Statistical Data Analysis in Chemistry

COURSE CURRICULUM

1.

Course title

STATISTICAL DATA PROCESSING IN CHEMISTRY

2.

Code

HM-602

3.

Study curriculum

All study programs in chemistry

4.

Organizer of the curriculum (institute, department)

Institute of Chemistry, Faculty of Natural Sciences and Mathematics

5.

Degree (BSc, MSc, PhD)

BSc

6.

Academic year/semester

III/VI

7.

ECTS points

4

8.

Lecturer

Prof. Dr. Igor Kuzmanovski

9.

Prerequisites

Mathematics II

10.

Course objectives (competences):
Students will be introduced to the basics of statistical processing of chemical data and their application in a chemical laboratory to extract the maximum amount of information from the obtained data.

11.

Course content:
–    Modern data processing in a chemical laboratory (errors during quantitative analysis, random and systematic errors, minimization of systematic errors);
–    Statistics of repeated measurements (population, sample, characteristics, measures of central tendency, measures of dispersion, distribution of repeated measurements, type I and type II errors);
–    Statistical tests (systematic error detection tests, tests for checking the influence of random errors on the results, tests for extreme values);
–    Quality of analytical measurements (sampling; separation and estimation of dispersion; sampling strategy, sampling quality control methods);
–    Univariate and multivariate data modeling (linear regression, multivariate linear regression; characteristic vector regression; partial least squares regression; non-linear modeling methods);
–    Optimization and design of experiments (stochastic and systematic optimization, factorial design of experiments, simplex optimization).

12.

Teaching methods: lectures, problem solving

13.

Total available time

120

14.

Time distribution

2+2 hours weekly (lectures, problem solving)

15.

Teaching methods distribution

15.1.

Teaching - lectures

30 hours

15.2.

Practicals (laboratory, problem solving), seminars, team work

30 hours

16.

Other activities

16.1.

Projects

0 hours

16.2.

Independent work

15 hours

16.3.

Homework

45 hours

17.

Grading methods

17.1.

Tests

70 points

17.2.

Seminars/projects (written/oral presentation)

/ points

17.3.

Activity

 30 points

18.

Grading scale (points/mark)

< 50 points

5 (five) (F)

51 to 60 points

6 (six) (E)

61 to 70 points

7 (seven) (D)

71 to 80 points

8 (eight) (C)

81 to 90 points

9 (nine) (B)

91 to 100 points

10 (ten) (A)

19.

Criteria for taking the final exam

Attendance at lectures and completed theoretical exercises

20.

Course language

Macedonian, English

21.

Teaching quality control

Anonymous surveys, questionnaires

22.

Literature

22.1.

Compulsory

No.

Author

Title

Publisher

Year

1.

I. Kuzmanovski

Statistical data processing in chemistry (internal script)

2.

M. Otto

Chemometrics: Statistics and Computer Application

in Analytical Chemistry

Wiley-WCH, Weinheim

2007

3.

J.N. Miller, J.C. Miller

Statistics and Chemometrics for Analytical Chemistry

Prentice Hall, Harlow

2000

22.2.

Additional

No.

Author

Title

Publisher

Year

1.

Paul Gemperline (ed.)

Practical Guide to Chemometrics

Taylor & Francis

2006