SlideShare une entreprise Scribd logo
1  sur  16
Télécharger pour lire hors ligne
Introduction to Statistics




          Queen Victoria, Cunard's newest cruise ship by savannahgrandfather
Why Study Statistics?

    • Two students in two different schools each have marks of 95 percent.
    Which student should receive an award for getting the 'higher' mark?




       • How do doctors decide that teenagers should or should not get
       hepatitis vaccine?



   • Judith and Francine, both age 19, have decided to go on a Caribbean
   cruise, and they want to have an enjoyable time, which means that they
   want to travel with other people their own age. They buy tickets for a
   cruise where the average age of the other passengers is 20 years. Sounds
   like fun, no?
Can you imagine their surprise at the start
of the cruise when they discover that all the
other passengers are parents (average age
32) with children (average age 8)?




     big_girl_04_m1_screen by pntphoto
Statistics: the branch of mathematics that deals with collecting, organizing,
displaying, and analyzing data.

statistic: a number that describes one aspect of a group of data.
EXAMPLE: mean, median, mode, range, standard deviation, etc...

datum: one bit (piece) of information.

data: many bits (pieces) of information.

Types of Data
quantitative data: data that is numeric
(eg. height, weight, time..)

         There are two kinds of quantitative data: continuous and discrete

   continuous data: can be represented using real numbers (eg. height, weight,
time, etc..)

   discrete data: can be represented by using ONLY intergers (eg. # of people, #
of cars, # of animals, etc..)

qualitative data: data that is non-numeric (eg. colours, flavours, etc...)
Measures of Central Tendency

mean: ( A.K.A. 'the arithmetic meanquot;) the symbol for mean is quot;x barquot;. The arithmetic average
of a set of values.



where x is the mean
where Σx means the sum of all data (x) in the set (Σ is called quot;sigmaquot;)
where n is the number of data in a set

EXAMPLE: find the average mark this set of 5 quizzes: 48,52,65,45,65.
Measures of Central Tendency

median (med): 1) the middle value in an ordered (from smallest to largest) set of data.
              2) if there are an even number of data, the median is the average of the
              middle pair in an ordered set of data.

EXAMPLE: find the median of these quiz scores: 12,10,17,11,15

SOLUTION: 10, 11, 12, 15, 17

12 is the median.

EXAMPLE: find the median of these scores: 12,10,17,11,15,11

SOLUTION: 10,11,11,12,15,17

the median is 11.5

mode (mo): the datum that occures most frequently in a set of data.

EXAMPLE: find the mode in the set of quiz scores: 12,10,17,11,15,11

SOLUTION: the mode is 11 because it occurs more often that any other number in the set.
Mean, Median, Mode, ...

    A clerk in a men's clothing store keeps a weekly record of the number of pairs of
    pants sold. The following is her list for two weeks.
                             Mon Tue        Wed Thur Fri         Sat
                      Week1 34   40         36  36   38          38
                      Week 2 32  36         36  42   34          34
    Calculate the mean, mode, and median for the data shown.




                                     Bimodal Distribution
Measures of Dispersion (Variability)

Dave can drive to work using the downtown route or the perimeter route. The
downtown route is shorter, but it has more traffic, and can become quite
crowded. The driving times in minutes for each route (arranged in ascending
order) for 5 days are shown on the table below.

               Downtown Route 15        26   30   39   45
               Perimeter Route 29       30   31   32   33

 The average driving time for each route is 31 minutes. Which route
 should he take?
Measures of Dispersion (Variability)
determine how quot;spread outquot; or variedquot; a set of data is.

Range: the difference between the largest and smallest value in a set of data.

EXAMPLE: find the range of ages of people in our class



highest value:
lowest value:
RANGE:

with teacher MR K. 40 yrs old.
highest value: 40
lowest value:
RANGE:
Measures of Dispersion (Variability)

Back to our example:
Dave can drive to work using the downtown route or the perimeter route. The
downtown route is shorter, but it has more traffic, and can become quite
crowded. The driving times in minutes for each route (arranged in ascending
order) for 5 days are shown on the table below.

                Downtown Route 15         26   30    39   45
                Perimeter Route 29        30   31    32   33

 Find the range associated with taking each route.

    Downtown Route                             Perimeter Route
Measures of Dispersion (Variability)
determine how quot;spread outquot; or variedquot; a set of data is.

Standard Deviation (σ): a measure that shows how the data are spread about the
mean value. Every value in the data set is used in calculating the standard
deviation.
 Find the standard deviation associated with taking each route to Dave's work
 using your calculator.
                   Downtown Route       15    26    30    39      45
                   Perimeter Route      29    30    31    32      33

    Downtown Route                              Perimeter Route
Measures of Dispersion (Variability)
determine how quot;spread outquot; or variedquot; a set of data is.

