General description

Knowledge about statistical methods and data analysis is of great importance in almost any field of research. In this course, general concepts of statistics will be provided so that students can be able to independently carry out a small scale empirical research with the statistical software SPSS or R. After the course, students should be familiar with the basic concepts of statistics, ranging from descriptive statistics, basic inference (estimation, confidence intervals and hypothesis testing), linear models (analysis of variance, simple and multiple linear regression), non-parametric tests and logistic regression.

Space is limited in this course due to computer room capacity. Priority will be granted for the first enrolments, based on the proportions: 60% PhD students and 40% for BSc and MSc students.

Please note that this course cannot be included inside the minimum 120 ECTS of Master's Degree Programme in CBDA (basic level course).

MTTTP1 Tilastotieteen johdantokurssi lectured in period I, II or III-IV is recommended for Finnish students.

This course will give a detailed overview of statistical models for modern regression and classification with emphasis on applications. A number of examples and case studies will be examined. This course will cover a range of models from linear regression through various classes of more flexible models including fully nonparametric regression models. We will consider both regression and classification problems. Methods such as splines, additive models, multivariate adaptive regression splines (MARS), neural networks, classification and regression trees (CART), linear and flexible discriminant analysis, generalized additive models, nearest- neighbor rules and learning vector quantization will be discussed.

Recommended preceding studies:

Basic courses of statistics and Regression analysis.

Please note

Students who have completed course MTTA2 Ei-parametrinen regressio can not get full credits of this course because some of the contents overlap.