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Science

Statistics

Hongtu NguyenFollow

- Class 1. Introduction Prof. Pieter-Paul Verhaeghe Ta. Mattias Van Hulle Ta. Dounia Bourabain
- Why study statistics? “There are lies, damned lies and statistics.” 2
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- AIM? • To read most policy reports • To evaluate measures or policies • To properly read news papers • To grasp our society • To analyse media behaviour • To make a business plan • To win sport competitions • To discern lies from statistics • … 10
- • Every Wednesday from 10am to 12am • Room: D.0.03 • Lecturer – Prof. Pieter-Paul Verhaeghe – Pieter-paul.verhaeghe@vub.be • Attendance is mandatory • Print or download the slides beforehand Practicalities Lectures 11
- • Seminars: to practice the statistics in small groups • Room: B.0.02, B.0.04, B.0.05 or B.0.06 (could be changed) • Six to seven groups: – Groups 1 & 2: Wednesday from 3pm to 6pm – Group 3: Thursday from 9pm to 12pm – Groups 4 & 5: Thursday from 1pm to 4pm – Groups 6 & 7: Friday from 9am to 12am • Check PointCarré to find out in which group you are and the exact location Practicalities Statistical seminars 12
- Mattias Van Hulle TA for Groups 1 & 4 & 6 Mattias.van.hulle@vub.be Practicalities Teaching assistants 13 Dounia Bourabain TA for Groups 2 & 3 & 5 & 7 Dounia.bourabain@vub.be
- 1. Slides of all lectures and statistical seminars • can be downloaded on PointCarré 2. Handbook ‘Statistical methods for the social sciences’ of Agresti & Finlay (2014, 4th international edition) • can be bought through the bookshop of the VUB = VUBTiek - https://my.vub.ac.be/en/bookshop Practicalities Study material 14
- Practicalities PointCarré 15
- • Two tests: 20% of the points – Test 1: during seminar 4 (25, 26 or 27th Oct) – Test 2: during seminar 8 (29, 30th Nov or 1st Dec) • Exam: 60% of the points • Assignments: 20% of the points How to pass this course? 16
- • Statistics = 6 ECTS study points = 178 hours study time – Classes and lectures: 65 hours – Study at home: 113 hours (>14 days of 8 hours) Practicalities How to pass this course? 17
- What is statistics? • Statistics = body of methods for obtaining and analysing data – Gathering the data – Summarizing the data – Interpreting the data Introduction to SPC methodology This course 18
- What is statistics? • Population: total set of subjects of interests in a research – Subjects = statistical units – E.g. people, families, schools, cities, countries… – Requires a clear definition that circumscribes the population: who’s in and who’s out? – Population population data – Number of subjects = population size – Statistical notation for population size = N 19
- What is statistics? • Sample: a smaller subset of subjects selected from the research population – simple random selection representative sample – Sample sample data – Number of sampled subjects = sample size – Statistical notation for the sample size: n – Sample size ≤ population size – n ≤ N 20
- What is statistics? • Descriptive statistics summarise the information from the data. • Inferential statistics provide predictions about a population, based on data from a sample of that population. 21
- Variables • Subjects in a population or sample vary from each other with respect to a characteristic • Data about this variability • Variable = a characteristic that can vary in value among subjects/statistical units in a sample or population 22
- Variables • Statistical notation for variables: X, Y, Z… • Each subject has a particular value on a variable: Y1, Y2, … , Yn • Example of a sample of 8 people – Sample size n = 8 – Variable Y = gender – Values of the subjects on variable Y: Y1 = man; Y2 = man; Y3 = woman; Y4 = man; Y5 = woman; Y6 = woman; Y7 = man; Y8 = woman 23
- Variables • Number of different values a variable can take: m • Measurement scale – All values the variable can take = Y1, Y2, … , Ym – Number of different values ≤ sample size – m ≤ n 24
- Variables • Example of a sample of 8 people – Sample size n = 8 – Variable Y = gender – Values of the subjects on variable Y: Y1 = man; Y2 = man; Y3 = woman; Y4 = man; Y5 = woman; Y6 = woman; Y7 = man; Y8 = woman – Number of different values m = 2 – Measurement scale = man, woman 25
- Variables • Univariate statistics: 1 variable • Bivariate statistics: association between 2 variables • Multivariate statistics: associations between more than 2 variables 26
- IN THIS COURSE Part I Univariate, descriptive statistics Part II Univariate, inferential statistics Part III Bivariate, descriptive and inferential statistics 27
- Types of variables Categorical versus metric variables • Different types of variables require different types of statistical methods • Categorical variables: values are categories – E.g. hair colour, political party preference, favorite television show… – Also known as qualitative variables • Metric variables: numerical values – E.g. net income, length, age, number of friends… – Also known as quantitative variables 28
- Type of variables: measurement level • Categorical variables – Nominal scale • Categorical values are unordered • There is no ‘higher’ or ‘lower, ‘larger’ or ‘smaller’ … • E.g. gender, eye colour, political party preference… 29
- Type of variables: measurement level • Categorical variables – Ordinal scale • Categorical values have a ‘natural’ ordening • Some values are ‘higher’ or ‘lower’, ‘larger’ or ‘smaller’ … • E.g. Social class: ‘working class’, ‘middle class’, ‘high class’ • E.g. educational level: ‘no education’, ‘primary education’, ‘secondary education’ and ‘tertiary education’ • E.g. Likert scales: ‘strongly disagree’, ‘disagree’, ‘neither disagree, nor agree’, ‘agree’, ‘strongly agree’ 30
- • Metric variables – Interval scale • Values can be ordered • Specific numerical distance or interval between values • How much higher or lower, larger or smaller… • Values can be added or subtracted • E.g. Year of birth • E.g. Temperature in Celsius Type of variables: measurement level 31
- • Metric variables – Ratio scale • Can be ordened • Specific numerical distance or interval between values • Has a meaningful or true zero point • Values can be added and subtracted • Values can be divided or multiplied ‘ratio’ scale • E.g. Number of children 0 = no children • E.g. Age 0 = no age • E.g. not year of birth 0 ≠ no year of birth • E.g. Temperature in Kelvin K 0 = no temperature • E.g. not temperature in Celsius 0 ≠ no temperature Type of variables: measurement level 32
- Natural order in values Specific numerical distance between values Can add or subtract values Can multiply or divide values True zero point Categorical 1. Nominal 2. Ordinal X Metric 3. Interval X X X 4. Ratio X X X X X 33
- • Discrete variable – A limited set of possible values – E.g. number of children, hair colour… – Values such as 0, 1, 2, 3, … • Continuous variable – An unlimited continuum of possible values – Between any two values there is always another possible value – E.g. height, age, time… – Values such as 1, 1.1111, …. , 1.1112, …, 2, … Type of variables: Discrete and continuous variables 34
- • All categorical variables are discrete • Metric variables could be either discrete or continuous Type of variables: Discrete and continuous variables 35
- Next week: Univariate descriptive statistics Contact: pieter-paul.verhaeghe@vub.be