Students will acquire the following knowledge and skills:

Data Interpretation and Analysis: Ability to interpret and analyze data using measures of central tendency and variability. Students will be skilled in extracting meaningful insights from datasets, understanding the importance of both population and sample in statistical analysis.

Understanding and Application of Probability Distributions: Proficiency in understanding and applying key probability distributions, particularly the normal and Z-distributions. This includes the ability to recognize and use these distributions in relevant scenarios.

Mastery of Sampling Techniques and the Central Limit Theorem: Competence in understanding and applying sampling techniques and the central limit theorem. Students will be able to explain why and how sample means tend to follow a normal distribution, regardless of the shape of the population distribution.

Confidence Interval and Hypothesis Testing Skills: Ability to construct and interpret confidence intervals, particularly using the Student’s distribution. Students will also be adept in conducting hypothesis tests, understanding the concepts of critical regions and p-values.

Advanced Data Analysis Techniques: Skills in advanced data analysis techniques, including t-tests, analysis of variance, and linear regression (both simple and multiple). This includes the ability to test hypotheses about means, analyze the variance in different groups, and understand relationships between variables.

Design of Experiments: Knowledge of designing experiments, including factorial designs, blocking in factorial designs, and fractional designs. Students will be able to design, execute, and analyze experiments in a methodical and statistically sound manner.

R: Rumsey D. J. - Statistics For Dummies, For Dummies, 2016, ISBN 1119293529

R: Rumsey D. J. - Statistics II For Dummies, For Dummies, 2021, ISBN 1119827396

A: Motulsky H. - Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking, Oxford University Press, 2017, ISBN 0190643560

A: Venables R. W. N., Smith D. M. and the R Core Team: An Introduction to R. 2023. (available for free)

Online course materials at UCT e-learning.

Coursera video course Basic Statistics

StatQuest video lectures by Josh Starmer

Online textbooks:

OpenIntro Statistics

OnlineStatBook

Hyperstat

StatPrimer

ústní

1. Descriptive statistics - graphs, measures of central tendency

   Introduction to basic statistical concepts; understanding and using graphical representations; measures of central tendency like mean, median, and mode.

2. Descriptive statistics - measures of variability, population and sample

   Exploring measures of spread such as range, interquartile range, variance and standard deviation; distinguishing between population and sample statistics.

3. Random variable, normal distribution, Z-distribution

   Introduction to random variables and probability distributions; in-depth study of the normal distribution and its applications; understanding stndardization and Z-distribution.

4. Sampling distribution, central limit theorem

   Understanding the concept of sampling distribution; exploring the central limit theorem and its significance in statistics.

5. Student's t-distribution, confidence interval

   Introduction to Student's t-distribution for small samples; learning to construct and interpret confidence interval around the mean.

6. Hypothesis testing, critical region, p-value

   Fundamentals of hypothesis testing; null and alternative hypotheses; understanding critical regions and p-values and their roles in hypothesis testing; one-sided and two-sided tests.

7. Testing hypotheses about a mean - t-test

   Detailed study of the t-test for comparing means; one-sample and two-sample t-test; dependent and independent sample t-tests; t-test applications and interpretations.

8. Analysis of variance (ANOVA)

   Introduction to ANOVA for comparing more than two groups; understanding between-group and within-group variations; F-ratio and F distribution

9. Covariance, correlation, simple linear rgeression I

   Exploring the concepts of covariance and correlation; basics of simple linear regression analysis.

10. Simple linear regression II, multiple linear regression

    Advanced topics in linear regression - decomposition of variability, multiple linear regression models.

11. Design of experiments (DoE) - factorial designs

    Introduction to experimental design; understanding and applying factorial designs in experiments.

12. Design of experiments (DoE) - blocking a factorial design

    Techniques for blocking in factorial designs to improve experimental accuracy.

13. Design of experiments (DoE) - fractional designs

    Study of fractional factorial designs and their applications in complex experiments.

14. Summary of the covered material

    A comprehensive review of all topics covered, ensuring a solid understanding of statistical data analysis principles and practices.

Mathematics

The credit is given on the completion of an individual project.

Zápočet se uděluje na základě vypracování individuálního projektu.