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Kreutzwaldi 62, A-213
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Loengukursused -> Matemaatiline statistika ja modelleerimine
Mathematical statistics and
modelling
[Matemaatiline
statistika ja modelleerimine] 
(DK.0007; 5 EAP; spring; E)
EMÜ
Doctoral School
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NB!
The deadline for individual works: April 28, 2013
Exam consists
3 parts: individual home works (40% of points), project/presentation
(30%) and test (30%).
1. Individual
home works
DK_exercises_2013_eng.pdf
2. Project
or presentation about own research:
-
research
area; interested questions, problems, hypothesis;
-
how
is planned to perform the experiments (or how the experiments
were/are perfomed), collect data, make questionaries, measure
something, ...;
-
how many experiments/studied objects, why so many/few, how
selected, ...
- how should
these data be analysed (or discussion about this, what kind
of results are requested to answer the questions, solve the
problems, ....
- ...
3. Test
To perform
the test successfully, you should understand
- how are
calculated basic descriptive characteristics like aritmetic
mean (average), median, standard deviation, variance, quartiles;
when is preferred average and when median; how differ mean and
median in case of asymmetric distribution;
- what descibes/measures
standard error, confidence interval, significance level, p-value;
- how looks
like the density function of normal distribution, what are the
basic properties of the normal distribution;
- when it
is suitable to use the t-test, Mann-Whitney or Wilcoxon test,
sign test, F-test, Kolmogorov-Smirnov test, chi-square test,
Fisher exact test; what is the Bonferroni correction/method;
- for what
kind of analyses should be used two-way frequency tables, Pearson
and Spearman correlation coefficients, linear regression, analysis
of variance (ANOVA), general linear models (GLM);
- when are
used logistic models (logistic regression), probit-models (probit-regression);
what is the odds ratio (OR), how is the odds ratio related with
the two-way frequency table or logistic regression;
- what is
measuring the model standard error, multiple correlation coefficient,
determination coefficient (R2);
- what is
the meaning of linear model's parameters (regression coefficient,
parameters of analysis of variance model), what are the contrasts;
- for what
kind of analyses should be used multivariate analysis methods
like cluster analysis, principal component analysis, discriminant
analysis.
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