Data Analysis STA2300 - Transtutors · Data Analysis STA2300 Professor Shahjahan Khan, PhD School...
Transcript of Data Analysis STA2300 - Transtutors · Data Analysis STA2300 Professor Shahjahan Khan, PhD School...
Data Analysis STA2300
Professor Shahjahan Khan, PhD
School of Agricultural, Computational and Environmental SciencesFaculty of Health, Engineering and Sciences
University of Southern Queensland
Lecture 1
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
Part I
Introduction
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
Welcome to STA2300
Warm Welcome to STA2300 Data Analysis CourseMy name is Shahjahan Khan
The Tajmahal built by Mughal Emperor ShahjahanWorked and lived in 9 different countriesExaminer of the course for this semesterI hope to work with you through out the semester
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
General Announcements
Announcements
Tutorials start this week:Bring textbook; Studybook (containing tutorialquestions); calculator
Use StudyDesk via UConnect regularlyCheck the News Forum at least once a week!Check the Social/Topics Forum at least once a week!Check your Umail regularly
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
General Announcements
Announcements
Tutorials start this week:Bring textbook; Studybook (containing tutorialquestions); calculator
Use StudyDesk via UConnect regularlyCheck the News Forum at least once a week!Check the Social/Topics Forum at least once a week!Check your Umail regularly
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
General Announcements
Announcements
Tutorials start this week:Bring textbook; Studybook (containing tutorialquestions); calculator
Use StudyDesk via UConnect regularlyCheck the News Forum at least once a week!Check the Social/Topics Forum at least once a week!Check your Umail regularly
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
General Announcements
Announcements
Tutorials start this week:Bring textbook; Studybook (containing tutorialquestions); calculator
Use StudyDesk via UConnect regularlyCheck the News Forum at least once a week!Check the Social/Topics Forum at least once a week!Check your Umail regularly
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
General Announcements
Announcements
Tutorials start this week:Bring textbook; Studybook (containing tutorialquestions); calculator
Use StudyDesk via UConnect regularlyCheck the News Forum at least once a week!Check the Social/Topics Forum at least once a week!Check your Umail regularly
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
General Announcements
Announcements
Tutorials start this week:Bring textbook; Studybook (containing tutorialquestions); calculator
Use StudyDesk via UConnect regularlyCheck the News Forum at least once a week!Check the Social/Topics Forum at least once a week!Check your Umail regularly
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
General Announcements
AssessmentThree assignments and 2 hour exam are compulsory
Three assignments (5%, 20% and 25%)Online Assignment 1 (i.e., Quiz 1) due Friday of nextweek! (Complete online through StudyDesk by 11.55pm)Assignments 2 & 3 submit as pdf document viaStudyDesk by 11.55 pm of due dateExam (50% total weighting):
Part A is 20 multiple choice questionsPart B is 30 marks of short answer questions
Passing grade: (1) at least 50% of total weightedmarks and (2) at least 40% of the 50% (ie 20/50) inthe final exam.
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
General Announcements
AssessmentThree assignments and 2 hour exam are compulsory
Three assignments (5%, 20% and 25%)Online Assignment 1 (i.e., Quiz 1) due Friday of nextweek! (Complete online through StudyDesk by 11.55pm)Assignments 2 & 3 submit as pdf document viaStudyDesk by 11.55 pm of due dateExam (50% total weighting):
Part A is 20 multiple choice questionsPart B is 30 marks of short answer questions
Passing grade: (1) at least 50% of total weightedmarks and (2) at least 40% of the 50% (ie 20/50) inthe final exam.
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
General Announcements
AssessmentThree assignments and 2 hour exam are compulsory
Three assignments (5%, 20% and 25%)Online Assignment 1 (i.e., Quiz 1) due Friday of nextweek! (Complete online through StudyDesk by 11.55pm)Assignments 2 & 3 submit as pdf document viaStudyDesk by 11.55 pm of due dateExam (50% total weighting):
Part A is 20 multiple choice questionsPart B is 30 marks of short answer questions
Passing grade: (1) at least 50% of total weightedmarks and (2) at least 40% of the 50% (ie 20/50) inthe final exam.
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
General Announcements
AssessmentThree assignments and 2 hour exam are compulsory
Three assignments (5%, 20% and 25%)Online Assignment 1 (i.e., Quiz 1) due Friday of nextweek! (Complete online through StudyDesk by 11.55pm)Assignments 2 & 3 submit as pdf document viaStudyDesk by 11.55 pm of due dateExam (50% total weighting):
Part A is 20 multiple choice questionsPart B is 30 marks of short answer questions
Passing grade: (1) at least 50% of total weightedmarks and (2) at least 40% of the 50% (ie 20/50) inthe final exam.
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
General Announcements
AssessmentThree assignments and 2 hour exam are compulsory
Three assignments (5%, 20% and 25%)Online Assignment 1 (i.e., Quiz 1) due Friday of nextweek! (Complete online through StudyDesk by 11.55pm)Assignments 2 & 3 submit as pdf document viaStudyDesk by 11.55 pm of due dateExam (50% total weighting):
Part A is 20 multiple choice questionsPart B is 30 marks of short answer questions
Passing grade: (1) at least 50% of total weightedmarks and (2) at least 40% of the 50% (ie 20/50) inthe final exam.
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
General Announcements
AssessmentThree assignments and 2 hour exam are compulsory
Three assignments (5%, 20% and 25%)Online Assignment 1 (i.e., Quiz 1) due Friday of nextweek! (Complete online through StudyDesk by 11.55pm)Assignments 2 & 3 submit as pdf document viaStudyDesk by 11.55 pm of due dateExam (50% total weighting):
Part A is 20 multiple choice questionsPart B is 30 marks of short answer questions
Passing grade: (1) at least 50% of total weightedmarks and (2) at least 40% of the 50% (ie 20/50) inthe final exam.
