SlideShare uma empresa Scribd logo
1 de 4
Baixar para ler offline
2013/05/23
1
STATISTICS
2. X-Kit Textbook
Chapter 13
3. Precalculus Textbook
Appendix B: Concepts in Statistics
Par B.3
Information23 May 2013
• Find information on Edulink on:
• Calculation of Semester Marks.
• Consultation Time during the June Exam.
• Exam Info.
• Report any problems with marks to Ms Durandt
as soon as possible, CR523.
• Semester Tests available at the Collection Facility
at the Mathematics Department.
• Find all memos on Edulink.
• Second Semester?
CONTENT
Dependent &
Independent
Variables
Scatter Diagrams
Correlation
Regression
TABLE: MARKS ACHIEVEDVERSUSTIME
STUDIED
STUDENT TIME STUDIED
(X) IN HOURS
MARKS
ACHIEVED (Y)
IN %
A 2 60
B 5 85
C 1 30
D 4 70
E 2 40
SCATTERDIAGRAM
2, 60
5, 85
1, 30
4, 70
2, 40
0
10
20
30
40
50
60
70
80
90
0 1 2 3 4 5 6
MarksAchieved(%)
Time Studied (Hours)
Scatter Diagram of marks achieved versus time studied
Y-Values
INTERPRETINGA SCATTERDIAGRAM
0
10
20
30
40
50
60
70
80
90
0 1 2 3 4 5 6
MarksAchieved(%)
Time Studied (Hours)
Y-Values
Linear (Y-Values)
2013/05/23
2
IS THERELATIONSHIPBETWEENVARIABLES
STRONGOR WEAK?
POSITIVE CORRELATION
0
5
10
15
20
25
30
35
40
0 2 4 6
Y-Values
NEGATIVE CORRELATION
0
5
10
15
20
25
0 2 4 6
Y-Values
IS THERELATIONSHIPBETWEENVARIABLES
STRONGOR WEAK?
NO CORRELATION
0
5
10
15
20
25
30
35
0 10 20 30 40
Y-Values
NON-LINEAR CORRELATION
0
0.5
1
1.5
2
2.5
3
3.5
0 1 2 3
Y-Values
SUMMINGUP CORRELATIONIN A NUMBER
• The correlation coefficient 𝑟 is a number that
tells us exactly how strong or weak the
correlation between two variables is.
• Calculate 𝑟 by using the formula:
𝒓 =
𝒙𝒚 − 𝒏𝒙 𝒚
𝒙 𝟐 − 𝒏𝒙 𝟐 𝒚 𝟐 − 𝒏𝒚 𝟐
• Calculate 𝑟 by using your calculator.
THE MEANINGOF 𝒓
Perfect Strong Mode-
rate
Weak No Linear
Correlation
Weak Mode-
rate
Strong Perfect
-1.00 -0.75 -0.50 -0.25 0.00 +0.25 +0.50 +0.75 +1.00
NEGATIVE CORRELATION POSITIVE CORRELATION
CALCULATETHE CORRELATIONCOEFFICIENT:
MARKS ACHIEVEDVERSUSTIME STUDIED
STUDENT TIME STUDIED
(X) IN HOURS
MARKS
ACHIEVED (Y)
IN %
A 2 60
B 5 85
C 1 30
D 4 70
E 2 40
SOLUTION
𝒓 = 𝟎. 𝟗𝟑𝟕
Very Strong Positive Linear
Relationship
2013/05/23
3
MAKE PREDICTIONS
Follow the plan:
•Find the LINE OF BEST FIT.
•Decide how “well” it fits.
•From a good fit we can make predictions.
LINE OF BESTFIT (REGRESSIONLINE)
• Formula 𝒚 = 𝒂 + 𝒃𝒙
• Formula for the regression coefficients:
𝒃 =
𝒙𝒚 − 𝒏𝒙 𝒚
𝒙 𝟐 − 𝒏𝒙 𝟐
𝒂 = 𝒚 − 𝒃𝒙
• Use your calculator to calculate the regression
coefficients.
CALCULATETHE REGRESSIONCOEFFICIENT:
MARKS ACHIEVEDVERSUSTIME STUDIED
STUDENT TIME STUDIED
(X) IN HOURS
MARKS
ACHIEVED (Y)
IN %
A 2 60
B 5 85
C 1 30
D 4 70
E 2 40
LINE OF BESTFIT
𝒚 = 𝟐𝟏. 𝟓 + 𝟏𝟐. 𝟕𝒙
INTERPRETINGA SCATTERDIAGRAM
y = 12.685x + 21.481
R² = 0.8777
0
10
20
30
40
50
60
70
80
90
0 1 2 3 4 5 6
MarksAchieved(%)
Time Studied (Hours)
Y-Values
Y-Values
Linear (Y-Values)
MEASURINGHOW WELL THELINE FITS
• How well does our line fit the real data? How accurate is
our model?
• 𝑟 , the CORRELATION COEFFICIENT tells us how STRONG
the relationship is between two variables, or how closely
the data fits our line.
• 𝑟2
, the COEFFICIENT OF DETERMINATION measure the
ACCURACY of our predictions. For a perfect fit 𝑟2
= 1 ,
closer to zero indicate a poorer fit.
• The coefficient of determination tells us that 87.8% of the
variation in students’ marks is linked to the amount of
time they spend studying. The other 12.2% is due to
other factors, like intelligence levels.
2013/05/23
4
PREDICTINGFROMTHELINE OF “BEST FIT”
If your friend only study for 2.5 hours,
will he pass the test?
𝒚 = 𝟐𝟏. 𝟓 + 𝟏𝟐. 𝟕𝒙
HOMEWORK
•Example X-Kit textbook page 310 – 311.
•Practise for your exams page 312 number
1, 2, 3, 4, 5, & 6.
•Par B.3 (page B14) all odd number
questions.

