Teachers:

• Zheying Zhang (SDE)
• Jaakko Peltonen (CBDA)
• Martti Juhola (CBDA)

Maisteriohjelman, opetuksen ja ohjauskäytäntöjen esittely kandivaiheessa oleville opiskelijoille.

Osallistuminen opetukseen, viikkoharjoitukset ja harjoitustyö sekä tentti.

Enroll on the course TIETA6 Tietorakenteet in NettiOpsu

Self studying, weekly excercises, practical work, and exam.

Course Contents

Matrix basic operations, random number generation, cross-validation, Jackknife, Bootstrap, use of the R program

Modes of Study and Registration

Independent work, only for CBDA-students.

Please see the Moodle page for details and instructions.

Contact teaching (lectures and excercises) is only in Finnish. For details, see the teaching schedule of the finnish version (MTTTA14 Tilastotieteen matriisilaskenta ja laskennalliset menetelmät). The lectures are based on study material which is available in English.

Students can get support for their studies in Statistics from the workshop.

The idea is that students work independently or in groups with the problems that have arisen. The workshop has a teacher and you can ask for the advice from him. However, the idea is that the teacher will not solve the problems for you. The workshop does not provide any compensation of the solved exercises.

Period I: especially for students taking MTTTA14 Matrices for Statistics and Computational Methods.

Period II: escpecially for students taking MTTTA2 Matemaattisen tilastotieteen perusteet.

Period III: escpecially for students taking MTTTA4 Statistical Inference 1.

If you are considering to begin the thesis work, it is a good time to enrol. It is recommended that you'd begin the seminar before you have agreed on the topic and/or supervisor of the thesis.

Students can enrol any time, not just at the beginning of the period. If enrolment is not open, contact the teacher by email.

Enrollment for the whole academic year 2017-2018:

Modes of study

- Lectures
- Exercises (independent work)
- Exam

1. Check the courses in Study offerings.
2. Update your personal study plan (fill in the TUT course) and send it to your study advisor for approval.
3. Apply for study right in Service for Cross-Institutional Studies
4. Follow the instructions.

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

1. Basic concepts (1 lecture)
   1. What is statistics?
   2. Statistical research and its phases of work
   3. Population and sample
   4. Types of variables and scales of measurement
2. Descriptive statistics (2 lectures)
   1. What is data?
   2. summary statistics (mean, median, quartiles, standard deviation and variance)
   3. Plotting the data (scatter plot, histogram, QQ-plot and boxplot)
   4. Contingency tables
3. Basics of statistical inference (4-5 lectures)
   1. Basic concepts of probability
   2. Probability distributions (Binomial and Normal)
   3. Basic concepts in sampling theory
   4. Confidence intervals (mean and difference of means in independent samples)
   5. Hypothesis testing (mean and equality of means in independent samples, type I and type II-errors and p-values)
   6. Introduction to some non-parametric tests
4. Linear models (3 lectures)
   1. Introduction to linear regression
   2. Basics of logistic regression
   3. Basics of Analysis of Variance (ANOVA)

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.

Content

• Lecture I (75 minutes): Estimation based on (i) Complete Sample, (ii) Failure-Truncated Sample; Quiz I (30 minutes): Based on Lecture I.
• Lecture II (60 minutes): Estimation based on Time-truncated Sample; Quiz II (30 minutes): Based on Lecture II.
• Lecture III (60 minutes): Prediction of Future Observation(s) Based on Exponential Samples; Quiz III (30minutes): Based on Lecture III.

Modes of study

Participation in course work and a practical work.

Please also note visiting lecture:

Professor (retired), Bikas Sinha, Indian Statistical Institute

Topic: “F AM NOT LICKED” – The Twelve Penny Problem with Applications

• 2.11.2017 at 14.15–15.00, Pinni B3109.

Are most published research findings false (Ionnadis, 2005, Open Science Collaboration, 2015)? The course deals with the interpretations of and possible solutions to the lack of replicating results in empirical research (e.g. social psychology and cancer research). As a result of the lack of successful registered replications, a growing number journals such as Psychological Science are favouring practices such as direct replications and registered reports. The course gives a hands-on introduction to two methods which are thought to improve the reliability and replicability of empirical research: p-curve analysis and pre-registration.

The course:

• Introduces participants to the ongoing conversation about the replicability of empirical research
• Introduces participants to different interpretative frameworks of p-values and statistical significance (Null Hypothesis Significance Testing, Fisher, Neyman-Pearson, Bayes)
• Provides participants with skills for pre-registration and p-curve analysis

Masters Degree students of CBDA-programme: course can be included in the advanced studies of CBDA (Statistical Data Analytics). For details, contact your Personal Study Plan teacher.

Modes of study

- Lectures
- Exercises (independent work)
- Exam

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.

TUT-students, to enrol please send e-mail to: mtt-studies@uta.fi

TTY:n opiskelijat hakeutuvat kurssille JOO-opiskelijoina. Ilmoittaudu ensin sähköpostitse osoitteeseen: mtt-studies@uta.fi

Students should bring their own laptops in practicals.

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 advanced methods are presented, as well as special methods for the analysis of  longitudinal data including likelihood-based methods and multiple imputation.

Modes of Study

Course work, exam.