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
General Announcements
AssessmentThree assignments and 2 hour exam are compulsory
Three assignments (5%, 20% and 25%)Online Assignment 1 (i.e., Quiz 1) due Friday of nextweek! (Complete online through StudyDesk by 11.55pm)Assignments 2 & 3 submit as pdf document viaStudyDesk by 11.55 pm of due dateExam (50% total weighting):
Part A is 20 multiple choice questionsPart B is 30 marks of short answer questions
Passing grade: (1) at least 50% of total weightedmarks and (2) at least 40% of the 50% (ie 20/50) inthe final exam.
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
What you need to have access toWhat we provide
Materials you needDe Veaux, Velleman & Bock 4th edition (3rd ed is also OK)
Introductory Material (on StudyDesk )Study Book (on StudyDesk )Text book: Intro Stats by De Veaux, Velleman & Bock(4th edition) with ActivStats CD (Or Stats: data andmodels, 4th Global Edn)Calculator (with STAT mode)Access to the SPSS (also called IMB SPSS) software
Available for purchase from the bookshop orbuy online 6 month’s licence from Hearne Scientifichttps://www.hearne.software/Software/SPSS-Grad-Packs-for-Students-by-IBM/EditionsAvailable in all USQ PC labs
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
What you need to have access toWhat we provide
Materials you needDe Veaux, Velleman & Bock 4th edition (3rd ed is also OK)
Introductory Material (on StudyDesk )Study Book (on StudyDesk )Text book: Intro Stats by De Veaux, Velleman & Bock(4th edition) with ActivStats CD (Or Stats: data andmodels, 4th Global Edn)Calculator (with STAT mode)Access to the SPSS (also called IMB SPSS) software
Available for purchase from the bookshop orbuy online 6 month’s licence from Hearne Scientifichttps://www.hearne.software/Software/SPSS-Grad-Packs-for-Students-by-IBM/EditionsAvailable in all USQ PC labs
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
What you need to have access toWhat we provide
Materials you needDe Veaux, Velleman & Bock 4th edition (3rd ed is also OK)
Introductory Material (on StudyDesk )Study Book (on StudyDesk )Text book: Intro Stats by De Veaux, Velleman & Bock(4th edition) with ActivStats CD (Or Stats: data andmodels, 4th Global Edn)Calculator (with STAT mode)Access to the SPSS (also called IMB SPSS) software
Available for purchase from the bookshop orbuy online 6 month’s licence from Hearne Scientifichttps://www.hearne.software/Software/SPSS-Grad-Packs-for-Students-by-IBM/EditionsAvailable in all USQ PC labs
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
What you need to have access toWhat we provide
Materials you needDe Veaux, Velleman & Bock 4th edition (3rd ed is also OK)
Introductory Material (on StudyDesk )Study Book (on StudyDesk )Text book: Intro Stats by De Veaux, Velleman & Bock(4th edition) with ActivStats CD (Or Stats: data andmodels, 4th Global Edn)Calculator (with STAT mode)Access to the SPSS (also called IMB SPSS) software
Available for purchase from the bookshop orbuy online 6 month’s licence from Hearne Scientifichttps://www.hearne.software/Software/SPSS-Grad-Packs-for-Students-by-IBM/EditionsAvailable in all USQ PC labs
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
What you need to have access toWhat we provide
Materials you needDe Veaux, Velleman & Bock 4th edition (3rd ed is also OK)
Introductory Material (on StudyDesk )Study Book (on StudyDesk )Text book: Intro Stats by De Veaux, Velleman & Bock(4th edition) with ActivStats CD (Or Stats: data andmodels, 4th Global Edn)Calculator (with STAT mode)Access to the SPSS (also called IMB SPSS) software
Available for purchase from the bookshop orbuy online 6 month’s licence from Hearne Scientifichttps://www.hearne.software/Software/SPSS-Grad-Packs-for-Students-by-IBM/EditionsAvailable in all USQ PC labs
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
What you need to have access toWhat we provide
We provide:We provide them—but you must use them to get the benefit
Lectures, tutorials to on-campus studentsSPSS Exercises (on StudyDesk )Face-to-face, StudyDesk forums, telephone, ande-mail assistance via UAskLTS support (The Learning Centre)MEET-UP program (see StudyDesk for details)Feedback on assignmentsWeb resources: discussion forums etc. onStudyDesk
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
What you need to have access toWhat we provide
We provide:We provide them—but you must use them to get the benefit
Lectures, tutorials to on-campus studentsSPSS Exercises (on StudyDesk )Face-to-face, StudyDesk forums, telephone, ande-mail assistance via UAskLTS support (The Learning Centre)MEET-UP program (see StudyDesk for details)Feedback on assignmentsWeb resources: discussion forums etc. onStudyDesk
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
What you need to have access toWhat we provide
We provide:We provide them—but you must use them to get the benefit
Lectures, tutorials to on-campus studentsSPSS Exercises (on StudyDesk )Face-to-face, StudyDesk forums, telephone, ande-mail assistance via UAskLTS support (The Learning Centre)MEET-UP program (see StudyDesk for details)Feedback on assignmentsWeb resources: discussion forums etc. onStudyDesk
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
What you need to have access toWhat we provide
We provide:We provide them—but you must use them to get the benefit
Lectures, tutorials to on-campus studentsSPSS Exercises (on StudyDesk )Face-to-face, StudyDesk forums, telephone, ande-mail assistance via UAskLTS support (The Learning Centre)MEET-UP program (see StudyDesk for details)Feedback on assignmentsWeb resources: discussion forums etc. onStudyDesk
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
What you need to have access toWhat we provide
We provide:We provide them—but you must use them to get the benefit
Lectures, tutorials to on-campus studentsSPSS Exercises (on StudyDesk )Face-to-face, StudyDesk forums, telephone, ande-mail assistance via UAskLTS support (The Learning Centre)MEET-UP program (see StudyDesk for details)Feedback on assignmentsWeb resources: discussion forums etc. onStudyDesk
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
What you need to have access toWhat we provide
We provide:We provide them—but you must use them to get the benefit
Lectures, tutorials to on-campus studentsSPSS Exercises (on StudyDesk )Face-to-face, StudyDesk forums, telephone, ande-mail assistance via UAskLTS support (The Learning Centre)MEET-UP program (see StudyDesk for details)Feedback on assignmentsWeb resources: discussion forums etc. onStudyDesk
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
What you need to have access toWhat we provide
We provide:We provide them—but you must use them to get the benefit
Lectures, tutorials to on-campus studentsSPSS Exercises (on StudyDesk )Face-to-face, StudyDesk forums, telephone, ande-mail assistance via UAskLTS support (The Learning Centre)MEET-UP program (see StudyDesk for details)Feedback on assignmentsWeb resources: discussion forums etc. onStudyDesk
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
What you need to have access toWhat we provide
LTS supportThe Learning Centre
The Learning Centre is located near the bookshopSuccess in Mathematics for Statistics Online TutorialsCalculator bookletsDrop-in and phone-in mathematics supportAcademic Skills WorkshopsMore details from postings and links on theStudyDesk