Mais conteúdo relacionado

Mais procurados

Mann Whitney U Test And Chi Squared
Mann Whitney U Test And Chi SquaredMann Whitney U Test And Chi Squared
Mann Whitney U Test And Chi Squared
guest2137aa
 
Linear graph[1]edit
Linear graph[1]editLinear graph[1]edit
Linear graph[1]edit
smarshall9
 

Mais procurados (17)

Medidas de tendencia central
Medidas de tendencia centralMedidas de tendencia central
Medidas de tendencia central
 
Data analysis, statistics, and probability review
Data analysis, statistics, and probability reviewData analysis, statistics, and probability review
Data analysis, statistics, and probability review
 
Quartile (ungrouped)
Quartile (ungrouped)Quartile (ungrouped)
Quartile (ungrouped)
 
Review on module 9
Review on module 9Review on module 9
Review on module 9
 
Khurram
KhurramKhurram
Khurram
 
quartiles,deciles,percentiles.ppt
quartiles,deciles,percentiles.pptquartiles,deciles,percentiles.ppt
quartiles,deciles,percentiles.ppt
 
Partitial values
Partitial valuesPartitial values
Partitial values
 
Ch 1 Review
Ch 1 ReviewCh 1 Review
Ch 1 Review
 
Exponents and powers--Part1
Exponents and powers--Part1Exponents and powers--Part1
Exponents and powers--Part1
 
Mann Whitney U Test And Chi Squared
Mann Whitney U Test And Chi SquaredMann Whitney U Test And Chi Squared
Mann Whitney U Test And Chi Squared
 
4.3 Dilation and Composition
4.3 Dilation and Composition4.3 Dilation and Composition
4.3 Dilation and Composition
 
Quartile Deviation
Quartile DeviationQuartile Deviation
Quartile Deviation
 
Stopping Problems
Stopping ProblemsStopping Problems
Stopping Problems
 
Lesson 2 percentiles
Lesson 2   percentilesLesson 2   percentiles
Lesson 2 percentiles
 
