SlideShare une entreprise Scribd logo
1  sur  18
CENTRAL TENDENCY
By: Marlenys Mora
Period 6
Range
The greatest number subtracted by the lowest number in a set of numbers.
Mean

In a set of data you add all the numbers together and divided by the #
                                 of data.
Median
Middle, thing, person, number ect. In a group. I they are two Middle
numbers add the numbers together and divide them by 2 or by the
                amount of middle numbers you find.
Mode
     The most common number in a data set.




The boys would be the “mode” of this picture because
              they appear the most.
Outliers

The Thing, number, place ect. That is most different from the rest




     The brown, gray and black kitten would be the outlier.
Try it!
Jessica’s test scores in Algebra for the first semester are 93, 79, 88, 77, 92, 88, 80, 34, 84, 88.
 Calculate the range, mean, median, and mode. Then make and explain a prediction for next
                                       semester’s test scores
                              34, 77, 79, 80, 84, 88, 88, 88, 92, 93
                                  Range: 93 - 34= 59
          Mean: 34 + 77 + 79 + 80 + 84 + 88 + 88 + 88 + 92 + 93 = 803/10 = 80.3
                             Median: 84 + 88 = 172/2 = 86
                                       Mode: 88
Group Exercises

Witch Measures of central tendency
 best represents the data? Justify
           your answer.
 Then find all the central tendency
measures and compare the results.
Question 1
1. DEFECTS A furniture manufacturer keeps records of how many units are
   defective each day.(7,12,9,10,14,8))

7, 8, 8, 9, 10, 12, 14

Mean: 11.33
Median:9
Mode:8
Outlier: No outlier
Range:7


How could you verify this decision?

I would use mean because it would be
more accurate and closer to the units
defective each day.
Question 2
2. SCIENCE TEST Mr. Wharton records his students scores on the last science
test(94,88,88,94,84.94.88.84,94)


84, 84, 88, 88, 88, 94, 94, 94, 94, 94

Mean:90.2
Mode:94
Range:10
Outlier: No Outlier
Median:182/2=91

Predict the outcome of the mean and range if there were 2 20’s added to the
science test explain?
Mean: 78.5
Range:74
The mean would be lower b/c of the two 20’s added together it will bring it down.
Question 3
3. PUPPIES A veterinarian keeps records of the weights of puppies in ounces
(4.1,3.8,5.0,5.6,4.7,11.6)

3.8, 4.1, 4.6, 4.7, 5.0, 5.6, 11.6

Mean: 5.62
Median:4.7
Mode: No Mode
Range:7.8
Outlier:11.6

How would you explain the range and its connection to the data set?

Range is the highest # subtracted by the lowest number in the data set. I think the
connection because confirms the accuracy
Question 4
4. COMMUTING The local newspaper conducted a telephone survey of commuters to
    see how the get to work each day. The responses were: commuter rail, 22; bus, 17;
    subway, 18; walking 15; car ,224.

15, 17, 18, 22, 224

Mean:59.2
Median:18
Mode: No Mode
Range:209
Outlier:224

What facts can you gather about the outlier? Which central tendency would be affected
   by
the outlier?
The outlier is the oddest number in a set of data, in other words the one that doesn’t
   belong. Mean
Could be affected by outlier. It would not be accurate.
Question 5
5. SNOWFALL A weather station keeps records of how many inches of snow fall each
week (9,2,0,3,0,2,1,2,3,1).

0, 0, 1, 1, 2, 2, 2, 3, 3, 9

Mean:2.3
Median:2
Mode:2
Range:9
Outlier:9

What would happen to your decision if we had a blizzard and added 24 inches to the
above data.

This would be the Mean:4.27
This would be the Median:2
This would be the Mode:2
This would be theRange:24
This would be theOutlier:24
Question 6
6. SALES a supermarket keeps records of how many boxes of cereal are sold each day
in a week (12,9,11,14,19,49,18)

9, 11, 11, 12, 14, 18, 19, 49

Mean:17.875
Mode:11
Median:13
Range:40
Outlier:49


Based on above information which cereal makes the most money.