Standard Deviation (σ): How is the standard deviation calculated numerically?




                  μ
Let's apply what we've learned ...       HOMEWORK
  The mean math marks and standard deviation for two classes are shown
  below. Assume that 68 percent of the marks in each class are within one
  standard deviation of the mean mark.

                        mean mark (μ)       standard deviation (σ)
             Class A        74                       4
             Class B        72                       8

 (a) In which class is the set of marks more dispersed?




 (b) Bert in Class A and Beth in Class B each have a mark of 82%. How many
 standard deviations are they from their class means? Who appears to have the
 better mark?
HOMEWORK
The following numbers represent the number of cars sold by Metro Motors in one
week:

    Monday       Tuesday     Wednesday      Thursday      Friday     Saturday
       4             5           8             9             7           9

 1. Determine the following statistics:

      (a) mean             (b) mode         (c) median           (d) range




 2. Which measure of central tendency may be the least significant? Explain.
HOMEWORK
The two sets of data show the weights of potatoes in bags. There are six bags in
each set.

                   Set #1    49   51   48   52    47   53
                   Set #2    40   60   45   55    35   65

The mean weight of each set of bags is 50 pounds. Which set has the greater
standard deviation? How do you know? (Do not do any calculations.)
HOMEWORK                                 78   92   62   52   65   59
A class of 30 students received the following
marks in a mathematics examination. Calculate      53   63   68   73   71   63
the mean, median, range, and standard deviation.   69   74   73   81   55   71
                                                   75   81   84   77   80   75
                                                   41   57   91   62   65   49

Contenu connexe

Tendances

lesson 4 measures of central tendency copy
lesson 4 measures of central tendency   copylesson 4 measures of central tendency   copy
lesson 4 measures of central tendency copyNerz Baldres
 
Creating frequency distribution table, histograms and polygons using excel an...
Creating frequency distribution table, histograms and polygons using excel an...Creating frequency distribution table, histograms and polygons using excel an...
Creating frequency distribution table, histograms and polygons using excel an...Sandra Nicks
 
Excel tutorial for frequency distribution
Excel tutorial for frequency distributionExcel tutorial for frequency distribution
Excel tutorial for frequency distributionS.c. Chopra
 
Central tendency
Central tendencyCentral tendency
Central tendencyvnkatare
 
Ppt central tendency measures
Ppt central tendency measuresPpt central tendency measures
Ppt central tendency measuresMtMt37
 
Mathematics Project Slides
Mathematics Project SlidesMathematics Project Slides
Mathematics Project Slidesmkulawat
 
frequency distribution table
frequency distribution tablefrequency distribution table
frequency distribution tableMonie Ali
 
Algebra unit 9.3
Algebra unit 9.3Algebra unit 9.3
Algebra unit 9.3Mark Ryder
 
Mean, Median, Mode: Measures of Central Tendency
Mean, Median, Mode: Measures of Central Tendency Mean, Median, Mode: Measures of Central Tendency
Mean, Median, Mode: Measures of Central Tendency Jan Nah
 
Module 2 statistics
Module 2   statisticsModule 2   statistics
Module 2 statisticsdionesioable
 
Frequency Distributions
Frequency DistributionsFrequency Distributions
Frequency Distributionsjasondroesch
 
Algebra unit 9.2
Algebra unit 9.2Algebra unit 9.2
Algebra unit 9.2Mark Ryder
 

Tendances (20)

Probability module 1
Probability module 1Probability module 1
Probability module 1
 
Variance
VarianceVariance
Variance
 
lesson 4 measures of central tendency copy
lesson 4 measures of central tendency   copylesson 4 measures of central tendency   copy
lesson 4 measures of central tendency copy
 
Mode
ModeMode
Mode
 
Creating frequency distribution table, histograms and polygons using excel an...
Creating frequency distribution table, histograms and polygons using excel an...Creating frequency distribution table, histograms and polygons using excel an...
Creating frequency distribution table, histograms and polygons using excel an...
 