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
What you need to have access toWhat we provide
LTS supportThe Learning Centre
The Learning Centre is located near the bookshopSuccess in Mathematics for Statistics Online TutorialsCalculator bookletsDrop-in and phone-in mathematics supportAcademic Skills WorkshopsMore details from postings and links on theStudyDesk
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
What you need to have access toWhat we provide
LTS supportThe Learning Centre
The Learning Centre is located near the bookshopSuccess in Mathematics for Statistics Online TutorialsCalculator bookletsDrop-in and phone-in mathematics supportAcademic Skills WorkshopsMore details from postings and links on theStudyDesk
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
What you need to have access toWhat we provide
LTS supportThe Learning Centre
The Learning Centre is located near the bookshopSuccess in Mathematics for Statistics Online TutorialsCalculator bookletsDrop-in and phone-in mathematics supportAcademic Skills WorkshopsMore details from postings and links on theStudyDesk
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
What you need to have access toWhat we provide
LTS supportThe Learning Centre
The Learning Centre is located near the bookshopSuccess in Mathematics for Statistics Online TutorialsCalculator bookletsDrop-in and phone-in mathematics supportAcademic Skills WorkshopsMore details from postings and links on theStudyDesk
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
What you need to have access toWhat we provide
LTS supportThe Learning Centre
The Learning Centre is located near the bookshopSuccess in Mathematics for Statistics Online TutorialsCalculator bookletsDrop-in and phone-in mathematics supportAcademic Skills WorkshopsMore details from postings and links on theStudyDesk
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
Why do this course?
You are doing it because, it isan essential skill of your degreedecided by your faculty lecturers/professors for youa skill expected by good employersessential for any research studies (eg honours)providing statistical literacy for everyday life
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
Why do this course?
You are doing it because, it isan essential skill of your degreedecided by your faculty lecturers/professors for youa skill expected by good employersessential for any research studies (eg honours)providing statistical literacy for everyday life
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
Why do this course?
You are doing it because, it isan essential skill of your degreedecided by your faculty lecturers/professors for youa skill expected by good employersessential for any research studies (eg honours)providing statistical literacy for everyday life
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
Why do this course?
You are doing it because, it isan essential skill of your degreedecided by your faculty lecturers/professors for youa skill expected by good employersessential for any research studies (eg honours)providing statistical literacy for everyday life
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
Why do this course?
You are doing it because, it isan essential skill of your degreedecided by your faculty lecturers/professors for youa skill expected by good employersessential for any research studies (eg honours)providing statistical literacy for everyday life
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
Why Statistics?
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
Contributions of Statistics?
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
Statistical disciplines
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
Statistics at the top
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
The basics of what we will learn
We will learnthe language of statistics (all Modules)how to summarise data (verbally, graphically andnumerically) (Modules 1 to 4)how to collect data (and how not to) (Module 5)how to generalise our observations to the wider world(Modules 6 to 11)
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
The basics of what we will learn
We will learnthe language of statistics (all Modules)how to summarise data (verbally, graphically andnumerically) (Modules 1 to 4)how to collect data (and how not to) (Module 5)how to generalise our observations to the wider world(Modules 6 to 11)
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
The basics of what we will learn
We will learnthe language of statistics (all Modules)how to summarise data (verbally, graphically andnumerically) (Modules 1 to 4)how to collect data (and how not to) (Module 5)how to generalise our observations to the wider world(Modules 6 to 11)
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
The basics of what we will learn
We will learnthe language of statistics (all Modules)how to summarise data (verbally, graphically andnumerically) (Modules 1 to 4)how to collect data (and how not to) (Module 5)how to generalise our observations to the wider world(Modules 6 to 11)
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
OutlineGeneral AnnouncementsWhat you need to have access toWhat we provide
1 Appendix A: Mathematics Review
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
What is Appendix A of the Study Book?
Appendix A is the assumed mathematics knowledgefor the course.It provides some of the essential mathematical skillsnecessary for Data AnalysisReturn to this chapter if your mathematics skills needrefreshing during the courseContact The Learning Centre for help with thismaterials support.It is not directly examinable, and not supported bythe course teaching team.
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
What is Appendix A of the Study Book?
Appendix A is the assumed mathematics knowledgefor the course.It provides some of the essential mathematical skillsnecessary for Data AnalysisReturn to this chapter if your mathematics skills needrefreshing during the courseContact The Learning Centre for help with thismaterials support.It is not directly examinable, and not supported bythe course teaching team.
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
What is Appendix A of the Study Book?
Appendix A is the assumed mathematics knowledgefor the course.It provides some of the essential mathematical skillsnecessary for Data AnalysisReturn to this chapter if your mathematics skills needrefreshing during the courseContact The Learning Centre for help with thismaterials support.It is not directly examinable, and not supported bythe course teaching team.
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
What is Appendix A of the Study Book?
Appendix A is the assumed mathematics knowledgefor the course.It provides some of the essential mathematical skillsnecessary for Data AnalysisReturn to this chapter if your mathematics skills needrefreshing during the courseContact The Learning Centre for help with thismaterials support.It is not directly examinable, and not supported bythe course teaching team.
Professor Shahjahan Khan, PhD Lecture 1
To get started. . .MaterialsOverview
Appendix A: Mathematics Review
What is Appendix A of the Study Book?