I think the outlier makes the most money
Question 7
7. A city councilman keeps tracks of the numbers of votes he receives in each
district(68,66,59,61,62,67)

59, 61, 62, 66, 67, 68

Mean:63.83
Median:64
Mode: No Mode
Range:9
Outlier: No Outlier

If you ran against the city councilman and wanted to beat him what voting numbers
would you want to see.
68 because is the highest
Question 8
8. BODYBUILDING A body builder keeps track of how many sets
of each exercise he performs each day:(9,8,6,5,11,7,10)

5, 6, 7, 8, 9, 10, 11

Mean:8
Median:8
Mode: No Mode
Range:6
Outlier: No Outlier

I think that mean because it would be the most accurate answer
Question 9
9. PROPERTY TAXES A landlord is keeping track of what he pays each month in
property taxes so he can budget accordingly. For the first half of the year, the tax bills
were $256, $256,$274,$256,$256,$274. Which measure of central tendency best
represents the data.


$256,$256,$256,$256,$274,$274=1572/6=262

Mean:262
Median:256
Mode:256
Range:18
Outlier: 274
THE END

Contenu connexe

Tendances

Mean, Mode, Median[1]
Mean, Mode, Median[1]Mean, Mode, Median[1]
Mean, Mode, Median[1]eldamontalvo
 
Mean, median, mode and range
Mean, median, mode and rangeMean, median, mode and range
Mean, median, mode and rangewhernanm
 
Multiplying fractions
Multiplying fractionsMultiplying fractions
Multiplying fractionsNeilfieOrit2
 
Multiplying mixed numbers
Multiplying mixed numbersMultiplying mixed numbers
Multiplying mixed numbersMs. Jones
 
G6 m2-a-lesson 1-s
G6 m2-a-lesson 1-sG6 m2-a-lesson 1-s
G6 m2-a-lesson 1-smlabuski
 
Section 4.6
Section 4.6Section 4.6
Section 4.6bweldon
 
Standard deviation
Standard deviationStandard deviation
Standard deviationKen Plummer
 
Applied Math 40S March 12, 2008
Applied Math 40S March 12, 2008Applied Math 40S March 12, 2008
Applied Math 40S March 12, 2008Darren Kuropatwa
 
G6 m4-g-lesson 22-s
G6 m4-g-lesson 22-sG6 m4-g-lesson 22-s
G6 m4-g-lesson 22-smlabuski
 
Strategic Intervention Material,by rodenette cagara
Strategic Intervention Material,by rodenette cagaraStrategic Intervention Material,by rodenette cagara
Strategic Intervention Material,by rodenette cagaraRodenette Cagara
 
Direct, indirect and partitive proportion
Direct, indirect and partitive proportionDirect, indirect and partitive proportion
Direct, indirect and partitive proportionmhera gabayoyo
 
Chapter 6 — Notecards
Chapter 6 — NotecardsChapter 6 — Notecards
Chapter 6 — Notecardspfoa54263
 
Multiplication on decimals
Multiplication on decimalsMultiplication on decimals
Multiplication on decimalsNeilfieOrit2
 

Tendances (18)

Mean, Mode, Median[1]
Mean, Mode, Median[1]Mean, Mode, Median[1]
Mean, Mode, Median[1]
 
Lesson 35
Lesson 35Lesson 35
Lesson 35
 
8:00 GED Math Week 1 Tue
8:00 GED Math Week 1 Tue8:00 GED Math Week 1 Tue
8:00 GED Math Week 1 Tue
 
Mean, median, mode and range
Mean, median, mode and rangeMean, median, mode and range
Mean, median, mode and range
 
Multiplying fractions
Multiplying fractionsMultiplying fractions
Multiplying fractions
 
Multiplying mixed numbers
Multiplying mixed numbersMultiplying mixed numbers
Multiplying mixed numbers
 
G6 m2-a-lesson 1-s
G6 m2-a-lesson 1-sG6 m2-a-lesson 1-s
G6 m2-a-lesson 1-s
 
Section 4.6
Section 4.6Section 4.6
Section 4.6
 
Standard deviation
Standard deviationStandard deviation
Standard deviation
 
Applied Math 40S March 12, 2008
Applied Math 40S March 12, 2008Applied Math 40S March 12, 2008
Applied Math 40S March 12, 2008
 
Variance
VarianceVariance
Variance
 
G6 m4-g-lesson 22-s
G6 m4-g-lesson 22-sG6 m4-g-lesson 22-s
G6 m4-g-lesson 22-s
 
Strategic Intervention Material,by rodenette cagara
Strategic Intervention Material,by rodenette cagaraStrategic Intervention Material,by rodenette cagara
Strategic Intervention Material,by rodenette cagara
 
Learning short cuts of vedic mathematics
Learning short cuts of vedic mathematicsLearning short cuts of vedic mathematics
Learning short cuts of vedic mathematics
 