Excel tutorial for frequency distribution
Excel tutorial for frequency distributionExcel tutorial for frequency distribution
Excel tutorial for frequency distribution
 
Psychological Statistics Chapter 2
Psychological Statistics Chapter 2Psychological Statistics Chapter 2
Psychological Statistics Chapter 2
 
Module 2 statistics
Module 2   statisticsModule 2   statistics
Module 2 statistics
 
Central tendency
Central tendencyCentral tendency
Central tendency
 
Ppt central tendency measures
Ppt central tendency measuresPpt central tendency measures
Ppt central tendency measures
 
Mathematics Project Slides
Mathematics Project SlidesMathematics Project Slides
Mathematics Project Slides
 
frequency distribution table
frequency distribution tablefrequency distribution table
frequency distribution table
 
Estimation by c.i
Estimation by c.iEstimation by c.i
Estimation by c.i
 
Algebra unit 9.3
Algebra unit 9.3Algebra unit 9.3
Algebra unit 9.3
 
Mean, Median, Mode: Measures of Central Tendency
Mean, Median, Mode: Measures of Central Tendency Mean, Median, Mode: Measures of Central Tendency
Mean, Median, Mode: Measures of Central Tendency
 
Statistics
StatisticsStatistics
Statistics
 
Module 2 statistics
Module 2   statisticsModule 2   statistics
Module 2 statistics
 
Frequency Distributions
Frequency DistributionsFrequency Distributions
Frequency Distributions
 
MEAN DEVIATION VTU
MEAN DEVIATION VTUMEAN DEVIATION VTU
MEAN DEVIATION VTU
 
Algebra unit 9.2
Algebra unit 9.2Algebra unit 9.2
Algebra unit 9.2
 

En vedette

GOOGLE ANALYTICS by Donny BU
GOOGLE ANALYTICS by Donny BUGOOGLE ANALYTICS by Donny BU
GOOGLE ANALYTICS by Donny BUAkademi Berbagi
 
Data What Type Of Data Do You Have V2.1
Data   What Type Of Data Do You Have V2.1Data   What Type Of Data Do You Have V2.1
Data What Type Of Data Do You Have V2.1TimKasse
 
Ewil survey results
Ewil survey resultsEwil survey results
Ewil survey resultsImede
 
Computer data type and Terminologies
Computer data type and Terminologies Computer data type and Terminologies
Computer data type and Terminologies glyvive
 
Type of data @ Web Mining Discussion
Type of data @ Web Mining DiscussionType of data @ Web Mining Discussion
Type of data @ Web Mining DiscussionCherryBerry2
 
Using hoshin planning for six sigma project selection
Using hoshin planning for six sigma project selectionUsing hoshin planning for six sigma project selection
Using hoshin planning for six sigma project selectionEd Powers
 
Introduction to Statistics
Introduction to StatisticsIntroduction to Statistics
Introduction to StatisticsSaurav Shrestha
 
211344558 certified-six-sigma-black-belt-asq-cssbb (1)
211344558 certified-six-sigma-black-belt-asq-cssbb (1)211344558 certified-six-sigma-black-belt-asq-cssbb (1)
211344558 certified-six-sigma-black-belt-asq-cssbb (1)Saieesha Chitoori
 
Data structure,abstraction,abstract data type,static and dynamic,time and spa...
Data structure,abstraction,abstract data type,static and dynamic,time and spa...Data structure,abstraction,abstract data type,static and dynamic,time and spa...
Data structure,abstraction,abstract data type,static and dynamic,time and spa...Hassan Ahmed
 
Introduction to statistics 2013
Introduction to statistics 2013Introduction to statistics 2013
Introduction to statistics 2013Mohammad Ihmeidan
 
Introduction to Statistics - Basic Statistical Terms
Introduction to Statistics - Basic Statistical TermsIntroduction to Statistics - Basic Statistical Terms
Introduction to Statistics - Basic Statistical Termssheisirenebkm
 
Introduction to Statistics
Introduction to StatisticsIntroduction to Statistics
Introduction to Statisticsjasondroesch
 
Introduction to Statistics - Part 1
Introduction to Statistics - Part 1Introduction to Statistics - Part 1
Introduction to Statistics - Part 1Damian T. Gordon
 
Statistics lesson 1
Statistics   lesson 1Statistics   lesson 1
Statistics lesson 1Katrina Mae
 
Introduction to statistics
Introduction to statisticsIntroduction to statistics
Introduction to statisticsakbhanj
 
Basic Statistical Concepts and Methods
Basic Statistical Concepts and MethodsBasic Statistical Concepts and Methods
Basic Statistical Concepts and MethodsAhmed-Refat Refat
 

En vedette (20)

GOOGLE ANALYTICS by Donny BU
GOOGLE ANALYTICS by Donny BUGOOGLE ANALYTICS by Donny BU
GOOGLE ANALYTICS by Donny BU
 