Appendix A is the assumed mathematics knowledgefor the course.It provides some of the essential mathematical skillsnecessary for Data AnalysisReturn to this chapter if your mathematics skills needrefreshing during the courseContact The Learning Centre for help with thismaterials support.It is not directly examinable, and not supported bythe course teaching team.
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Part II
Module 1: Exploring andunderstanding data
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Outline
2 §1.1 What is/are Statistics?Why do Data Analysis?
3 §1.2 About dataSome languageTypes of data
4 §1.3 Displaying categorical dataBar chartsPie chartsContingency tables
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical dataWhy do Data Analysis?
Outline
2 §1.1 What is/are Statistics?Why do Data Analysis?
3 §1.2 About dataSome languageTypes of data
4 §1.3 Displaying categorical dataBar chartsPie chartsContingency tables
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical dataWhy do Data Analysis?
Why learn Data Analysis?SB §1.1
Data are generated by repeated observationData analysis finds the information in dataInformation is used to learn about the world and helpdecision making
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical dataWhy do Data Analysis?
Why learn Data Analysis?SB §1.1
Data are generated by repeated observationData analysis finds the information in dataInformation is used to learn about the world and helpdecision making
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical dataWhy do Data Analysis?
Why learn Data Analysis?SB §1.1
Data are generated by repeated observationData analysis finds the information in dataInformation is used to learn about the world and helpdecision making
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical dataWhy do Data Analysis?
Who needs to know statistics?
If your job is: Statistics helps you:Collecting data know how much data you need
get max. information at min. costcommunicate with your analyst
Analysis extract informationmake correct decisions
Making decisions justify your decisionsmake informed decisionscommunicate with your analyst
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical dataWhy do Data Analysis?
Why do Data Analysis?
Statistics used correctly can assist good decisionmakingStatistics used incorrectly can misinform‘Figures don’t lie, but liars can figure’‘Figures fool, when fools figure’Your choice: user or victim!
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical dataWhy do Data Analysis?
Why do Data Analysis?
Statistics used correctly can assist good decisionmakingStatistics used incorrectly can misinform‘Figures don’t lie, but liars can figure’‘Figures fool, when fools figure’Your choice: user or victim!
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical dataWhy do Data Analysis?
Why do Data Analysis?
Statistics used correctly can assist good decisionmakingStatistics used incorrectly can misinform‘Figures don’t lie, but liars can figure’‘Figures fool, when fools figure’Your choice: user or victim!
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical dataWhy do Data Analysis?
Why do Data Analysis?
Statistics used correctly can assist good decisionmakingStatistics used incorrectly can misinform‘Figures don’t lie, but liars can figure’‘Figures fool, when fools figure’Your choice: user or victim!
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical dataWhy do Data Analysis?
Why do Data Analysis?
Statistics used correctly can assist good decisionmakingStatistics used incorrectly can misinform‘Figures don’t lie, but liars can figure’‘Figures fool, when fools figure’Your choice: user or victim!
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical dataWhy do Data Analysis?
Use, Abuse and Misuse of Statistics
On 5 October 2012 the US Bureau of Labor Statisticsreported unemployment rate dropped from 8.1% to 7.8%.
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical dataWhy do Data Analysis?
Use, Abuse and Misuse of Statistics
In November 2015, the republican Presidential Candidatein the USA, Donald Trump tweeted
“Whites killed by blacks - 81%”,citing “Crime Statistics Bureau of San Francisco”.
The US fact-checking site Politifact found that this“Bureau” did not exist, and thetrue figure is around 15%.
When confronted, Trump shrugged and said,“Am I going to check every statistic?”
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Outline
2 §1.1 What is/are Statistics?Why do Data Analysis?
3 §1.2 About dataSome languageTypes of data
4 §1.3 Displaying categorical dataBar chartsPie chartsContingency tables
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Some languageSB §1.2
Cases are the individuals or objects being describedA variable is any characteristic of a caseData are the observed values of the variablesA data set contains the observed values of thevariables for a group of individuals
ExampleGender is a variable; ‘Male’ and ‘Female’ are theobserved values of the variable (ie data)
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Some languageSB §1.2
Cases are the individuals or objects being describedA variable is any characteristic of a caseData are the observed values of the variablesA data set contains the observed values of thevariables for a group of individuals
ExampleGender is a variable; ‘Male’ and ‘Female’ are theobserved values of the variable (ie data)
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Some languageSB §1.2
Cases are the individuals or objects being describedA variable is any characteristic of a caseData are the observed values of the variablesA data set contains the observed values of thevariables for a group of individuals
ExampleGender is a variable; ‘Male’ and ‘Female’ are theobserved values of the variable (ie data)
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Some languageSB §1.2
Cases are the individuals or objects being describedA variable is any characteristic of a caseData are the observed values of the variablesA data set contains the observed values of thevariables for a group of individuals
ExampleGender is a variable; ‘Male’ and ‘Female’ are theobserved values of the variable (ie data)
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Some languageSB §1.2
Cases are the individuals or objects being describedA variable is any characteristic of a caseData are the observed values of the variablesA data set contains the observed values of thevariables for a group of individuals
ExampleGender is a variable; ‘Male’ and ‘Female’ are theobserved values of the variable (ie data)
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
A typical data setThe data here is loaded in SPSS
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Understanding the SPSS window
This is a dataset: it contains the observed values ofvariables for a group of individuals (cases)Variables are in the columnsCases are in the rowsThe variable names are: gender; height; faculty; etc.
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Understanding the SPSS window
This is a dataset: it contains the observed values ofvariables for a group of individuals (cases)Variables are in the columnsCases are in the rowsThe variable names are: gender; height; faculty; etc.
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Understanding the SPSS window
This is a dataset: it contains the observed values ofvariables for a group of individuals (cases)Variables are in the columnsCases are in the rowsThe variable names are: gender; height; faculty; etc.
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Understanding the SPSS window
This is a dataset: it contains the observed values ofvariables for a group of individuals (cases)Variables are in the columnsCases are in the rowsThe variable names are: gender; height; faculty; etc.
