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Applied Statistics deals with basic statistical methods used in pharmacy. It gives an overview of the most common statistical tests and methods, with an emphasis on choosing the right type of statistical test and the resulting interpretation of the results. Students will become familiar with basic statistical functions in common software (mainly Prism GraphPad) using concrete examples from pharmacy and related fields.
Last update: Duintjer Tebbens Erik Jurjen, doc. Dipl.-Math., Ph.D. (12.09.2025)
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Conditions for granting the credit test – Applied statistics
Last update: Duintjer Tebbens Erik Jurjen, doc. Dipl.-Math., Ph.D. (12.09.2025)
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Recommended:
Last update: Duintjer Tebbens Erik Jurjen, doc. Dipl.-Math., Ph.D. (12.09.2025)
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The guarantor lectures, teachers conduct seminars. Consultation may be based on a personal, telephone or email order.
Last update: Duintjer Tebbens Erik Jurjen, doc. Dipl.-Math., Ph.D. (11.10.2024)
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There is no exam for this course. Last update: Duintjer Tebbens Erik Jurjen, doc. Dipl.-Math., Ph.D. (10.10.2024)
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- t-tests and nonparametric counterparts
- Chi-square and exact Fisher test
Last update: Duintjer Tebbens Erik Jurjen, doc. Dipl.-Math., Ph.D. (10.10.2024)
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The Applied Statistics course expands general knowledge of descriptive statistics and introduces students to mathematical statistics, building on the knowledge gained in the course Mathematics. After completing the course, students will be able to use the following statistical procedures: T-tests, ANOVA, linear and logistic regression, chi-square test and Fisher's exact test, non-parametric analogues of t-tests and ANOVA, normality and log-normality tests, survival analysis.
Learning outcomes: After completing the course, the student is able to correctly perform the following tasks:
- explain the principles of hypothesis testing and explain variability in data in ANOVA and linear regression analyses; - select the correct type of statistical analysis for a given issue and assess the suitability of the procedure based on data properties such as normality and sample size; - calculate their coefficients in regression analyses using software, especially the slope, odds ratio and hazard ratio; - interpret the results based on p-values and confidence intervals; - illustrate the results by creating appropriate graphs and figures;
Last update: Duintjer Tebbens Erik Jurjen, doc. Dipl.-Math., Ph.D. (12.09.2025)
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