Teaching Schedule
2018–2019

Bachelor's Degree Programme in Mathematics and Statistics

Basic Studies in Mathematics and Statistics

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 the students can be able to independently carry out a small scale empirical research with the statistical software R.

**Contents**

- Basic concepts (1 lecture)
- What is statistics?
- Statistical research and its phases of work
- Population and sample
- Types of variables and scales of measurement

- Descriptive statistics (2 lectures)
- What is data?
- summary statistics (mean, median, quartiles, standard deviation and variance)
- Plotting the data (scatter plot, histogram, QQ-plot and boxplot)
- Contingency tables

- Basics of statistical inference (4-5 lectures)
- Basic concepts of probability
- Probability distributions (Binomial and Normal)
- Basic concepts in sampling theory
- Confidence intervals (mean and difference of means in independent samples)
- Hypothesis testing (mean and equality of means in independent samples, type I and type II-errors and p-values)
- Introduction to some non-parametric tests

- Linear models (3 lectures)
- Introduction to linear regression
- Basics of logistic regression
- Basics of Analysis of Variance (ANOVA)

Enrolment for University Studies

A maximum number of 50 students will be allowed in this course (70% doctoral students and 30% masters 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.

Period
(27-Aug-2018
- 21-Oct-2018)

Intermediate Studies in Mathematics and Statistics [Period I]

Enrolment for University Studies

Enrolment time has expired

Teaching

3-Sep-2018
–
15-Oct-2018

This course explores methods for the analysis of longitudinal data and latent variable methods for linear models, generalized linear models, and nonlinear models. Focusing on applications, this course explores: the analysis of repeated measures ANOVA, multivariate approaches, random-effects regression, covariance-pattern models, generalized-estimating equations and generalizations, latent variable methods including finite mixture modelling, and likelihood methods. Students will develop expertise using the SAS and R computer packages, although no previous programming experience will be assumed. Grading is based on homework and computer assignments and a project, as well as several exams.

Enrolment for University Studies

Enrolment time has expired

Teaching

17-Sep-2018
–
21-Oct-2018

Course begins with a lecture on Monday 17th of September at 2 pm.

There are three exams during the course.

**Recommended preceding studies:**

Basic courses of statistics and Regression analysis

Period
(7-Jan-2019
- 3-Mar-2019)

Intermediate Studies in Mathematics and Statistics [Period III]

Enrolment for University Studies

Enrolment time has expired

Teaching

7-Jan-2019
–
27-Feb-2019

Period
(4-Mar-2019
- 26-May-2019)

Intermediate Studies in Mathematics and Statistics [Period IV]

**Content**

It is quite common that we do not get all the information we want for our statistical analysis. For example, in medical research a person can refuse to provide certain information they feel sensitive, such as weight, substance abuse, sexual orientation etc. Particularly, missing data in longitudinal studies is more the rule than an exception. Missing data in statistical analysis causes all sorts of problems. For example, the desired statistical method cannot be directly applied; loss of information or the results obtained can be biased if the analysis is not done properly accomplished. The course introduces various missing data mechanisms and their effects on statistical analysis. In addition, it presents and evaluates some of the commonly used methods for statistical analysis with missing data. Also special methods for the analysis of longitudinal data are presented including likelihood-based methods and multiple imputation.

**Modes of Study**

Course work, exam.

Enrolment for University Studies

Enrolment time has expired

Teaching

4-Mar-2019
–
26-May-2019

This course is an intermediate study version of MTTS1 Statistical Analysis with Missing Data. The student can only complete **one of the two versions**.

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.

Enrolment for University Studies

Enrolment time has expired

Teaching

8-Apr-2019
–
17-May-2019

**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.

Degree Programme in Mathematics and Statistics

Degree Programme in Computer Sciences

Master's Degree Programmes in English

Free Choice Studies

Doctoral Programme

Open university teaching