Direct, indirect and partitive proportion
Direct, indirect and partitive proportionDirect, indirect and partitive proportion
Direct, indirect and partitive proportion
 
Kinds of proportion
Kinds of proportionKinds of proportion
Kinds of proportion
 
Chapter 6 — Notecards
Chapter 6 — NotecardsChapter 6 — Notecards
Chapter 6 — Notecards
 
Multiplication on decimals
Multiplication on decimalsMultiplication on decimals
Multiplication on decimals
 

Similaire à Central tendency

Algebra unit 9.3
Algebra unit 9.3Algebra unit 9.3
Algebra unit 9.3Mark Ryder
 
DEMO LESSON PLAN.docx
DEMO LESSON PLAN.docxDEMO LESSON PLAN.docx
DEMO LESSON PLAN.docxChinGa7
 
Mean-Median-Mode-Range-Demonstration.pptx
Mean-Median-Mode-Range-Demonstration.pptxMean-Median-Mode-Range-Demonstration.pptx
Mean-Median-Mode-Range-Demonstration.pptxssuserb9172b1
 
Mean-Median-Mode-Range-Demonstration.pptx
Mean-Median-Mode-Range-Demonstration.pptxMean-Median-Mode-Range-Demonstration.pptx
Mean-Median-Mode-Range-Demonstration.pptxkrishan425
 
Measures of Central Tendency Final.ppt
Measures of Central Tendency Final.pptMeasures of Central Tendency Final.ppt
Measures of Central Tendency Final.pptAdamManlunas
 
Measures of Central Tendency Final.ppt
Measures of Central Tendency Final.pptMeasures of Central Tendency Final.ppt
Measures of Central Tendency Final.pptAdamRayManlunas1
 
Statistics and inferences review - bootcamp
Statistics and inferences review  - bootcampStatistics and inferences review  - bootcamp
Statistics and inferences review - bootcamparinedge
 
Module 3 statistics
Module 3   statisticsModule 3   statistics
Module 3 statisticsdionesioable
 
Measures of central tendency by maria diza c. febrio
Measures of central tendency by maria diza c. febrioMeasures of central tendency by maria diza c. febrio
Measures of central tendency by maria diza c. febriomariadiza
 
Basic Mean median mode Standard Deviation
Basic Mean median mode Standard DeviationBasic Mean median mode Standard Deviation
Basic Mean median mode Standard DeviationJovendin Leonardo
 
Dr digs central tendency
Dr digs central tendencyDr digs central tendency
Dr digs central tendencydrdig
 
Applied 40S March 20, 2009
Applied 40S March 20, 2009Applied 40S March 20, 2009
Applied 40S March 20, 2009Darren Kuropatwa
 
Mathematics 7th grade and cambridge curricilum.pptx
Mathematics 7th grade and cambridge curricilum.pptxMathematics 7th grade and cambridge curricilum.pptx
Mathematics 7th grade and cambridge curricilum.pptxLamiyQurbanlKomptert
 
3. Mean__Median__Mode__Range.ppt
3. Mean__Median__Mode__Range.ppt3. Mean__Median__Mode__Range.ppt
3. Mean__Median__Mode__Range.pptABDULRAUF411
 
Mean__Median__Mode__Range.ppt
Mean__Median__Mode__Range.pptMean__Median__Mode__Range.ppt
Mean__Median__Mode__Range.ppttrader33
 

Similaire à Central tendency (20)

Algebra unit 9.3
Algebra unit 9.3Algebra unit 9.3
Algebra unit 9.3
 
DEMO LESSON PLAN.docx
DEMO LESSON PLAN.docxDEMO LESSON PLAN.docx
DEMO LESSON PLAN.docx
 
MEAN MEDIAN MODE.ppt
MEAN MEDIAN MODE.pptMEAN MEDIAN MODE.ppt
MEAN MEDIAN MODE.ppt
 
Mean-Median-Mode-Range-Demonstration.pptx
Mean-Median-Mode-Range-Demonstration.pptxMean-Median-Mode-Range-Demonstration.pptx
Mean-Median-Mode-Range-Demonstration.pptx
 
Mean-Median-Mode-Range-Demonstration.pptx
Mean-Median-Mode-Range-Demonstration.pptxMean-Median-Mode-Range-Demonstration.pptx
Mean-Median-Mode-Range-Demonstration.pptx
 
Measures of Central Tendency Final.ppt
Measures of Central Tendency Final.pptMeasures of Central Tendency Final.ppt
Measures of Central Tendency Final.ppt
 