Data What Type Of Data Do You Have V2.1
Data   What Type Of Data Do You Have V2.1Data   What Type Of Data Do You Have V2.1
Data What Type Of Data Do You Have V2.1
 
Ewil survey results
Ewil survey resultsEwil survey results
Ewil survey results
 
Computer data type and Terminologies
Computer data type and Terminologies Computer data type and Terminologies
Computer data type and Terminologies
 
All Nationwide Internal Certs
All Nationwide Internal CertsAll Nationwide Internal Certs
All Nationwide Internal Certs
 
04 type of data
04 type of data04 type of data
04 type of data
 
Type of data @ Web Mining Discussion
Type of data @ Web Mining DiscussionType of data @ Web Mining Discussion
Type of data @ Web Mining Discussion
 
Using hoshin planning for six sigma project selection
Using hoshin planning for six sigma project selectionUsing hoshin planning for six sigma project selection
Using hoshin planning for six sigma project selection
 
Introduction to Statistics
Introduction to StatisticsIntroduction to Statistics
Introduction to Statistics
 
211344558 certified-six-sigma-black-belt-asq-cssbb (1)
211344558 certified-six-sigma-black-belt-asq-cssbb (1)211344558 certified-six-sigma-black-belt-asq-cssbb (1)
211344558 certified-six-sigma-black-belt-asq-cssbb (1)
 
Data structure,abstraction,abstract data type,static and dynamic,time and spa...
Data structure,abstraction,abstract data type,static and dynamic,time and spa...Data structure,abstraction,abstract data type,static and dynamic,time and spa...
Data structure,abstraction,abstract data type,static and dynamic,time and spa...
 
Introduction to statistics 2013
Introduction to statistics 2013Introduction to statistics 2013
Introduction to statistics 2013
 
Introduction to Statistics - Basic Statistical Terms
Introduction to Statistics - Basic Statistical TermsIntroduction to Statistics - Basic Statistical Terms
Introduction to Statistics - Basic Statistical Terms
 
Data type
Data typeData type
Data type
 
Learning Six Sigma
Learning Six SigmaLearning Six Sigma
Learning Six Sigma
 
Introduction to Statistics
Introduction to StatisticsIntroduction to Statistics
Introduction to Statistics
 
Introduction to Statistics - Part 1
Introduction to Statistics - Part 1Introduction to Statistics - Part 1
Introduction to Statistics - Part 1
 
Statistics lesson 1
Statistics   lesson 1Statistics   lesson 1
Statistics lesson 1
 
Introduction to statistics
Introduction to statisticsIntroduction to statistics
Introduction to statistics
 
Basic Statistical Concepts and Methods
Basic Statistical Concepts and MethodsBasic Statistical Concepts and Methods
Basic Statistical Concepts and Methods
 

Similaire à Applied Math 40S March 12, 2008

Applied Math 40S March 14, 2008
Applied Math 40S March 14, 2008Applied Math 40S March 14, 2008
Applied Math 40S March 14, 2008Darren Kuropatwa
 
Applied 40S March 23, 2009
Applied 40S March 23, 2009Applied 40S March 23, 2009
Applied 40S March 23, 2009Darren Kuropatwa
 
Lect 3 background mathematics
Lect 3 background mathematicsLect 3 background mathematics
Lect 3 background mathematicshktripathy
 
Graphical presentation of data
Graphical presentation of dataGraphical presentation of data
Graphical presentation of dataprince irfan
 
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 Mininghktripathy
 
Engineering Statistics
Engineering Statistics Engineering Statistics
Engineering Statistics Bahzad5
 
Central tendency
Central tendencyCentral tendency
Central tendencyheyyou02
 
Applied 40S March 27, 2009
Applied 40S March 27, 2009Applied 40S March 27, 2009
Applied 40S March 27, 2009Darren Kuropatwa
 
3Measurements of health and disease_MCTD.pdf
3Measurements of health and disease_MCTD.pdf3Measurements of health and disease_MCTD.pdf
3Measurements of health and disease_MCTD.pdfAmanuelDina
 
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.pdfkelashraisal
 
Statistics (Measures of Dispersion)
Statistics (Measures of Dispersion)Statistics (Measures of Dispersion)
Statistics (Measures of Dispersion)Ron_Eick
 

Similaire à Applied Math 40S March 12, 2008 (20)

Applied Math 40S March 14, 2008
Applied Math 40S March 14, 2008Applied Math 40S March 14, 2008
Applied Math 40S March 14, 2008
 
Applied 40S March 23, 2009
Applied 40S March 23, 2009Applied 40S March 23, 2009
Applied 40S March 23, 2009
 
