470-2404/02 – Introduction to Statistics (ZS)
Gurantor department | Department of Applied Mathematics | Credits | 4 |
Subject guarantor | Ing. Martina Litschmannová, Ph.D. | Subject version guarantor | Ing. Martina Litschmannová, Ph.D. |
Study level | undergraduate or graduate | Requirement | Compulsory |
Year | 3 | Semester | winter |
| | Study language | English |
Year of introduction | 2019/2020 | Year of cancellation | |
Intended for the faculties | FEI | Intended for study types | Bachelor |
Subject aims expressed by acquired skills and competences
This subject is an introductory course of statistics. The aim of the course is to develop sufficient knowledge of statistical tools and procedures, understanding of the underlying theory on which the procedures are based, and facility in the application of statistical tools to enable the student to incorporate sound statistical methodology into other areas of his or her own work.
Teaching methods
Lectures
Tutorials
Project work
Summary
Statistics is an important field of math that is used to analyze, interpret, and predict outcomes from data. This course will teach students the basic concepts used to describe data. With the knowledge gained in this course, students will be ready to undertake their first very own data analysis using the open source software R, which is rapidly becoming the leading programming language in statistics and data science.
Compulsory literature:
Recommended literature:
[1] Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University.
Way of continuous check of knowledge in the course of semester
Discussion
Full-time study:
- homeworks/intermediate tests (max. 20 points overall, required minimum: 20 points)
Conditions for credit:
For successful completion of the Discussions is given credit. Students will receive credit if they gain at least 20 points.
Exam
- written exam (max. 60 points, required minimum: 30 points)
Conditions for taking the exam:
Students will pass the exam if they meet the the required minimum of the exam and compensatory gain (Discussions and Exam) at least 51 points.
E-learning
Other requirements
Full-time study
Participation at all discussions is obligatory, 2 apologies are accepted. Participation at lectures is recommended, knowledge of lecture materials is a prerequisite for participation at the exercises.
Combined study
Participation at all tutorials is obligatory, 1 apology is accepted.
Prerequisities
Subject has no prerequisities.
Co-requisities
Subject has no co-requisities.
Subject syllabus:
1) Introduction to Probability Theory
2) Conditonal probability, Bayes Theorem
3) Discrete random variable
4) Discrete probability distributions
5) Continuous random variable
6) Continous probability distributions
7) Random Vector
8) Exploratory data analysis - qualitative variable and two qualitative variables
9) Exploratory data analysis - quantitative variable
10) Exploratory data analysis - two quantitative variables (independent variables vs. paired data)
11) Introduction to statistical induction, Introduction to estimation theory
12) Introduction to hypothesis testing
13) One sample tests of mean and binomial test of proportion
Conditions for subject completion
Occurrence in study plans
Occurrence in special blocks
Assessment of instruction
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