Measures of Central Tendency Final.ppt
Measures of Central Tendency Final.pptMeasures of Central Tendency Final.ppt
Measures of Central Tendency Final.ppt
 
Statistics and inferences review - bootcamp
Statistics and inferences review  - bootcampStatistics and inferences review  - bootcamp
Statistics and inferences review - bootcamp
 
presentation2.pptx
presentation2.pptxpresentation2.pptx
presentation2.pptx
 
Module 3 statistics
Module 3   statisticsModule 3   statistics
Module 3 statistics
 
MATH DEMO.pptx
MATH DEMO.pptxMATH DEMO.pptx
MATH DEMO.pptx
 
Measures of central tendency by maria diza c. febrio
Measures of central tendency by maria diza c. febrioMeasures of central tendency by maria diza c. febrio
Measures of central tendency by maria diza c. febrio
 
Basic Mean median mode Standard Deviation
Basic Mean median mode Standard DeviationBasic Mean median mode Standard Deviation
Basic Mean median mode Standard Deviation
 
Dr digs central tendency
Dr digs central tendencyDr digs central tendency
Dr digs central tendency
 
Applied 40S March 20, 2009
Applied 40S March 20, 2009Applied 40S March 20, 2009
Applied 40S March 20, 2009
 
MeanMedianMode.ppt
MeanMedianMode.pptMeanMedianMode.ppt
MeanMedianMode.ppt
 
Mathematics 7th grade and cambridge curricilum.pptx
Mathematics 7th grade and cambridge curricilum.pptxMathematics 7th grade and cambridge curricilum.pptx
Mathematics 7th grade and cambridge curricilum.pptx
 
Chapter 3.2
Chapter 3.2Chapter 3.2
Chapter 3.2
 
3. Mean__Median__Mode__Range.ppt
3. Mean__Median__Mode__Range.ppt3. Mean__Median__Mode__Range.ppt
3. Mean__Median__Mode__Range.ppt
 
Mean__Median__Mode__Range.ppt
Mean__Median__Mode__Range.pptMean__Median__Mode__Range.ppt
Mean__Median__Mode__Range.ppt
 

Dernier

Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubKalema Edgar
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Manik S Magar
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsSergiu Bodiu
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyAlfredo García Lavilla
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .Alan Dix
 
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks..."LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...Fwdays
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 3652toLead Limited
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 
Powerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time ClashPowerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time Clashcharlottematthew16
 
Scanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsScanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsRizwan Syed
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfPrecisely
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Commit University
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024Stephanie Beckett
 
Search Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfSearch Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfRankYa
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brandgvaughan
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Mark Simos
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.Curtis Poe
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Scott Keck-Warren
 

Dernier (20)

Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding Club
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platforms
 
Commit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easyCommit 2024 - Secret Management made easy
Commit 2024 - Secret Management made easy
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
 
From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .
 
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks..."LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
 
Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365Ensuring Technical Readiness For Copilot in Microsoft 365
Ensuring Technical Readiness For Copilot in Microsoft 365
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 
Powerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time ClashPowerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time Clash
 
Scanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL CertsScanning the Internet for External Cloud Exposures via SSL Certs
Scanning the Internet for External Cloud Exposures via SSL Certs
 
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdfHyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
Hyperautomation and AI/ML: A Strategy for Digital Transformation Success.pdf
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!
 
What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024What's New in Teams Calling, Meetings and Devices March 2024
What's New in Teams Calling, Meetings and Devices March 2024
 
Search Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdfSearch Engine Optimization SEO PDF for 2024.pdf
Search Engine Optimization SEO PDF for 2024.pdf
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brand
 
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data PrivacyTrustArc Webinar - How to Build Consumer Trust Through Data Privacy
TrustArc Webinar - How to Build Consumer Trust Through Data Privacy
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.
 
Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024Advanced Test Driven-Development @ php[tek] 2024
Advanced Test Driven-Development @ php[tek] 2024
 