Practive test 1
Practive test 1Practive test 1
Practive test 1
 
Lect 3 background mathematics
Lect 3 background mathematicsLect 3 background mathematics
Lect 3 background mathematics
 
Graphical presentation of data
Graphical presentation of dataGraphical presentation of data
Graphical presentation of data
 
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
 
Practice Test 1
Practice Test 1Practice Test 1
Practice Test 1
 
Practice test1 solution
Practice test1 solutionPractice test1 solution
Practice test1 solution
 
Engineering Statistics
Engineering Statistics Engineering Statistics
Engineering Statistics
 
Central tendency
Central tendencyCentral tendency
Central tendency
 
Applied 40S March 27, 2009
Applied 40S March 27, 2009Applied 40S March 27, 2009
Applied 40S March 27, 2009
 
3Measurements of health and disease_MCTD.pdf
3Measurements of health and disease_MCTD.pdf3Measurements of health and disease_MCTD.pdf
3Measurements of health and disease_MCTD.pdf
 
Descriptive statistics
Descriptive statisticsDescriptive statistics
Descriptive statistics
 
9주차
9주차9주차
9주차
 
Bab 3.ppt
Bab 3.pptBab 3.ppt
Bab 3.ppt
 
Measures of variation discuss 2.1
Measures of variation discuss  2.1Measures of variation discuss  2.1
Measures of variation discuss 2.1
 
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
 
presentation2.pptx
presentation2.pptxpresentation2.pptx
presentation2.pptx
 
Statistics (Measures of Dispersion)
Statistics (Measures of Dispersion)Statistics (Measures of Dispersion)
Statistics (Measures of Dispersion)
 
Medidas de tendencia central
Medidas de tendencia centralMedidas de tendencia central
Medidas de tendencia central
 

Plus de Darren Kuropatwa

Providing Permission To Wonder v2.1
Providing Permission To Wonder v2.1Providing Permission To Wonder v2.1
Providing Permission To Wonder v2.1Darren Kuropatwa
 
Things That Suck About Digital Citizenship v1
Things That Suck About Digital Citizenship v1Things That Suck About Digital Citizenship v1
Things That Suck About Digital Citizenship v1Darren Kuropatwa
 
Digital Storytelling for Deeper Learning v1
Digital Storytelling for Deeper Learning v1Digital Storytelling for Deeper Learning v1
Digital Storytelling for Deeper Learning v1Darren Kuropatwa
 
Tales of Learning and the Gifts of Footprints v4.2
Tales of Learning and the Gifts of Footprints v4.2Tales of Learning and the Gifts of Footprints v4.2
Tales of Learning and the Gifts of Footprints v4.2Darren Kuropatwa
 
Making Student Thinking Visible v4
 Making Student Thinking Visible v4 Making Student Thinking Visible v4
Making Student Thinking Visible v4Darren Kuropatwa
 
Leveraging Digital for Deeper Learning
Leveraging Digital for Deeper LearningLeveraging Digital for Deeper Learning
Leveraging Digital for Deeper LearningDarren Kuropatwa
 
We Learn Through Stories at PRIZMAH17
We Learn Through Stories at PRIZMAH17We Learn Through Stories at PRIZMAH17
We Learn Through Stories at PRIZMAH17Darren Kuropatwa
 
Providing Permission to Wonder v3
Providing Permission to Wonder v3Providing Permission to Wonder v3
Providing Permission to Wonder v3Darren Kuropatwa
 
Making Student Thinking Visible v3.7
 Making Student Thinking Visible v3.7 Making Student Thinking Visible v3.7
Making Student Thinking Visible v3.7Darren Kuropatwa
 
Providing Permission to Wonder v2
Providing Permission to Wonder v2Providing Permission to Wonder v2
Providing Permission to Wonder v2Darren Kuropatwa
 
We Learn Through Stories v4 (master class)
We Learn Through Stories v4 (master class)We Learn Through Stories v4 (master class)
We Learn Through Stories v4 (master class)Darren Kuropatwa
 
Deep Learning Design: the middle ring
Deep Learning Design: the middle ringDeep Learning Design: the middle ring
Deep Learning Design: the middle ringDarren Kuropatwa
 

Plus de Darren Kuropatwa (20)

Behind Their Eyes v1
Behind Their Eyes v1Behind Their Eyes v1
Behind Their Eyes v1
 
Leading Change v1
Leading Change v1Leading Change v1
Leading Change v1
 
Providing Permission To Wonder v2.1
Providing Permission To Wonder v2.1Providing Permission To Wonder v2.1
Providing Permission To Wonder v2.1
 
