NB! The deadline for individual works (for those, who want to pass the course before Christmas): December 2, 2018

 

Exam consists 3 parts: individual home works (40% of points), project (30%) and test (30%).
To pass the exam, minimum 61% of points in total are necessary.

1. Individual home works

DK_exercises_2018_eng.pdf

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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 or solve the problems) - think about statistical software and analysis methods
• ...

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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.