Crash Course in Statistics for Neuroscience Center Zurich, University of Zurich, Summer 2016 [2.5 credit points]

**If you miss more than 3 h (half day) of Course, you don't get any CP's!**

**Time:**

Date |
Time |
Lecturer |
Subject |
Venue |

June 27 |
0900-1200 & 1300-1600 |
Dr. Christoph Luchsinger |
Theory |
Y27 H35/36 |

June 28 |
0900-1200 & 1300-1600 |
Dr. Christoph Luchsinger |
Theory |
Y27 H35/36 |

June 30 |
0900-1200 |
Dr. Christoph Luchsinger |
Theory |
Y27 H35/36 |

July 1 |
0900-1200 |
Dr. Christoph Luchsinger |
Theory |
Y27 H35/36 |

July 4 |
0900-1200 |
Dr. Christoph Luchsinger |
Theory |
Y27 H35/36 |

July 4 |
1300-1600 |
Dr. Daniel Stekhoven and Dr. Adin Ross-Gillespie |
Introduction to R |
Y27 H35/36 |

July 5 |
0900-1200 & 1300-1600 |
Dr. Daniel Stekhoven and Dr. Adin Ross-Gillespie |
Performing statistical analyses in R |
Y27 H35/36 |

July 6 |
0900-1200 & 1300-1600 |
Dr. Daniel Stekhoven and Dr. Adin Ross-Gillespie |
Diagnosis, visualisation & workshop |
Y27 H35/36 |

**Lecturers:**

- Dr. Christoph Luchsinger, 043 243 15 08, chris@all-acad.com
- Dr. Daniel Stekhoven, office@quantik.ch
- Dr. Adin Ross-Gillespie, ross-gillespie@quantik.ch

**Aims of the Course: Participants...**

- have basic knowledge of probability theory
- can solve simple statistical problems without help
- can reconstruct the train of thought of correct solutions to more complicated problems and adapt them to their own problems with same structure
- have basic knowledge of statistics, enabling them to familiarize themselves with more advanced topics in the literature given below
- have R-Documentation to methods treated (no complete introduction!)
- see limitations of statistical reasoning

**We are going to omit (among other topics):** descriptive statistics (important, read yourself, too time-consuming);
Design of Experiments (important, too individual, too time-consuming, we touch some of it); Quality control

**The way I teach:** A script is online (below). Please print it out.
Therefore you are not going to lose time just copying from the blackboard or OHP. Instead, we are going to solve many
problems in class. For example, I will first motivate the term of "Mean" (or "Variance"), then give the definition, then I
solve 1 or 2 problems using this new statistical concept, then maybe one problem will be solved together in class, then you
have to solve some problems yourself. Finally, at the end of the day, there is time to solve additional exercises with me being
present in the class room. Teaching will be very interactively. I will omit almost all proofs!

The binomial distribution (a discrete random variable) and the normal distribution (a continuous random variable) will be
treated broadly. We will present the theory of chapter 6 and 7 using the binomial and normal random
variables.

Please bring light pens (Leuchtstifte) in the following three colours to the course: Blue="Structure", Red="Danger" and
Green="important, learn by heart".

**Contents / Downloads (Script):**

First 5 chapters are a necessary, theoretical and mathematical basis; data and applications follow in chapters 6 - 10.

- Probability
- Random Variables
- Expectations
- Selected Probability Distributions
- Law of Large Numbers
Summary chapters 1-5

Solutions to Exercises Chapters 1-5 - Estimators and Confidence Intervals
- Test theory (incl 1 way ANOVA)
- Regression

- Solutions to Exercises Chapters 6-8
Useful stuff:

- Guidelines
- Incongruence between test statistics and P values in medical papers
- Why Most Published Research Findings Are False
- Bradford-Hill-Kriterien für Kausalität in der Medizin (useful)
- Sir Karl Raimund Popper (fundamental)

**Prerequisits:**

- BSc/MSc or PhD Natural Science, Pharmacy, Medicine.
- and
- 1 Semester of Introductory Probability Theory and Statistics and/or
- extremly motivated and numbers loving - participants must go through tough theory so that they can follow more advanced courses later on

- Suggested preparation: Stahel Chapter 1-6; or Cartoon Guide Chapter 1-5 (easy to read).

**Administration/Registration:** Heidi Gauss, hgauss@neuroscience.uzh.ch, Tel. 044 635 33 82

**Consulting hours:** breaks; follow-up treatment (only topics treated in this course) via E-Mail and phone.

**Literature:** Script and in particular the following books:

- easy to read: The cartoon guide to Statistics by L. Gonick and W. Smith, HarperPerennial - same in German: Statistik in Cartoons L. Gonick und W. Smith
- more serious: Statistische Datenanalyse by W.A. Stahel (ETH Zürich), Vieweg
- Something in English: Fundamental Concepts in the Design of Experiments by C.R. Hicks, Oxford University Press, New York, 1993

**Links:**

- Course each summer for basic Maths (choose the first course)
- Script for BSc students of Science at Stochastik für die Naturwissenschaften
- List of Consulting Services
- Consulting Services at

Webmaster: Dr. Christoph Luchsinger / chris@all-acad.com