Things That Suck About Digital Citizenship v1
Things That Suck About Digital Citizenship v1Things That Suck About Digital Citizenship v1
Things That Suck About Digital Citizenship v1
 
Digital Storytelling for Deeper Learning v1
Digital Storytelling for Deeper Learning v1Digital Storytelling for Deeper Learning v1
Digital Storytelling for Deeper Learning v1
 
Tales of Learning and the Gifts of Footprints v4.2
Tales of Learning and the Gifts of Footprints v4.2Tales of Learning and the Gifts of Footprints v4.2
Tales of Learning and the Gifts of Footprints v4.2
 
The Fourth Screen v4.2
The Fourth Screen v4.2The Fourth Screen v4.2
The Fourth Screen v4.2
 
Making Student Thinking Visible v4
 Making Student Thinking Visible v4 Making Student Thinking Visible v4
Making Student Thinking Visible v4
 
Learning is at BYTE 2017
Learning is at BYTE 2017Learning is at BYTE 2017
Learning is at BYTE 2017
 
Leveraging Digital for Deeper Learning
Leveraging Digital for Deeper LearningLeveraging Digital for Deeper Learning
Leveraging Digital for Deeper Learning
 
We Learn Through Stories at PRIZMAH17
We Learn Through Stories at PRIZMAH17We Learn Through Stories at PRIZMAH17
We Learn Through Stories at PRIZMAH17
 
Providing Permission to Wonder v3
Providing Permission to Wonder v3Providing Permission to Wonder v3
Providing Permission to Wonder v3
 
The Fourth Screen v4.1
The Fourth Screen v4.1The Fourth Screen v4.1
The Fourth Screen v4.1
 
Making Student Thinking Visible v3.7
 Making Student Thinking Visible v3.7 Making Student Thinking Visible v3.7
Making Student Thinking Visible v3.7
 
Learning is at AUHSD
Learning is at AUHSDLearning is at AUHSD
Learning is at AUHSD
 
Providing Permission to Wonder v2
Providing Permission to Wonder v2Providing Permission to Wonder v2
Providing Permission to Wonder v2
 
The Fourth Screen v4
The Fourth Screen v4The Fourth Screen v4
The Fourth Screen v4
 
Learning is at BLC16
Learning is at BLC16Learning is at BLC16
Learning is at BLC16
 
We Learn Through Stories v4 (master class)
We Learn Through Stories v4 (master class)We Learn Through Stories v4 (master class)
We Learn Through Stories v4 (master class)
 
Deep Learning Design: the middle ring
Deep Learning Design: the middle ringDeep Learning Design: the middle ring
Deep Learning Design: the middle ring
 

Dernier

Arihant handbook biology for class 11 .pdf
Arihant handbook biology for class 11 .pdfArihant handbook biology for class 11 .pdf
Arihant handbook biology for class 11 .pdfchloefrazer622
 
Grant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingGrant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingTechSoup
 
Introduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The BasicsIntroduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The BasicsTechSoup
 
Russian Call Girls in Andheri Airport Mumbai WhatsApp 9167673311 💞 Full Nigh...
Russian Call Girls in Andheri Airport Mumbai WhatsApp  9167673311 💞 Full Nigh...Russian Call Girls in Andheri Airport Mumbai WhatsApp  9167673311 💞 Full Nigh...
Russian Call Girls in Andheri Airport Mumbai WhatsApp 9167673311 💞 Full Nigh...Pooja Nehwal
 
Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3JemimahLaneBuaron
 
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...Krashi Coaching
 
The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13Steve Thomason
 
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptxPOINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptxSayali Powar
 
Organic Name Reactions for the students and aspirants of Chemistry12th.pptx
Organic Name Reactions  for the students and aspirants of Chemistry12th.pptxOrganic Name Reactions  for the students and aspirants of Chemistry12th.pptx
Organic Name Reactions for the students and aspirants of Chemistry12th.pptxVS Mahajan Coaching Centre
 
Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104misteraugie
 
CARE OF CHILD IN INCUBATOR..........pptx
CARE OF CHILD IN INCUBATOR..........pptxCARE OF CHILD IN INCUBATOR..........pptx
CARE OF CHILD IN INCUBATOR..........pptxGaneshChakor2
 
Interactive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communicationInteractive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communicationnomboosow
 
Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17Celine George
 
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Sapana Sha
 
JAPAN: ORGANISATION OF PMDA, PHARMACEUTICAL LAWS & REGULATIONS, TYPES OF REGI...
JAPAN: ORGANISATION OF PMDA, PHARMACEUTICAL LAWS & REGULATIONS, TYPES OF REGI...JAPAN: ORGANISATION OF PMDA, PHARMACEUTICAL LAWS & REGULATIONS, TYPES OF REGI...
JAPAN: ORGANISATION OF PMDA, PHARMACEUTICAL LAWS & REGULATIONS, TYPES OF REGI...anjaliyadav012327
 
Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)eniolaolutunde
 

Dernier (20)

Arihant handbook biology for class 11 .pdf
Arihant handbook biology for class 11 .pdfArihant handbook biology for class 11 .pdf
Arihant handbook biology for class 11 .pdf
 
Grant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingGrant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy Consulting
 
Introduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The BasicsIntroduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The Basics
 
Russian Call Girls in Andheri Airport Mumbai WhatsApp 9167673311 💞 Full Nigh...
Russian Call Girls in Andheri Airport Mumbai WhatsApp  9167673311 💞 Full Nigh...Russian Call Girls in Andheri Airport Mumbai WhatsApp  9167673311 💞 Full Nigh...
Russian Call Girls in Andheri Airport Mumbai WhatsApp 9167673311 💞 Full Nigh...
 
Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3
 
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptxINDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
 
Advance Mobile Application Development class 07
Advance Mobile Application Development class 07Advance Mobile Application Development class 07
Advance Mobile Application Development class 07
 
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
 
The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13
 
Código Creativo y Arte de Software | Unidad 1
Código Creativo y Arte de Software | Unidad 1Código Creativo y Arte de Software | Unidad 1
Código Creativo y Arte de Software | Unidad 1
 
Mattingly "AI & Prompt Design: The Basics of Prompt Design"
Mattingly "AI & Prompt Design: The Basics of Prompt Design"Mattingly "AI & Prompt Design: The Basics of Prompt Design"
Mattingly "AI & Prompt Design: The Basics of Prompt Design"
 
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptxPOINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
 
Organic Name Reactions for the students and aspirants of Chemistry12th.pptx
Organic Name Reactions  for the students and aspirants of Chemistry12th.pptxOrganic Name Reactions  for the students and aspirants of Chemistry12th.pptx
Organic Name Reactions for the students and aspirants of Chemistry12th.pptx
 
Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104
 
CARE OF CHILD IN INCUBATOR..........pptx
CARE OF CHILD IN INCUBATOR..........pptxCARE OF CHILD IN INCUBATOR..........pptx
CARE OF CHILD IN INCUBATOR..........pptx
 
Interactive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communicationInteractive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communication
 
Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17
 
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
 
JAPAN: ORGANISATION OF PMDA, PHARMACEUTICAL LAWS & REGULATIONS, TYPES OF REGI...
JAPAN: ORGANISATION OF PMDA, PHARMACEUTICAL LAWS & REGULATIONS, TYPES OF REGI...JAPAN: ORGANISATION OF PMDA, PHARMACEUTICAL LAWS & REGULATIONS, TYPES OF REGI...
JAPAN: ORGANISATION OF PMDA, PHARMACEUTICAL LAWS & REGULATIONS, TYPES OF REGI...
 
Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)
 