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
An example of a variable in the data set
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
An example of a case in the data set
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Types of data: Quantitative dataKnowing the type of data means we can select the correct techniques later
Quantitative data: (Scale in SPSS) take on numericalvalues for which mathematical operations (like +, ÷)make senseHas unit of measurement.
ExampleQuantitative variables in the SPSS data: height (in cm)
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Types of data: Quantitative dataKnowing the type of data means we can select the correct techniques later
Quantitative data: (Scale in SPSS) take on numericalvalues for which mathematical operations (like +, ÷)make senseHas unit of measurement.
ExampleQuantitative variables in the SPSS data: height (in cm)
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Types of data: Quantitative dataKnowing the type of data means we can select the correct techniques later
Quantitative data generallyhas units
ExampleHeight is quantitative. You mustindicate if it is measured in inches,metres, or cm.
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Types of data: Quantitative dataKnowing the type of data means we can select the correct techniques later
Quantitative data generallyhas units
ExampleHeight is quantitative. You mustindicate if it is measured in inches,metres, or cm.
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Types of data: Categorical data
Categorical variable: (Nominal in SPSS) values definecategories
ExampleCategorical variables in the SPSS data: gender (values‘Male’ and ‘Female’), faculty (of enrolment)
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Types of data: Categorical data
Categorical variable: (Nominal in SPSS) values definecategories
ExampleCategorical variables in the SPSS data: gender (values‘Male’ and ‘Female’), faculty (of enrolment)
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Types of data: Categorical data
Categorical (Nominal in SPSS) data is usually codedfor use in SPSS
ExampleConsider gender: Males may be coded as 1; females as2. Or females coded as 0; males as 1.
Careful: Categorical data may look quantitative if thecategories are given numerical codes
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Types of data: Categorical data
Categorical (Nominal in SPSS) data is usually codedfor use in SPSS
ExampleConsider gender: Males may be coded as 1; females as2. Or females coded as 0; males as 1.
Careful: Categorical data may look quantitative if thecategories are given numerical codes
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Types of data: Categorical data
Categorical (Nominal in SPSS) data is usually codedfor use in SPSS
ExampleConsider gender: Males may be coded as 1; females as2. Or females coded as 0; males as 1.
Careful: Categorical data may look quantitative if thecategories are given numerical codes
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Types of data in the dataset
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Types of data in the dataset
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
The variable ‘faculty’
The variable ‘Faculty’ iscategorical, with levelsdefined numericallyWe can see thesedefinitions using‘Variable Views’
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
The variable ‘faculty’
The variable ‘Faculty’ iscategorical, with levelsdefined numericallyWe can see thesedefinitions using‘Variable Views’
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
The variable ‘faculty’
The variable ‘Faculty’ iscategorical, with levelsdefined numericallyWe can see thesedefinitions using‘Variable Views’
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
The variable ‘faculty’
The variable ‘Faculty’ iscategorical, with levelsdefined numericallyWe can see thesedefinitions using‘Variable Views’
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
The variable ‘faculty’
The variable ‘Faculty’ iscategorical, with levelsdefined numericallyWe can see thesedefinitions using‘Variable Views’
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Types of data: Ordinal data
Data is ordinal if it is between categorical andquantitative
ExampleResponses such as ‘Disagree’, ‘Neutral’ and ‘Agree’ areordinal: they can be placed in a natural order, but are notquantitative
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Types of data: Ordinal data
Data is ordinal if it is between categorical andquantitative
ExampleResponses such as ‘Disagree’, ‘Neutral’ and ‘Agree’ areordinal: they can be placed in a natural order, but are notquantitative
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Types of data: Ordinal data
Data is ordinal if it is between categorical andquantitative
Example‘How often do you smoke? Never; Sometimes; Often;Regularly’. This variable is also ordinal.
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Types of data: Ordinal data
Data is ordinal if it is between categorical andquantitative
Example‘How often do you smoke? Never; Sometimes; Often;Regularly’. This variable is also ordinal.
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Understanding dataSB §1.2
Who: information about the casesWhat, and in what units: The meaning of thevariablesWhenWhereWhyHow
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Understanding dataSB §1.2
Who: information about the casesWhat, and in what units: The meaning of thevariablesWhenWhereWhyHow
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Understanding dataSB §1.2
Who: information about the casesWhat, and in what units: The meaning of thevariablesWhenWhereWhyHow
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Understanding dataSB §1.2
Who: information about the casesWhat, and in what units: The meaning of thevariablesWhenWhereWhyHow
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Understanding dataSB §1.2
Who: information about the casesWhat, and in what units: The meaning of thevariablesWhenWhereWhyHow
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Understanding dataSB §1.2
Who: information about the casesWhat, and in what units: The meaning of thevariablesWhenWhereWhyHow
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Understanding data: The SPSS data setThere’s no point having data if you don’t understand it
Who: students attending the first STA2300 lectureWhat: various: height (in cm), gender, etc.When: Semester, YearWhere: L209 (ie. on-campus students only whocame)Why: To generate some data for use in lecturesHow: A quick paper survey
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Understanding data: The SPSS data setThere’s no point having data if you don’t understand it
Who: students attending the first STA2300 lectureWhat: various: height (in cm), gender, etc.When: Semester, YearWhere: L209 (ie. on-campus students only whocame)Why: To generate some data for use in lecturesHow: A quick paper survey
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Understanding data: The SPSS data setThere’s no point having data if you don’t understand it
Who: students attending the first STA2300 lectureWhat: various: height (in cm), gender, etc.When: Semester, YearWhere: L209 (ie. on-campus students only whocame)Why: To generate some data for use in lecturesHow: A quick paper survey
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Understanding data: The SPSS data setThere’s no point having data if you don’t understand it
Who: students attending the first STA2300 lectureWhat: various: height (in cm), gender, etc.When: Semester, YearWhere: L209 (ie. on-campus students only whocame)Why: To generate some data for use in lecturesHow: A quick paper survey
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Understanding data: The SPSS data setThere’s no point having data if you don’t understand it
Who: students attending the first STA2300 lectureWhat: various: height (in cm), gender, etc.When: Semester, YearWhere: L209 (ie. on-campus students only whocame)Why: To generate some data for use in lecturesHow: A quick paper survey
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Some languageTypes of data
Understanding data: The SPSS data setThere’s no point having data if you don’t understand it
Who: students attending the first STA2300 lectureWhat: various: height (in cm), gender, etc.When: Semester, YearWhere: L209 (ie. on-campus students only whocame)Why: To generate some data for use in lecturesHow: A quick paper survey
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Outline
2 §1.1 What is/are Statistics?Why do Data Analysis?
