additional resources#

On this page, you will find links to additional resources/materials relevant to the material covered in EGM101 - Skills Toolbox.

textbooks#

In preparing the materials for this module, I have used the following textbooks - they are not required reading, but if you have access through a library or the means to pick up a (used) copy, they can help fill in some of the details.

  • Illowsky, B. and S. Dean (2018). Introductory Statistics. OpenStax. ISBN 978194717205-0 [Website]

  • Caswell, F. (1995). Success in Statistics (3 rd ed.). John Murray Ltd. ISBN 978071957202-9 [Google Books]

  • Weiss, N. A. (2015). Elementary Statistics (9 th ed.). Pearson. ISBN 978032198939-0
    [Google Books]

  • Bergstrom, C. T. and J. D. West (2020). Calling Bullsh*t - The Art of Scepticism in a Data-Driven World. Penguin. ISBN 978014198705-7. [Website]

  • Huff, D. (1954). How to lie with statistics. Penguin. ISBN 978014013629-6. [Google Books]

week 5: introduction to quantitative skills#

Part 1: Introduction to Quantitative Skills

  • Illowsky and Dean, Chapter 1.1 – 1.2

  • Caswell, Chapter 4.1

  • Weiss, Chapter 1.1, 2.1

  • Can Maths Predict the Future? [The Royal Institution]

  • How Not to Fall for Bad Statistics [The Royal Institution]

  • Why you should love statistics [TED]

Part 2: Presenting Data

  • Illowsky and Dean, Chapters 2.1, 2.4

  • Caswell, Chapter 5

  • Weiss, Chapters 2.2 – 2.5

  • Huff, “The Gee-Whiz Graph” (Chapter 5)

  • Bergstrom and West, “Data Visualization” (Chapter 7)

  • Effective Communication, Better Science [Scientific American]

  • Crameri et al., 2020 [Nature Communications]

Part 3: Collecting Data

  • Illowsky and Dean, Chapter 1.2

  • Caswell, Chapter 2

  • Weiss, Chapter 1.2 – 1.4

  • Fowler, Cohen and Jarvis, Chapter 2

  • Bergstrom and West, “Selection Bias” (Chapter 6)

  • Techniques for random sampling and avoiding bias [Khan Academy]

Part 4: Frequency

  • Illowsky and Dean, Chapters 1.3, 2.1 – 2.2

  • Caswell, Chapter 4

  • Weiss, Chapter 2.3

  • Manipulating Bin Sizes [UW iSchool]

Part 5: Descriptive Statistics

Part 6: Data Distributions

  • Illowsky and Dean, Chapters 2.2, 2.6

  • Caswell, Chapters 7, 8

  • Weiss, Chapter 2.4

week 6: correlation and regression#

Part 1: Variables

  • Illowsky and Dean, Chapter 12.1

  • Caswell, Chapters 9.1, 9.2

  • Weiss, Chapter 4.2

  • Explanatory Variables Explained [Prof. Essa]

  • What is a variable? [Jim Frost]

Part 2: Correlation

  • Illowsky and Dean, Chapter 12.3

  • Caswell, Chapter 9.5 – 9.7

  • Huff, “Post Hoc Rides Again” (Chapter 8)

  • Bergstrom and West, “Causality” (Chapter 4)

  • tylervigen.com/spurious-correlations

Part 3: (Linear) Regression

  • Illowsky and Dean, Chapters 12.1 – 12.3

  • Caswell, Chapters 9.1 – 9.4

  • Fowler, Cohen, and Jarvis, Chapter 15

  • 7 Classical Assumptions of OLS Linear Regression [Jim Frost]

  • Intro to residuals and least squares regression [Khan Academy]

  • Regression line example [Khan Academy]

Part 4: The Coefficient of Determination

  • Illowsky and Dean, Chapter 12.3

  • Weiss, Chapter 4.3

  • R-squared or coefficient of determination [Khan Academy]

  • How To Interpret R-squared in Regression Analysis [Jim Frost]

  • How High Does R-squared Need to Be? [Jim Frost]

  • Five Reasons Why Your R-squared can be Too High [Jim Frost]

Part 5: Outliers

  • Illowsky and Dean, Chapter 12.6

  • 5 Ways to Find Outliers in Your Data [Jim Frost]

