Lecturer
Herwig Friedl .

Timing
3 hours weekly during Summer Terms.

Teaching Aims
To provide further results and techniques in the field of statistics. To improve the student's ability to use this theory in data analysis and statistical modelling. To introduce the statistical software package R.

Learning Objectives
Students should be able to analyse small studies by their own. They should be also familiar with the concepts of exploratory data analysis, probability theory and classical statistical models.

Contents Part I: Linear Regression
Introduction: Exploratory Data Analysis. Repetition: Parameter Estimation, Confidence Intervalls, Tests. (1) Simple Linear Regression. (2) Inference in Regression Analysis. (3) Diagnostics. (4) Simultaneous Inference. (5) Matrix Algebra. (6) Multiple Linear Regression. (7) Extra Sums of Squares (ANOVA). (8) (Building the Regression Model). (11) Qualitative Predictor Variables.

Contents Part IIa: Design of Experiments
Repetition: Linear Regression Analysis. (1) Analysis of Variance. (2) More about Single Factor Experiments. (3) Randomized Blocks, Latin Squares. (4) Factorial Designs. (5) 2^k Factorial Designs. (6) Blocking and Confounding.

Contents Part IIb: Applied Multivariate Statistical Analysis
(1) Aspects of Multivariate Analysis. (2) Principal Components Analysis (PCA). (3) Factor Analysis. (4) Discrimintaion and Classification.

Prerequisites
Mathematics I and II, Probability and Statistics.

Classes begin on March 6th, 2017 (10.00). Afterwards we meet on

• Thursday, 13.00 - 14.30, Seminar Room Statistic, Kopernikusgasse 24/III
• Thursday, 15.00 - 16.30, Seminar Room Statistic, Kopernikusgasse 24/III

Literature

• Müller, W. (2000): Statistik für Maschinenbauer (Teil II), Lecture Notes ;-)
• Neter, J., Kutner, M., Nachtsheim, C., Wassermann, W. (1996): Applied Linear Stistical Models (4th Edition), McGraw-Hill Boston.
• Montgomery, D.C. (1997): Design and Analysis of Experiments (4th Edition), Wiley New York.

Tables

• Tables with Quantiles of the N(0,1), t-, chi^2-, and F-Distribution

Slides
The introductory part of the course is about principal concepts in data analysis and in statistics. The pdf files of my slides used in class are provided below:

• Module 2: Exploratory Data Analysis,
• Module 3: Statistical Inference.

• Chapter 1: Simple Linear Regression,
• Chapter 2: Inference in Regression Analysis,
• Chapter 3: Diagnostics,
• Chapter 4: Simultaneous Inference,
• Chapter 5: Matrix Algebra,
• Chapter 6: Multiple Linear Regression,
• Chapter 7: Extra Sums of Squares (ANOVA),
• Chapter 11: Qualitative Predictor Variables,

Part II is about two different topis in Applied Statistics:
The first topic is about the Design and Analysis of Experiments. I will use the book by Montgomery (Chapters 3-8) and the pdf file of my slides used in class is provided. This most recent version includes already slides about the last part on Blocking and Confounding!

• ANOVA, More about Single Factor Experiments
• Randomized Blockes and Latin Squares
• Introduction to Factorial Designs
• The 2**k Factorial Design
• Blocking and Confounding in the 2**k Factorial Design
• R code for Tukey's Test on Non Additivity (from Dr. Hee-Seok Oh)

• Chapter 1. Aspects of Multivariate Analysis
• Chapter 8. Principal Components Analysis (PCA)
• Chapter 9. Factor Analysis
• Chapter 10. Discrimintaion and Classification

Software Information
We will use 'R', the freeware version of S-Plus. Here are some helpful links about this statistical package:

• An Introduction to R (W.N. Venables & D.M. Smith)
• Useful R links (by Brett Presnell)
• R code used in the Demo Session
• Introductory Guide to S-Plus (by B.D. Ripley - postscript file)
• Regression by Simulation (R file)

Data

• Aimu, and Data Definition
• Houses, and Data Definition
• Prestige, Data Definition and Modelling Instructions, and my solution