Applied Math 40S March 12, 2008

  • 1. Introduction to Statistics Queen Victoria, Cunard's newest cruise ship by savannahgrandfather
  • 2. Why Study Statistics? • Two students in two different schools each have marks of 95 percent. Which student should receive an award for getting the 'higher' mark? • How do doctors decide that teenagers should or should not get hepatitis vaccine? • Judith and Francine, both age 19, have decided to go on a Caribbean cruise, and they want to have an enjoyable time, which means that they want to travel with other people their own age. They buy tickets for a cruise where the average age of the other passengers is 20 years. Sounds like fun, no?
  • 3. Can you imagine their surprise at the start of the cruise when they discover that all the other passengers are parents (average age 32) with children (average age 8)? big_girl_04_m1_screen by pntphoto
  • 4. Statistics: the branch of mathematics that deals with collecting, organizing, displaying, and analyzing data. statistic: a number that describes one aspect of a group of data. EXAMPLE: mean, median, mode, range, standard deviation, etc... datum: one bit (piece) of information. data: many bits (pieces) of information. Types of Data quantitative data: data that is numeric (eg. height, weight, time..) There are two kinds of quantitative data: continuous and discrete continuous data: can be represented using real numbers (eg. height, weight, time, etc..) discrete data: can be represented by using ONLY intergers (eg. # of people, # of cars, # of animals, etc..) qualitative data: data that is non-numeric (eg. colours, flavours, etc...)
  • 5. Measures of Central Tendency mean: ( A.K.A. 'the arithmetic meanquot;) the symbol for mean is quot;x barquot;. The arithmetic average of a set of values. where x is the mean where Σx means the sum of all data (x) in the set (Σ is called quot;sigmaquot;) where n is the number of data in a set EXAMPLE: find the average mark this set of 5 quizzes: 48,52,65,45,65.
  • 6. Measures of Central Tendency median (med): 1) the middle value in an ordered (from smallest to largest) set of data. 2) if there are an even number of data, the median is the average of the middle pair in an ordered set of data. EXAMPLE: find the median of these quiz scores: 12,10,17,11,15 SOLUTION: 10, 11, 12, 15, 17 12 is the median. EXAMPLE: find the median of these scores: 12,10,17,11,15,11 SOLUTION: 10,11,11,12,15,17 the median is 11.5 mode (mo): the datum that occures most frequently in a set of data. EXAMPLE: find the mode in the set of quiz scores: 12,10,17,11,15,11 SOLUTION: the mode is 11 because it occurs more often that any other number in the set.
  • 7. Mean, Median, Mode, ... A clerk in a men's clothing store keeps a weekly record of the number of pairs of pants sold. The following is her list for two weeks. Mon Tue Wed Thur Fri Sat Week1 34 40 36 36 38 38 Week 2 32 36 36 42 34 34 Calculate the mean, mode, and median for the data shown. Bimodal Distribution
  • 8. Measures of Dispersion (Variability) Dave can drive to work using the downtown route or the perimeter route. The downtown route is shorter, but it has more traffic, and can become quite crowded. The driving times in minutes for each route (arranged in ascending order) for 5 days are shown on the table below. Downtown Route 15 26 30 39 45 Perimeter Route 29 30 31 32 33 The average driving time for each route is 31 minutes. Which route should he take?
  • 9. Measures of Dispersion (Variability) determine how quot;spread outquot; or variedquot; a set of data is. Range: the difference between the largest and smallest value in a set of data. EXAMPLE: find the range of ages of people in our class highest value: lowest value: RANGE: with teacher MR K. 40 yrs old. highest value: 40 lowest value: RANGE:
  • 10. Measures of Dispersion (Variability) Back to our example: Dave can drive to work using the downtown route or the perimeter route. The downtown route is shorter, but it has more traffic, and can become quite crowded. The driving times in minutes for each route (arranged in ascending order) for 5 days are shown on the table below. Downtown Route 15 26 30 39 45 Perimeter Route 29 30 31 32 33 Find the range associated with taking each route. Downtown Route Perimeter Route
  • 11. Measures of Dispersion (Variability) determine how quot;spread outquot; or variedquot; a set of data is. Standard Deviation (σ): a measure that shows how the data are spread about the mean value. Every value in the data set is used in calculating the standard deviation. Find the standard deviation associated with taking each route to Dave's work using your calculator. Downtown Route 15 26 30 39 45 Perimeter Route 29 30 31 32 33 Downtown Route Perimeter Route
  • 12. Measures of Dispersion (Variability) determine how quot;spread outquot; or variedquot; a set of data is. Standard Deviation (σ): How is the standard deviation calculated numerically? μ
  • 13. Let's apply what we've learned ... HOMEWORK The mean math marks and standard deviation for two classes are shown below. Assume that 68 percent of the marks in each class are within one standard deviation of the mean mark. mean mark (μ) standard deviation (σ) Class A 74 4 Class B 72 8 (a) In which class is the set of marks more dispersed? (b) Bert in Class A and Beth in Class B each have a mark of 82%. How many standard deviations are they from their class means? Who appears to have the better mark?
  • 14. HOMEWORK The following numbers represent the number of cars sold by Metro Motors in one week: Monday Tuesday Wednesday Thursday Friday Saturday 4 5 8 9 7 9 1. Determine the following statistics: (a) mean (b) mode (c) median (d) range 2. Which measure of central tendency may be the least significant? Explain.
  • 15. HOMEWORK The two sets of data show the weights of potatoes in bags. There are six bags in each set. Set #1 49 51 48 52 47 53 Set #2 40 60 45 55 35 65 The mean weight of each set of bags is 50 pounds. Which set has the greater standard deviation? How do you know? (Do not do any calculations.)
  • 16. HOMEWORK 78 92 62 52 65 59 A class of 30 students received the following marks in a mathematics examination. Calculate 53 63 68 73 71 63 the mean, median, range, and standard deviation. 69 74 73 81 55 71 75 81 84 77 80 75 41 57 91 62 65 49