Linear graph[1]edit
Linear graph[1]editLinear graph[1]edit
Linear graph[1]edit
 
Understanding Statistics 1#12 Quartiles of Data
Understanding Statistics 1#12 Quartiles of DataUnderstanding Statistics 1#12 Quartiles of Data
Understanding Statistics 1#12 Quartiles of Data
 
Education 309 – Statistics for Educational Research
Education 309 – Statistics for Educational ResearchEducation 309 – Statistics for Educational Research
Education 309 – Statistics for Educational Research
 

Destaque

Chapter 13 finding relationships
Chapter 13 finding relationshipsChapter 13 finding relationships
Chapter 13 finding relationships
201120305
 

Destaque (11)

Chapter 13 finding relationships
Chapter 13 finding relationshipsChapter 13 finding relationships
Chapter 13 finding relationships
 
TRIANGULOS
TRIANGULOSTRIANGULOS
TRIANGULOS
 
Proyecto final informatica
Proyecto final informatica Proyecto final informatica
Proyecto final informatica
 
Legislación Laboral Aplicada
Legislación Laboral AplicadaLegislación Laboral Aplicada
Legislación Laboral Aplicada
 
Customer Experience - Changing Shopping Experiences In India – End to End Bra...
Customer Experience - Changing Shopping Experiences In India – End to End Bra...Customer Experience - Changing Shopping Experiences In India – End to End Bra...
Customer Experience - Changing Shopping Experiences In India – End to End Bra...
 
5 Tech-Enabled Business Trends in 2017
5 Tech-Enabled Business Trends in 20175 Tech-Enabled Business Trends in 2017
5 Tech-Enabled Business Trends in 2017
 
The Boon of Cross Border E-commerce
The Boon of Cross Border E-commerceThe Boon of Cross Border E-commerce
The Boon of Cross Border E-commerce
 
eTailing India Expo Mumbai 2017 - Recap
eTailing India Expo Mumbai 2017 - RecapeTailing India Expo Mumbai 2017 - Recap
eTailing India Expo Mumbai 2017 - Recap
 
Chapter 8
Chapter 8Chapter 8
Chapter 8
 
Chapter 7
Chapter 7Chapter 7
Chapter 7
 
6) gemo 005 guia tecnica audiometria
6) gemo 005 guia tecnica audiometria6) gemo 005 guia tecnica audiometria
6) gemo 005 guia tecnica audiometria
 

Semelhante a Chapter 13 finding relationships

Predictive Modelling
Predictive ModellingPredictive Modelling
Predictive Modelling
Rajiv Advani
 
Mod 4 data presentation graphs bar charts tables
Mod 4 data presentation graphs bar charts tablesMod 4 data presentation graphs bar charts tables
Mod 4 data presentation graphs bar charts tables
mpape
 
Pertemuan 1 pengukuran te d3
Pertemuan 1   pengukuran te d3Pertemuan 1   pengukuran te d3
Pertemuan 1 pengukuran te d3
atikah ardi
 

Semelhante a Chapter 13 finding relationships (20)

Data mining 8 estimasi linear regression
Data mining 8   estimasi linear regressionData mining 8   estimasi linear regression
Data mining 8 estimasi linear regression
 
STANDARD DEVIATION (2018) (STATISTICS)
STANDARD DEVIATION (2018) (STATISTICS)STANDARD DEVIATION (2018) (STATISTICS)
STANDARD DEVIATION (2018) (STATISTICS)
 
Measures of Central Tendency.pptx
Measures of Central Tendency.pptxMeasures of Central Tendency.pptx
Measures of Central Tendency.pptx
 
Predictive Modelling
Predictive ModellingPredictive Modelling
Predictive Modelling
 
Lesson 27 using statistical techniques in analyzing data
Lesson 27 using statistical techniques in analyzing dataLesson 27 using statistical techniques in analyzing data
Lesson 27 using statistical techniques in analyzing data
 
Measures of-variation
Measures of-variationMeasures of-variation
Measures of-variation
 
Frequency Distribution Table 3
Frequency Distribution Table 3Frequency Distribution Table 3
Frequency Distribution Table 3
 
frequency distribution table 3
frequency distribution table 3frequency distribution table 3
frequency distribution table 3
 
