Basic statistics for doctoral students - GDZST01
Title: Basic statistics for doctoral students
Guaranteed by: Department of Biophysics and Physical Chemistry (16-16110)
Faculty: Faculty of Pharmacy in Hradec Králové
Actual: from 2019
Semester: both
Points: 0
E-Credits: 0
Examination process: oral
Hours per week, examination: 1/0, Ex [HT]
Capacity: winter:unknown / unknown (unknown)
summer:unknown / unknown (unknown)
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
Key competences:  
State of the course: taught
Language: English
Teaching methods: full-time
Teaching methods: full-time
Level:  
Note: course is intended for doctoral students only
course can be enrolled in outside the study plan
enabled for web enrollment
you can enroll for the course in winter and in summer semester
Guarantor: doc. Dipl.-Math. Erik Jurjen Duintjer Tebbens, Ph.D.
Examination dates   WS schedule   
Annotation -
The course Basic statistics for doctoral students addresses traditional and modern statistical methods popular in pharmaceutical research. Experience with basic statistical procedures and resulting interpretation of results is expected.
Last update: Duintjer Tebbens Erik Jurjen, doc. Dipl.-Math., Ph.D. (13.08.2019)
Literature

Obligatory:

  • Li Wan Po, Alain. Statistics for pharmacists. London: Blackwell Sci., 1997, 252 s. ISBN 0-632-04881-6.

  • Box, George E. P. Hunter, J. Stuart Hunter, William Gordon. Statistics for experimenters : design, innovation, and discovery. Hoboken, N.J.: Wiley-Interscience, 2005, 633 s. ISBN 0-471-71813-0.

Last update: prepocet_literatura.php (19.09.2024)
Syllabus -

Overview of basic concepts

Descriptive and mathematical (inductive) statistics. The most common statistical parameters. Normal distribution and the central limit theorem. T-, Chi-squared- and F-distributions. Sampling and statistical independence, correlation. 

Basic tests, confidence intervals

The z-test and various types of t-test, the F-test for equality of variances. One- and two-sided tests, ANOVA for one and more factors. Decomposition of variability using different types of sums of squares. Confidence intervals and their relation to hypothesis tests.  

Regression models

Their purpose and ways to use them. Linear regression and logistic regression. Multivariate models. Survival analysis, Cox models.

Tests for categorical (qualitative) variables

Chi-squared test of independence (Pearson‘s chi-squared test).  Dummy coding for linear and logistic regression. 

Pairing

Paired tests, randomization, (randomized) blocking.

Nonparametric methods

The usage of nonparametric methods. Permutation tests, Fisher‘s exact test, the rank-sum test (Mann-Whitney U-test), Wilcoxon‘s test, the Kruskal-Wallis test and the Friedland test.

Classification tasks

Clustering, (linear) discriminant analysis.

Planning of experiments

Design of Experiments (DoE), factorial designs, PCA, statistical power.

Processing of results

Verifying the assumptions, exclusion of outliers, missing values, meta-analysis.

Last update: Duintjer Tebbens Erik Jurjen, doc. Dipl.-Math., Ph.D. (13.08.2019)