Mittaustulosten käsittely (T0210), 5 op

Basic information

Prerequisites

Basic cources in mathematics, the most important are statistical abilities.

Course contents (core content level)

Data processing is a computer-based course to study and analyse great data files of different types efficiently. Laboratory sessions with plenty of practical examples are carried out. The student has success to evaluate the reliability of the results critically. In addition, the use of suitable computer programs (Excel, Matlab, Statgraphics Plus) is encouraged, for it allows the student to concentrate on the interpretation of the analysis.

-Basic principles for designing experiments
-Picturing the distribution, e.g.,scattergrams, histograms, box plots, cross-tabulation
-Simple and multiple linear regression model, confidence intervals, fitted values, residuals, analysis of variance results, ANOVA-tables, testing for lack of fit
-Quantitative, qualitative and transformed variables

Course contents (additional)

Advanced regression analysis
Factor analysis
Time series

Core content level learning outcomes (knowledge and understanding)

After completing the course the student will know several methods in statistics and how to model and analyse great data files. He or she is able to criticise results of the models and experiments made.

Core content level learning outcomes (skills)

The aim is to achieve a problem-oriented ability to analyse and model individually problems arising in engineering and computer science using techniques described below.

Recommended reading

Neter-Kutner-Nachtsheim-Wasserman: Applied Linear Statistical Models,
H. Karttunen, CSC, Datan käsittely, and material from teacher

Teaching and learning strategies

Laboratory 42 h
Project 24 h
Self study 54 h

Teaching methods and student workload

Assessment weighting and grading

The work in the laboratory is the must.

Related competences of the degree programme