Dr Oliver Johnson

This is the web page for the Level 1 course 'Statistics 1' lectured by Dr Oliver Johnson of the School of Mathematics of Bristol University.

Lectures take throughout teaching block 2.

Tuesdays 1 (Powell LT Physics), Thursdays 10 (Pugsley LT Queen's Building).

Office hours: 2.30-3.30 Tuesdays I will be in my office (Room 3.17) if you would like to ask about the course - or send me an email at other times.

A more detailed description of the course including syllabus, suggested reading and unit aims.

Lecture notes

Problem booklet

Sheet 2 talks about the Disasters data set. There are a couple of typos on the version of the sheet handed out. The best bet is to type
source("http://www.stats.bris.ac.uk/~maotj/teach/disasters.R")
gaps <- disasters$gap[2:121]

Solutions will be handed out in due course, in paper form only, in lectures.

This material is copyright of the University of Bristol unless explicitly stated otherwise. It is provided exclusively for educational purposes at the University and is to be downloaded or copied for your private study only.

Downloading R to your own computer

R data set for the course.

R: a self-learn tutorial.

Using R in the Computing Lab.

Common parametric families and plots of their density functions.

The recommended textbook for the unit is:
Mathematical statistics and data analysis by JA Rice

A good introductory text for further reading about R is:
Introductory Statistics with R by Peter Dalgaard (e-book available through UoB)

Other Statistics books:

• Introduction to the practice of statistics by DS Moore and GP McCabe
• Mathematical statistics by JE Freund
• Probability and statistics by MH DeGroot
• Statistical theory by BW Lindgren
• Introduction to the theory of statistics by AM Mood, FA Graybill and DC Boes
• Introduction to mathematical statistics by RV Hogg and AT Craig
• Introduction to probability theory and statistical inference by HJ Larson
• Exploratory data analysis by JW Tukey

Lecture 1: Visualisations from Information is Beautiful e.g. Truth about twitter and Peak breakup times .

Distribution of income data.

Lecture 2: Letter frequency counts in the age of Big Data

Lecture 3: Benford's Law: (i) Financial crisis example (ii) Online calculator

Lecture 4: What is the distribution of twitter followers? Challenge: anyone like to repeat the analysis for Bristol?

Lecture 5: Larry Summers New York Times article (see point 6 in particular)

Lecture 6: Quantifying uncertainty in forecasts: Metcheck rainfall probabilities, Bank of England inflation predictions, Betfair implied probabilities

Lecture 7: Moneyball trailer

Lecture 8: Regression by eye (requires Java)

Lecture 9: New York Times article and Blog post on the value of statistics

Lecture 10: Quincunx

Lecture 11: Chance News

Lecture 12: Significance magazine (including free app) -- e.g. Olympics prediction

Lecture 13: Cheltenham Festival special: Two Horse racing papers

Lecture 14: 538 -- see also Wikipedia page

Lecture 15: Relevant XKCD cartoons: Null hypothesis | Statistical tests | Correlation vs causation

Lecture 16: Dodgy p-values