Luchsinger Mathematics AG

Crash Course in Statistics for Neuroscience Center Zurich, University of Zurich, Fall 2010 [2 or 2.5 credit points]

Without SPSS-Basic Course (only Luchsinger & Schwarz): 2 CP
With SPSS-Basic Course (Luchsinger, Meier & Schwarz): 2.5 CP
If you miss more than 3 h (half day) of Course, you don't get any CP's - to get 0.5 CP for SPSS-Basic Course, you must follow the entire SPSS-Basic Course without any exception!

Time:

Date Time Lecturer Subject Venue
July 15, July 16, July 20 0900-1200 & 1300-1600 Dr. Christof Luchsinger Theory Uni Irchel, Y 16 G 05
July 21 0900-1200 Dr. Christof Luchsinger Theory Uni Irchel, Y 16 G 05
July 27 0900-1200 & 1300-1600 David Meier SPSS-Basic Course, Part 1 Uni Irchel, Y 01 F 08
July 28 0900-1200 David Meier SPSS-Basic Course, Part 2 Uni Irchel, Y 01 F 08
July 29 0900-1200 Dr. Jürg Schwarz SPSS-Applications (Data Analysis), Part 1 Uni Irchel, Y 01 F 08
July 30 0900-1200 & 1300-1600 Dr. Jürg Schwarz SPSS-Applications (Data Analysis), Part 2 Uni Irchel, Y 01 F 08

Lecturers:

Aims of the Course: Participants...

  1. have basic knowledge of probability theory
  2. can solve simple statistical problems without help
  3. can reconstruct the train of thought of correct solutions to more complicated problems
  4. have basic knowledge of statistics, enabling them to familiarize themselves with more advanced topics in the literature given below & follow more advanced courses at www.math-jobs.com/conf.html
  5. have SPSS-Documentation to methods treated (no complete introduction!)
  6. 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. Finaly, 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.

  1. Probability
  2. Random Variables
  3. Expectations
  4. Selected Probability Distributions; please also visit Online-Demos Distributions
  5. Law of Large Numbers

    Summary chapters 1-5
    Solutions to Exercises Chapters 1-5

  6. Estimators and Confidence Intervals
  7. Test theory (incl 1 way ANOVA)
  8. Regression

Prerequisits:

Administration/Registration: Nadia Mouci, mouci@neuroscience.uzh.ch, Tel. (+41) 044 635 33 81

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

Literature: Script. Further Literature at www.math-jobs.com/lit.html.

In particular the following 2 books:

Something in English:

Links:

Webmaster: Dr. Christof Luchsinger / jobs@math-jobs.com