Central tendency

  • 2. Range The greatest number subtracted by the lowest number in a set of numbers.
  • 3. Mean In a set of data you add all the numbers together and divided by the # of data.
  • 4. Median Middle, thing, person, number ect. In a group. I they are two Middle numbers add the numbers together and divide them by 2 or by the amount of middle numbers you find.
  • 5. Mode The most common number in a data set. The boys would be the “mode” of this picture because they appear the most.
  • 6. Outliers The Thing, number, place ect. That is most different from the rest The brown, gray and black kitten would be the outlier.
  • 7. Try it! Jessica’s test scores in Algebra for the first semester are 93, 79, 88, 77, 92, 88, 80, 34, 84, 88. Calculate the range, mean, median, and mode. Then make and explain a prediction for next semester’s test scores 34, 77, 79, 80, 84, 88, 88, 88, 92, 93 Range: 93 - 34= 59 Mean: 34 + 77 + 79 + 80 + 84 + 88 + 88 + 88 + 92 + 93 = 803/10 = 80.3 Median: 84 + 88 = 172/2 = 86 Mode: 88
  • 8. Group Exercises Witch Measures of central tendency best represents the data? Justify your answer. Then find all the central tendency measures and compare the results.
  • 9. Question 1 1. DEFECTS A furniture manufacturer keeps records of how many units are defective each day.(7,12,9,10,14,8)) 7, 8, 8, 9, 10, 12, 14 Mean: 11.33 Median:9 Mode:8 Outlier: No outlier Range:7 How could you verify this decision? I would use mean because it would be more accurate and closer to the units defective each day.
  • 10. Question 2 2. SCIENCE TEST Mr. Wharton records his students scores on the last science test(94,88,88,94,84.94.88.84,94) 84, 84, 88, 88, 88, 94, 94, 94, 94, 94 Mean:90.2 Mode:94 Range:10 Outlier: No Outlier Median:182/2=91 Predict the outcome of the mean and range if there were 2 20’s added to the science test explain? Mean: 78.5 Range:74 The mean would be lower b/c of the two 20’s added together it will bring it down.
  • 11. Question 3 3. PUPPIES A veterinarian keeps records of the weights of puppies in ounces (4.1,3.8,5.0,5.6,4.7,11.6) 3.8, 4.1, 4.6, 4.7, 5.0, 5.6, 11.6 Mean: 5.62 Median:4.7 Mode: No Mode Range:7.8 Outlier:11.6 How would you explain the range and its connection to the data set? Range is the highest # subtracted by the lowest number in the data set. I think the connection because confirms the accuracy
  • 12. Question 4 4. COMMUTING The local newspaper conducted a telephone survey of commuters to see how the get to work each day. The responses were: commuter rail, 22; bus, 17; subway, 18; walking 15; car ,224. 15, 17, 18, 22, 224 Mean:59.2 Median:18 Mode: No Mode Range:209 Outlier:224 What facts can you gather about the outlier? Which central tendency would be affected by the outlier? The outlier is the oddest number in a set of data, in other words the one that doesn’t belong. Mean Could be affected by outlier. It would not be accurate.
  • 13. Question 5 5. SNOWFALL A weather station keeps records of how many inches of snow fall each week (9,2,0,3,0,2,1,2,3,1). 0, 0, 1, 1, 2, 2, 2, 3, 3, 9 Mean:2.3 Median:2 Mode:2 Range:9 Outlier:9 What would happen to your decision if we had a blizzard and added 24 inches to the above data. This would be the Mean:4.27 This would be the Median:2 This would be the Mode:2 This would be theRange:24 This would be theOutlier:24
  • 14. Question 6 6. SALES a supermarket keeps records of how many boxes of cereal are sold each day in a week (12,9,11,14,19,49,18) 9, 11, 11, 12, 14, 18, 19, 49 Mean:17.875 Mode:11 Median:13 Range:40 Outlier:49 Based on above information which cereal makes the most money. I think the outlier makes the most money
  • 15. Question 7 7. A city councilman keeps tracks of the numbers of votes he receives in each district(68,66,59,61,62,67) 59, 61, 62, 66, 67, 68 Mean:63.83 Median:64 Mode: No Mode Range:9 Outlier: No Outlier If you ran against the city councilman and wanted to beat him what voting numbers would you want to see. 68 because is the highest
  • 16. Question 8 8. BODYBUILDING A body builder keeps track of how many sets of each exercise he performs each day:(9,8,6,5,11,7,10) 5, 6, 7, 8, 9, 10, 11 Mean:8 Median:8 Mode: No Mode Range:6 Outlier: No Outlier I think that mean because it would be the most accurate answer
  • 17. Question 9 9. PROPERTY TAXES A landlord is keeping track of what he pays each month in property taxes so he can budget accordingly. For the first half of the year, the tax bills were $256, $256,$274,$256,$256,$274. Which measure of central tendency best represents the data. $256,$256,$256,$256,$274,$274=1572/6=262 Mean:262 Median:256 Mode:256 Range:18 Outlier: 274