3 §1.2 About dataSome languageTypes of data
4 §1.3 Displaying categorical dataBar chartsPie chartsContingency tables
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Distributions and graphsSB §1.3
The distribution of a variable tells us what values thevariable takes and how often it takes them
ExampleThe distribution of gender tells us how many Males andFemales are in the dataset.
Different graphs are used for different reasons anddifferent data types
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Distributions and graphsSB §1.3
The distribution of a variable tells us what values thevariable takes and how often it takes them
ExampleThe distribution of gender tells us how many Males andFemales are in the dataset.
Different graphs are used for different reasons anddifferent data types
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Distributions and graphsSB §1.3
The distribution of a variable tells us what values thevariable takes and how often it takes them
ExampleThe distribution of gender tells us how many Males andFemales are in the dataset.
Different graphs are used for different reasons anddifferent data types
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Different graphs for displaying one variable
Categorical variables Quantitative variables
Bar chart Stem-and-leaf plotPie chart Histogram
Boxplot
Which of these options we use depends on the data. . .
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Different graphs for displaying one variable
Categorical variables Quantitative variables
Bar chart Stem-and-leaf plotPie chart Histogram
Boxplot
Which of these options we use depends on the data. . .
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
One categorical variable: bar chart
Categorical variable on the horizontal axisCount or percentage on the vertical axisTitles and labels essential!Bars don’t touchCan order bars alphabetically, from largest tosmallest, etc.Don’t add an artificial third dimension
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
One categorical variable: bar chart
Categorical variable on the horizontal axisCount or percentage on the vertical axisTitles and labels essential!Bars don’t touchCan order bars alphabetically, from largest tosmallest, etc.Don’t add an artificial third dimension
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
One categorical variable: bar chart
Categorical variable on the horizontal axisCount or percentage on the vertical axisTitles and labels essential!Bars don’t touchCan order bars alphabetically, from largest tosmallest, etc.Don’t add an artificial third dimension
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
One categorical variable: bar chart
Categorical variable on the horizontal axisCount or percentage on the vertical axisTitles and labels essential!Bars don’t touchCan order bars alphabetically, from largest tosmallest, etc.Don’t add an artificial third dimension
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
One categorical variable: bar chart
Categorical variable on the horizontal axisCount or percentage on the vertical axisTitles and labels essential!Bars don’t touchCan order bars alphabetically, from largest tosmallest, etc.Don’t add an artificial third dimension
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
One categorical variable: bar chart
Categorical variable on the horizontal axisCount or percentage on the vertical axisTitles and labels essential!Bars don’t touchCan order bars alphabetically, from largest tosmallest, etc.Don’t add an artificial third dimension
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Example barchartSPSS Exercise 5 explains how to draw barcharts
The bars could beordered:
alphabeticallyby sizeby my preferenceor any way I like
since the variable graphedis categorical
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Get the little things right!We look for these things when we mark assignments
Variable names onaxesTitleScale on axesGaps between bars
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Get the little things right!We look for these things when we mark assignments
Variable names onaxesTitleScale on axesGaps between bars
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Get the little things right!We look for these things when we mark assignments
Variable names onaxesTitleScale on axesGaps between bars
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Get the little things right!We look for these things when we mark assignments
Variable names onaxesTitleScale on axesGaps between bars
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The good and bad
Good things about bar charts:used for any categorical variablesimple to construct
Bad things about barcharts:not so easy to see what fraction of the whole group aparticular category is
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The good and bad
Good things about bar charts:used for any categorical variablesimple to construct
Bad things about barcharts:not so easy to see what fraction of the whole group aparticular category is
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
One categorical variable: pie chart
Circle divided into areas of the appropriate sizePie charts need all categories (must add to 100%)
Not all categorical data can be graphed with a piechart
Don’t add an artificial third dimension
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
One categorical variable: pie chart
Circle divided into areas of the appropriate sizePie charts need all categories (must add to 100%)
Not all categorical data can be graphed with a piechart
Don’t add an artificial third dimension
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
One categorical variable: pie chart
Circle divided into areas of the appropriate sizePie charts need all categories (must add to 100%)
Not all categorical data can be graphed with a piechart
Don’t add an artificial third dimension
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
One categorical variable: pie chart
Circle divided into areas of the appropriate sizePie charts need all categories (must add to 100%)
Not all categorical data can be graphed with a piechart
Don’t add an artificial third dimension
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The good and bad
Good things about pie charts:used for showing parts of a whole
Bad things about pie charts:Pie charts are hard for the brain to understandHard to compare the sizes of pie segmentsAdding an artificial third dimension makes them verymisleadingCan’t always use a pie chart
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The good and bad
Good things about pie charts:used for showing parts of a whole
Bad things about pie charts:Pie charts are hard for the brain to understandHard to compare the sizes of pie segmentsAdding an artificial third dimension makes them verymisleadingCan’t always use a pie chart
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
An example pie chartSPSS Exercise 5 explains how to draw barcharts
This is one place wherethe pie chart can bedrawn for the dataNotice it is hard tocompare the sizes of thesegments
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
A bad pie chart
The third dimension isunnecessaryThe third dimension ismisleadingEven harder tocompare the sizes ofthe segments
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Two categorical vars: Contingency tables
When we look at two categorical variables, use acontingency tableContingency tables are also called two-way tables, orcross-tabulations (cross-tabs)Can examine contingency tables in many ways:
joint distributionsmarginal distributionsconditional distributions
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Two categorical vars: Contingency tables
When we look at two categorical variables, use acontingency tableContingency tables are also called two-way tables, orcross-tabulations (cross-tabs)Can examine contingency tables in many ways:
joint distributionsmarginal distributionsconditional distributions
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Two categorical vars: Contingency tables
When we look at two categorical variables, use acontingency tableContingency tables are also called two-way tables, orcross-tabulations (cross-tabs)Can examine contingency tables in many ways:
joint distributionsmarginal distributionsconditional distributions
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Two categorical vars: Contingency tables
When we look at two categorical variables, use acontingency tableContingency tables are also called two-way tables, orcross-tabulations (cross-tabs)Can examine contingency tables in many ways:
joint distributionsmarginal distributionsconditional distributions
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Two categorical vars: Contingency tables
When we look at two categorical variables, use acontingency tableContingency tables are also called two-way tables, orcross-tabulations (cross-tabs)Can examine contingency tables in many ways:
joint distributionsmarginal distributionsconditional distributions
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Two categorical vars: Contingency tables
When we look at two categorical variables, use acontingency tableContingency tables are also called two-way tables, orcross-tabulations (cross-tabs)Can examine contingency tables in many ways:
joint distributionsmarginal distributionsconditional distributions
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Consider an exampleSPSS Exercise 4 explains how to construct contingency tables
ExampleAll 722 members of a senior class at the Uni. of Illinoiswere asked which business major they had chosen. Hereare the data of those who responded:
Female Male TotalAccounting 68 56 124Administration 91 40 131Economics 5 6 11Finance 61 59 120Total 225 161 386
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Understanding the table
There are two categorical variables:1 Gender of the student (with values ‘Female’ and
‘Male’)2 The chosen business major (with values ‘Accounting’,
‘Administration’, etc.)