Machine learning meetup
Machine learning meetupMachine learning meetup
Machine learning meetup
 
Types of graphs
Types of graphsTypes of graphs
Types of graphs
 
Measures of central tendency - STATISTICS
Measures of central tendency - STATISTICSMeasures of central tendency - STATISTICS
Measures of central tendency - STATISTICS
 
Class X Mathematics Study Material
Class X Mathematics Study MaterialClass X Mathematics Study Material
Class X Mathematics Study Material
 
Mod 4 data presentation graphs bar charts tables
Mod 4 data presentation graphs bar charts tablesMod 4 data presentation graphs bar charts tables
Mod 4 data presentation graphs bar charts tables
 
MEASURES OF DISPERSION NOTES.pdf
MEASURES OF DISPERSION NOTES.pdfMEASURES OF DISPERSION NOTES.pdf
MEASURES OF DISPERSION NOTES.pdf
 
ITEM ANALYSIS
ITEM ANALYSIS ITEM ANALYSIS
ITEM ANALYSIS
 
Ee184405 statistika dan stokastik statistik deskriptif 1 grafik
Ee184405 statistika dan stokastik   statistik deskriptif 1 grafikEe184405 statistika dan stokastik   statistik deskriptif 1 grafik
Ee184405 statistika dan stokastik statistik deskriptif 1 grafik
 
633e639cc8efda0018e1ca63_##_Graphical Representation 01 _ Class Notes __ (Vic...
633e639cc8efda0018e1ca63_##_Graphical Representation 01 _ Class Notes __ (Vic...633e639cc8efda0018e1ca63_##_Graphical Representation 01 _ Class Notes __ (Vic...
633e639cc8efda0018e1ca63_##_Graphical Representation 01 _ Class Notes __ (Vic...
 
refreshENM1500.pdf
refreshENM1500.pdfrefreshENM1500.pdf
refreshENM1500.pdf
 
Pertemuan 1 pengukuran te d3
Pertemuan 1   pengukuran te d3Pertemuan 1   pengukuran te d3
Pertemuan 1 pengukuran te d3
 
plc learn
plc learnplc learn
plc learn
 

Último

Último (20)

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
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps ScriptAutomating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
 
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...2024: Domino Containers - The Next Step. News from the Domino Container commu...
2024: Domino Containers - The Next Step. News from the Domino Container commu...
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
 
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
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
 
