Basics of Statistics A
General data
Course ID: | 1200-1CHJPSTA2 |
Erasmus code / ISCED: |
13.3
|
Course title: | Basics of Statistics A |
Name in Polish: | Podstawy statystyki A |
Organizational unit: | Faculty of Chemistry |
Course groups: |
(in Polish) Przedmioty minimum programowego dla studentów 2-go semestru (S1-PRK-CHJR) |
ECTS credit allocation (and other scores): |
3.00
|
Language: | Polish |
Main fields of studies for MISMaP: | chemistry |
Type of course: | obligatory courses |
Mode: | Classroom |
Short description: |
This is the only course of statistics for nuclear chemistry (bachelor studies) dealing with problems of statistical data analysis. Its aim is to familiarize students with theoretical foundations of the most important statistical methods, their applications, especially on examples related to chemical and biochemical research. The main issues to be studied are: descriptive statistics, probability, random variables and their distributions, confidence intervals, testing of statistical hypotheses, analysis of variance, regression analysis, the least squares method, correlations and nonparametric methods. The lecture is to: (a) present systematically the knowledge needed for the sensible use of statistical methods in chemistry; and (b) familiarize students with the theoretical foundations of the most important methods. Exercises are designed to teach students how to solve statistical problems and learn practical aspects of the most important statistical methods using computer software. |
Full description: |
Problems addressed during the lecture concern: definition of the scope and nature of statistics, descriptive statistics, including measures of concentration and measures of data dispersion, definitions of probability, random variables and their distributions; theory of estimation, confidence intervals, testing of statistical hypotheses; analysis of variance (one-way and multi-component ) in the parametric and non-parametric versions; regression and correlation analysis; least squares curve fitting, simple and multiple regression, nonlinear regression, role of residuals, confidence intervals and tolerance intervals in regression analysis; correlation analysis; parametric and non-parametric coefficients of correlation. We will try to analyse the above issues using biochemical and chemical examples and referring to the problems of medicinal chemistry. At the beginning of each class, students will provided with access to electronic materials necessary to master the planned statistical problems. Classes are designed to: (a) teach students how to solve chemical/biochemical/medical problems using different statistical tools, (b) teach student a sensible use of statistical methods in medical chemistry, biochemistry and medicine, as well as choosing the right method for a given problem, (c) familiarize students with the practical aspects of the most important statistical methods, (d) teach student to use the available statistical information sources. (e) teach students to use computer software in statistical data analysis |
Bibliography: |
There is not one specific textbook that contains the entire course, but most statistical textbooks at the higher level contain most of the topics presented. For example we can recommend a book: : „Zbiór zadań ze statystyki medycznej” Antoniego Lemańczyka, edited by Wydawnictwo Naukowe Uniwersytetu Medycznego im. Karola Marcinkowskiego, Poznań 2008, ISBN 978-83-7597-011-1. Students will be given complete hand-outs at the beginning of each lecture. |
Learning outcomes: |
After completing the lectures and classes, students should: (a) be able to choose statistical methods to solve a specific chemical/biochemical/medical problem (b) be able to perform the necessary statistical calculations, (c) be able to interpret the results of statistical analysis (d) understand and critically refer to the results obtained through the experiment or calculations, (e) be able to use computer software used in statistical data analysis |
Assessment methods and assessment criteria: |
At the end of the course student will have to pass a written exam and perform a short project using three different statistical methods to solve a problem defined. This project will be reported individually in a form of a presentation and presented on the forum of the group. The written exam will consist of test questions and several exercises to solve using computer software. Depending on the number of points scored, the student will receive the following marks: 5.5 above 90% 5.0 80 - 90% 4.5 70 - 80% 4.0 60 - 70% 3.5 50 - 60% 3.0 40 - 50% 2.0 below 40% |
Classes in period "Summer semester 2023/24" (in progress)
Time span: | 2024-02-19 - 2024-06-16 |
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MO TU W LAB
WYK
LAB
LAB
TH FR |
Type of class: |
Lab, 15 hours
Lecture, 15 hours
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Coordinators: | Wojciech Sławiński | |
Group instructors: | Wojciech Sławiński | |
Students list: | (inaccessible to you) | |
Examination: | Examination |
Copyright by University of Warsaw.