This is a 4 × 2 table: four rows and two columns(don’t count the Total row or column)
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Understanding the table
There are two categorical variables:1 Gender of the student (with values ‘Female’ and
‘Male’)2 The chosen business major (with values ‘Accounting’,
‘Administration’, etc.)
This is a 4 × 2 table: four rows and two columns(don’t count the Total row or column)
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Understanding the table
There are two categorical variables:1 Gender of the student (with values ‘Female’ and
‘Male’)2 The chosen business major (with values ‘Accounting’,
‘Administration’, etc.)
This is a 4 × 2 table: four rows and two columns(don’t count the Total row or column)
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Understanding the table
There are two categorical variables:1 Gender of the student (with values ‘Female’ and
‘Male’)2 The chosen business major (with values ‘Accounting’,
‘Administration’, etc.)
This is a 4 × 2 table: four rows and two columns(don’t count the Total row or column)
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Questions
Female Male TotalAccounting 68 56 124Administration 91 40 131Economics 5 6 11Finance 61 59 120Total 225 161 386
What proportion of students are male Finance majors?
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Questions
Female Male TotalAccounting 68 56 124Administration 91 40 131Economics 5 6 11Finance 61 59 120Total 225 161 386
What proportion of students are female Economicsmajors?
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Questions
Female Male TotalAccounting 68 56 124Administration 91 40 131Economics 5 6 11Finance 61 59 120Total 225 161 386
What proportion of students are female?
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Questions
Female Male TotalAccounting 68 56 124Administration 91 40 131Economics 5 6 11Finance 61 59 120Total 225 161 386
What proportion of Accounting majors are male?
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The joint distribution
Female Male Total
Accounting 68386 × 100 56
386 × 100
Administration 91386 × 100 40
386 × 100
Economics 5386 × 100 6
386 × 100
Finance 61386 × 100 59
386 × 100
Total 100%
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The joint distribution
Female Male Total
Accounting 17.6% 14.5%
Administration 23.6% 10.4%
Economics 1.3% 1.6%
Finance 15.8% 15.3%
Total 100%
15.3% of students are male finance majors.1.3% of students are female economics majors.
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The joint distribution
Female Male Total
Accounting 17.6% 14.5%
Administration 23.6% 10.4%
Economics 1.3% 1.6%
Finance 15.8% 15.3%
Total 100%
15.3% of students are male finance majors.1.3% of students are female economics majors.
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The joint distribution
Female Male Total
Accounting 17.6% 14.5%
Administration 23.6% 10.4%
Economics 1.3% 1.6%
Finance 15.8% 15.3%
Total 100%
15.3% of students are male finance majors.1.3% of students are female economics majors.
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The marginal distribution by rows
Female Male Total
Accounting 124386 × 100
Administration 131386 × 100
Economics 11386 × 100
Finance 120386 × 100
Total 100%
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The marginal distribution by rows
Female Male Total
Accounting 32.1%
Administration 33.9%
Economics 2.8%
Finance 31.1%
Total 100%
33.9% of students are administration majors
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The marginal distribution by rows
Female Male Total
Accounting 32.1%
Administration 33.9%
Economics 2.8%
Finance 31.1%
Total 100%
33.9% of students are administration majors
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Can draw a bar chart of major
Accnt. Admin. Econ. Fin.
Bar plot of choice of majors (n=386)
Per
cent
0
5
10
15
20
25
30
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The marginal distribution by column
Female Male Total
Accounting
Administration
Economics
Finance
Total 225386 × 100 161
386 × 100 100%
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The marginal distribution by column
Female Male Total
Accounting
Administration
Economics
Finance
Total 58.3% 41.7% 100%
58.3% of students are female
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The marginal distribution by column
Female Male Total
Accounting
Administration
Economics
Finance
Total 58.3% 41.7% 100%
58.3% of students are female
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Conditional distributions
What proportion of the Accounting majors are males?
Female Male TotalAccounting 68 56 124Administration 91 40 131Economics 5 6 11Finance 61 59 120Total 225 161 386
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Conditional distributions
What proportion of the Accounting majors are males?
Female Male TotalAccounting 68 56 124Administration 91 40 131Economics 5 6 11Finance 61 59 120Total 225 161 386
Of the 124 Accounting majors,
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Conditional distributions
What proportion of the Accounting majors are males?
Female Male TotalAccounting 68 56 124Administration 91 40 131Economics 5 6 11Finance 61 59 120Total 225 161 386
Of the 124 Accounting majors, 56 are male.