  • Guidelines for Removing and Handling Outliers in Data [Jim Frost]

Part 6: Interpolation and Extrapolation

week 7: probability#

Part 1: Introduction to Probability

  • Illowsky and Dean, Chapter 3.1

  • Caswell, Chapter 12

  • Weiss, Chapter 5.1

  • Probability Definition and Fundamentals [Jim Frost]

Part 2: More Probability

  • Illowsky and Dean, Chapters 3.2 – 3.4

  • Caswell, Chapter 8

  • Weiss, Chapters 5.1–5.3

Part 3: Even More Probability

  • Illowsky and Dean, Chapters 3.2 – 3.4

  • Caswell, Chapter 8

  • Weiss, Chapters 5.1–5.3

  • Independent and dependent events [Khan Academy]

Part 4: Discrete Probability Distributions

  • Illowsky and Dean, Chapter 4

  • Caswell, Chapter 13

  • Weiss, Chapter 5.4

  • The mathematical secrets of Pascal’s triangle [TED-Ed]

  • Pascal’s Triangle [Numberphile]

  • 14 Super Bowl Coin Tosses [Numberphile]

Part 5: Continuous Probability Distributions

  • Illowsky and Dean, Chapters 5, 6

  • Caswell, Chapters 8.9, 13.5–13.9

  • Weiss, Chapter 6

  • Uniform Distribution [Jim Frost]

  • Exponential Distribution [Jim Frost]

  • Empirical Rule [Jim Frost]

  • Continuous probability distribution [Khan Academy]

Part 6: The Central Limit Theorem

  • Illowsky and Dean, Chapter 7

  • Caswell, Chapter 14.1–14.2

  • Weiss, Chapters 7.2–7.3

  • Central Limit Theorem Explained [Jim Frost]

  • Assessing Normality [Jim Frost]

  • Central Limit Theorem [Khan Academy]

  • Sampling distribution of the sample mean [Khan Academy]

  • onlinestatbook.com sampling distribution simulator

week 8: statistical significance#

Part 1: Bayes’ Theorem

  • Bergstrom and West, “The Susceptibility of Science” (Chapter 9)

  • Bayes’ Theorem: What’s the big deal? [Scientific American]

  • Bayesian Inference [Seeing Theory]

  • Bayes theorem, the geometry of changing beliefs [3Blue1Brown]

  • P Values and the Prosecutor’s fallacy [UW iSchool]

Part 2: Hypothesis Testing

  • Illowsky and Dean, Chapter 9

  • Caswell, Chapter 15

  • Weiss, Chapter 9

  • Statistical inference: definition, methods & example [Jim Frost]

  • Understanding significance levels in statistics [Jim Frost]

  • Idea behind hypothesis testing [Khan Academy]

  • The method that can “prove” almost anything [TED-Ed]

  • Hack your way to scientific glory [FiveThirtyEight]

Part 3: Parametric tests

  • Illowsky and Dean, Chapters 9, 10

  • Caswell, Chapter 15

  • Weiss, Chapters 9, 10

  • What are degrees of freedom in statistics? [Minitab Blog]

  • Nonparametric tests vs Parametric tests [Jim Frost]

  • One-tailed and two-tailed tests [Khan Academy]

  • Z-statistics vs T-statistics [Khan Academy]

Part 4: ANOVA

  • Illowsky and Dean, Chapter 13

  • Weiss, Chapter 13

  • How F-tests work in ANOVA [Jim Frost]

  • Using Post Hoc Tests with ANOVA [Jim Frost]

  • Calculating total sum of squares [Khan Academy]

  • Calculating SS error and SS treatment [Khan Academy]

Part 5: Non-parametric tests

  • Weiss, Chapters 9.6, 10.4, 10.6

  • Parametric vs Non-parametric tests, and when to use them [A. Kline]

  • Parametric and Nonparametric Tests [DATAtab]

  • Parametric vs. Non Parametric Tests [Prof. Essa]

Part 6: The Chi-square Distribution

  • Illowsky and Dean, Chapter 11

  • Caswell, Chapter 15.5

  • Weiss, Chapter 12

  • Chi-square Test of Independence [Jim Frost]

  • Chi-square distribution introduction [Khan Academy]

  • Pearson’s chi-square test [Khan Academy]