Learning Issues and Trends in Te...
Site2013-"Badgering" Preservice Teacher into Learning Issues and Trends in Te...Site2013-"Badgering" Preservice Teacher into Learning Issues and Trends in Te...
Site2013-"Badgering" Preservice Teacher into Learning Issues and Trends in Te...
 
SITE 2014 - Applying the ESPRI to K-12 Blended Learning
SITE 2014 - Applying the ESPRI to K-12 Blended LearningSITE 2014 - Applying the ESPRI to K-12 Blended Learning
SITE 2014 - Applying the ESPRI to K-12 Blended Learning
 
IT6230 - Generation Unit Summary
IT6230 - Generation Unit SummaryIT6230 - Generation Unit Summary
IT6230 - Generation Unit Summary
 
IT6230 - Generation Unit Intro
IT6230 - Generation Unit IntroIT6230 - Generation Unit Intro
IT6230 - Generation Unit Intro
 
Using PowerPoint as a game design tool in science education.
Using PowerPoint as a game design tool in science education. Using PowerPoint as a game design tool in science education.
Using PowerPoint as a game design tool in science education.
 
Populations
PopulationsPopulations
Populations
 
Evolution
EvolutionEvolution
Evolution
 
Energy flow
Energy flowEnergy flow
Energy flow
 
Classification
ClassificationClassification
Classification
 
Biomes
BiomesBiomes
Biomes
 
Succession
SuccessionSuccession
Succession
 

Recently uploaded

+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
?#DUbAI#??##{{(☎️+971_581248768%)**%*]'#abortion pills for sale in dubai@
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Victor Rentea
 
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
 
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
 

Recently uploaded (20)

+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
+971581248768>> SAFE AND ORIGINAL ABORTION PILLS FOR SALE IN DUBAI AND ABUDHA...
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
 
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
 
MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024MINDCTI Revenue Release Quarter One 2024
MINDCTI Revenue Release Quarter One 2024
 
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
Navigating the Deluge_ Dubai Floods and the Resilience of Dubai International...
 
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
 
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
Modular Monolith - a Practical Alternative to Microservices @ Devoxx UK 2024
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
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
 
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...
 
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
 
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
 
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
 
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
 
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
 
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
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
 

Basic Descriptive Statistics

  • 1. Basic Descriptive Statistics Mr. Siko Clarkston HS
  • 2. Why?  Descriptive statistics do just that: Describe Data!  What we’ll cover in this slidecast – Mean (average) – Median – Mode – Range
  • 3. Mean  Fancy Formula  What this means: add µ = ΣX/N up all your data, then divide by the number of data points
  • 4. Mean Sample data: How to calculate: 98cm 76cm 98+76+82+54+90 = 82cm 400cm 54cm 90cm 400cm/5 = 80cm
  • 5. Median  The median is the middle data point in a set  To determine the median, sort the data from smallest to largest and find the middle data point
  • 6. Median Sample data: Rearranged Data: 98cm 54cm 76cm 76cm 82cm 82cm 54cm 90cm 90cm 98cm
  • 7. Median  If there is an even number of data, there will be two middle points.  To find the median, take the average of those two data.
  • 8. Median Sample Data: Rearranged Data: 4ml 2ml 8ml 4ml 12ml 8ml 2ml 12ml 4 + 8 = 12ml 12/2 = 6ml
  • 9. Mode  The mode is the most frequently occurring data point.  To find the mode, arrange the data from smallest to largest, and then determine which amount occurs most often.
  • 10. Mode Sample Data: Rearranged Data: 20g 23g 20g 20g 20g 30g 30g 22g 22g 27g 23g 23g 23g 23g 25g 20g 24g 23g 24g 25g 25g 23g 25g 27g 20g 23g 30g 30g
  • 11. Range  The range is the distance between the smallest and largest data point.  To calculate, determine the smallest data point and the largest data point, then subtract the smallest from the largest.
  • 12. Range Sample data: Rearranged Data: 98cm 54cm 76cm 76cm 82cm 82cm 54cm 90cm 90cm 98cm 98cm – 54cm = 44cm
  • 13. Recap  Mean, Median, Mode, and Range “describe” the data.
  • 14. Acknowledgements American Chemical Society. (2006). Chemistry in the community: ChemCom (5th ed). New York: W.H. Freeman