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Conditional distributions
What proportion of the Accounting majors are males?
Female Male TotalAccounting 68 56 124Administration 91 40 131Economics 5 6 11Finance 61 59 120Total 225 161 386
The answer is56
124× 100 = 45.2%
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The conditional distribution by rows
Female Male Total
Accounting 68124 × 100 56
124 × 100 100%
Administration 91131 × 100 40
131 × 100 100%
Economics 511 × 100 6
11 × 100 100%
Finance 61120 × 100 59
120 × 100 100%
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The conditional distribution by rows
Female Male Total
Accounting 54.8% 45.2% 100%
Administration 69.5% 30.5% 100%
Economics 45.5% 54.5% 100%
Finance 50.8% 49.2% 100%
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The conditional distribution by rows
Female Male Total
Accounting 54.8% 45.2% 100%
Administration 69.5% 30.5% 100%
Economics 45.5% 54.5% 100%
Finance 50.8% 49.2% 100%
The male–female ratio is similar for all majors, exceptAdmin.
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The conditional distribution by rows
Female Male Total
Accounting 54.8% 45.2% 100%
Administration 69.5% 30.5% 100%
Economics 45.5% 54.5% 100%
Finance 50.8% 49.2% 100%
45.2% of accounting majors are male.
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The conditional distribution by rows
Female Male Total
Accounting 54.8% 45.2% 100%
Administration 69.5% 30.5% 100%
Economics 45.5% 54.5% 100%
Finance 50.8% 49.2% 100%
45.2% of accounting majors are male.
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The conditional distribution by rows
Female Male Total
Accounting 54.8% 45.2% 100%
Administration 69.5% 30.5% 100%
Economics 45.5% 54.5% 100%
Finance 50.8% 49.2% 100%
50.8% of finance majors are female.
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The conditional distribution by rows
Female Male Total
Accounting 54.8% 45.2% 100%
Administration 69.5% 30.5% 100%
Economics 45.5% 54.5% 100%
Finance 50.8% 49.2% 100%
50.8% of finance majors are female.
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Can draw a stacked bar chart
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The conditional distribution by column
Female Male Total
Accounting 68225 × 100 56
161 × 100 124386 × 100
Administration 91225 × 100 40
161 × 100 131386 × 100
Economics 5225 × 100 6
161 × 100 11386 × 100
Finance 61225 × 100 59
161 × 100 120386 × 100
Total 100% 100%
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The conditional distribution by column
Female Male
Accounting 30.2% 34.8%
Administration 40.4% 24.8%
Economics 2.2% 3.7%
Finance 27.1% 36.6%
Total 100% 100%
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The conditional distribution by column
Female Male
Accounting 30.2% 34.8%
Administration 40.4% 24.8%
Economics 2.2% 3.7%
Finance 27.1% 36.6%
Total 100% 100%
Females divide into majors in a similar way to the males,apart from Administration.
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The conditional distribution by column
Female Male
Accounting 30.2% 34.8%
Administration 40.4% 24.8%
Economics 2.2% 3.7%
Finance 27.1% 36.6%
Total 100% 100%
30.2% of females study accounting.
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The conditional distribution by column
Female Male
Accounting 30.2% 34.8%
Administration 40.4% 24.8%
Economics 2.2% 3.7%
Finance 27.1% 36.6%
Total 100% 100%
30.2% of females study accounting.
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The conditional distribution by column
Female Male
Accounting 30.2% 34.8%
Administration 40.4% 24.8%
Economics 2.2% 3.7%
Finance 27.1% 36.6%
Total 100% 100%
24.8% of males study administration.
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
The conditional distribution by column
Female Male
Accounting 30.2% 34.8%
Administration 40.4% 24.8%
Economics 2.2% 3.7%
Finance 27.1% 36.6%
Total 100% 100%
24.8% of males study administration.
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
About contingency tables
The marginal distribution of the row variable comesfrom the row totalsThe marginal distribution of the column variablecomes from the column totalsThe cell counts give rise to the joint distribution of therow and column variables
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
About contingency tables
The marginal distribution of the row variable comesfrom the row totalsThe marginal distribution of the column variablecomes from the column totalsThe cell counts give rise to the joint distribution of therow and column variables
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
About contingency tables
The marginal distribution of the row variable comesfrom the row totalsThe marginal distribution of the column variablecomes from the column totalsThe cell counts give rise to the joint distribution of therow and column variables
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
About contingency tables
Each row of counts produces a conditionaldistributionEach column of counts produces a conditionaldistributionThe conditional distributions provide evidence for oragainst an association between the variables
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
About contingency tables
Each row of counts produces a conditionaldistributionEach column of counts produces a conditionaldistributionThe conditional distributions provide evidence for oragainst an association between the variables
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
About contingency tables
Each row of counts produces a conditionaldistributionEach column of counts produces a conditionaldistributionThe conditional distributions provide evidence for oragainst an association between the variables
Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Associations between variables
ExampleIs there an association between gender and major?Yes—the row conditional distributions are not the same.The female–male split is similar for all majors except forAdministration.
Female Male Total
Accounting 54.8% 45.2% 100%
Administration 69.5% 30.5% 100%
Economics 45.5% 54.5% 100%
Finance 50.8% 49.2% 100%Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Associations between variables
ExampleIs there an association between gender and major?Yes—the row conditional distributions are not the same.The female–male split is similar for all majors except forAdministration.
Female Male Total
Accounting 54.8% 45.2% 100%
Administration 69.5% 30.5% 100%
Economics 45.5% 54.5% 100%
Finance 50.8% 49.2% 100%Professor Shahjahan Khan, PhD Lecture 1
§1.1 What is/are Statistics?§1.2 About data
§1.3 Displaying categorical data
Bar chartsPie chartsContingency tables
Keeping up
Material covered today: §Module 1 (Study Book)Before next week, read §Module 2 (Study Book)Tutorials commence this week:
bring textbook; study book (or Module 1) with tutorialquestions; calculator
Assignment 1 is due next weekRemember to check the The Learning Centre websitefor additional assistance
Professor Shahjahan Khan, PhD Lecture 1