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
 

Chapter 13 finding relationships

  • 1. 2013/05/23 1 STATISTICS 2. X-Kit Textbook Chapter 13 3. Precalculus Textbook Appendix B: Concepts in Statistics Par B.3 Information23 May 2013 • Find information on Edulink on: • Calculation of Semester Marks. • Consultation Time during the June Exam. • Exam Info. • Report any problems with marks to Ms Durandt as soon as possible, CR523. • Semester Tests available at the Collection Facility at the Mathematics Department. • Find all memos on Edulink. • Second Semester? CONTENT Dependent & Independent Variables Scatter Diagrams Correlation Regression TABLE: MARKS ACHIEVEDVERSUSTIME STUDIED STUDENT TIME STUDIED (X) IN HOURS MARKS ACHIEVED (Y) IN % A 2 60 B 5 85 C 1 30 D 4 70 E 2 40 SCATTERDIAGRAM 2, 60 5, 85 1, 30 4, 70 2, 40 0 10 20 30 40 50 60 70 80 90 0 1 2 3 4 5 6 MarksAchieved(%) Time Studied (Hours) Scatter Diagram of marks achieved versus time studied Y-Values INTERPRETINGA SCATTERDIAGRAM 0 10 20 30 40 50 60 70 80 90 0 1 2 3 4 5 6 MarksAchieved(%) Time Studied (Hours) Y-Values Linear (Y-Values)
  • 2. 2013/05/23 2 IS THERELATIONSHIPBETWEENVARIABLES STRONGOR WEAK? POSITIVE CORRELATION 0 5 10 15 20 25 30 35 40 0 2 4 6 Y-Values NEGATIVE CORRELATION 0 5 10 15 20 25 0 2 4 6 Y-Values IS THERELATIONSHIPBETWEENVARIABLES STRONGOR WEAK? NO CORRELATION 0 5 10 15 20 25 30 35 0 10 20 30 40 Y-Values NON-LINEAR CORRELATION 0 0.5 1 1.5 2 2.5 3 3.5 0 1 2 3 Y-Values SUMMINGUP CORRELATIONIN A NUMBER • The correlation coefficient 𝑟 is a number that tells us exactly how strong or weak the correlation between two variables is. • Calculate 𝑟 by using the formula: 𝒓 = 𝒙𝒚 − 𝒏𝒙 𝒚 𝒙 𝟐 − 𝒏𝒙 𝟐 𝒚 𝟐 − 𝒏𝒚 𝟐 • Calculate 𝑟 by using your calculator. THE MEANINGOF 𝒓 Perfect Strong Mode- rate Weak No Linear Correlation Weak Mode- rate Strong Perfect -1.00 -0.75 -0.50 -0.25 0.00 +0.25 +0.50 +0.75 +1.00 NEGATIVE CORRELATION POSITIVE CORRELATION CALCULATETHE CORRELATIONCOEFFICIENT: MARKS ACHIEVEDVERSUSTIME STUDIED STUDENT TIME STUDIED (X) IN HOURS MARKS ACHIEVED (Y) IN % A 2 60 B 5 85 C 1 30 D 4 70 E 2 40 SOLUTION 𝒓 = 𝟎. 𝟗𝟑𝟕 Very Strong Positive Linear Relationship
  • 3. 2013/05/23 3 MAKE PREDICTIONS Follow the plan: •Find the LINE OF BEST FIT. •Decide how “well” it fits. •From a good fit we can make predictions. LINE OF BESTFIT (REGRESSIONLINE) • Formula 𝒚 = 𝒂 + 𝒃𝒙 • Formula for the regression coefficients: 𝒃 = 𝒙𝒚 − 𝒏𝒙 𝒚 𝒙 𝟐 − 𝒏𝒙 𝟐 𝒂 = 𝒚 − 𝒃𝒙 • Use your calculator to calculate the regression coefficients. CALCULATETHE REGRESSIONCOEFFICIENT: MARKS ACHIEVEDVERSUSTIME STUDIED STUDENT TIME STUDIED (X) IN HOURS MARKS ACHIEVED (Y) IN % A 2 60 B 5 85 C 1 30 D 4 70 E 2 40 LINE OF BESTFIT 𝒚 = 𝟐𝟏. 𝟓 + 𝟏𝟐. 𝟕𝒙 INTERPRETINGA SCATTERDIAGRAM y = 12.685x + 21.481 R² = 0.8777 0 10 20 30 40 50 60 70 80 90 0 1 2 3 4 5 6 MarksAchieved(%) Time Studied (Hours) Y-Values Y-Values Linear (Y-Values) MEASURINGHOW WELL THELINE FITS • How well does our line fit the real data? How accurate is our model? • 𝑟 , the CORRELATION COEFFICIENT tells us how STRONG the relationship is between two variables, or how closely the data fits our line. • 𝑟2 , the COEFFICIENT OF DETERMINATION measure the ACCURACY of our predictions. For a perfect fit 𝑟2 = 1 , closer to zero indicate a poorer fit. • The coefficient of determination tells us that 87.8% of the variation in students’ marks is linked to the amount of time they spend studying. The other 12.2% is due to other factors, like intelligence levels.
  • 4. 2013/05/23 4 PREDICTINGFROMTHELINE OF “BEST FIT” If your friend only study for 2.5 hours, will he pass the test? 𝒚 = 𝟐𝟏. 𝟓 + 𝟏𝟐. 𝟕𝒙 HOMEWORK •Example X-Kit textbook page 310 – 311. •Practise for your exams page 312 number 1, 2, 3, 4, 5, & 6. •Par B.3 (page B14) all odd number questions.