Course manual 2018/2019

Course content

In this course general aspects of statistics applied for analytical methods will be treated. Parameters to describe the quality of analytical methods (e.g. accuracy and precision, sensitivity, selectivity, robustness) will be defined. Basic statistical methods applied to modern analytical instrumentation will be discussed. These include data exploration and visualization, statistical inference, hypothesis testing, and calibration, applied to univariate and multivariate data.Attention will also be given to sampling strategies, design-of-experiments, validation procedures and to quality control in analytical laboratories. One of the main objectives of the course is to acquire the skills for adequate software handling for data analysis (e.g. Excel and Matlab).

Study materials

Literature

  • Statistics and Chemometrics for Analytical Chemistry, Miller&Miller, Pearson, 6th ed.

Software

  • MATLAB

  • GPower

Other

  • Hand-outs and exercises.

Objectives

After this course students should be able to:

  • estimate and compare the merits of different analytical techniques
  • set-up an experimental design and interpret the experimental results
  • define suitable sampling strategies
  • find the main characteristics of signals obtained by instrumental analytical techniques
  • propose suitable methods for signal and data processing
  • use suitable statistical methods for the processing and interpretation of experimental results
  • use suitable statistical software

Teaching methods

  • Lecture
  • Seminar
  • Computer lab session/practical training
  • Laptop seminar
  • Self-study

Learning activities

Activity

Number of hours

Zelfstudie

168

Attendance

The programme does not have requirements concerning attendance (OER-B).

Assessment

Item and weight Details

Final grade

0.8 (80%)

Exam

0.2 (20%)

Bonus tests

Best of

There will be 5x 15-minute written tests at the start of the lectures: Friday February 8, February 15, February 22, March 1, and March 8. In these tests, the students will be assessed on basic knowledge from the previous lectures. If the mean of the best 4 grades** is larger than the exam grade, then the final grade is calculated as follows: Exam (80%), Mean of 4 best tests (20%). If the mean of the best 4 grades is lower than the exam grade, then the final grade is 100% obtained from the exam grade. This means that the tests can only improve your exam grade (as a bonus), never lower it. Participation is not mandatory.

** This means that you are allowed to miss out on one test.

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Date

Topic

Lec.

Staff

Location

Time

6-Feb

Introduction to Statistics

L1-A

BP, MU, LN

SP C1.110

9:00-11:00

 

Introduction to MATLAB

L1-B

BP, MU, LN

SP C1.110

15:00-17:00

8-Feb

Repeated Measurements

L2

BP, MU, LN

VU HG-14A33

9:00-13:00

13-Feb

Hypothesis Testing Introduction, Tail tests

L3-A

BP, MU, LN

SP C0.110

9:00-11:00

 

Comparison of two means, mean with value

L3-B

BP, MU, LN

SP C1.110

15:00-17:00

15-Feb

Power analysis

L4

BP, MU, LN

VU WN-KC159

9:00-13:00

18-Feb

Comparison of standard deviations - Part A

L5-A

BP, MU, LN

SP D1.111

9:00-11:00

20-Feb

Comparison of standard deviations - Part B

L5-B

BP, MU, LN

SP C0.05

17:00-19:00

22-Feb

ANOVA

L6

BP, MU, LN

VU WN-KC159

9:00-13:00

25-Feb

Goodness of Fit / Outliers

L7

BP, MU, LN

SP D1.111

9:00-13:00

1-Mar

Non Parametric Stats

L8

BP, MU, LN

VU WN-KC159

9:00-13:00

6-Mar

Calibration I - Part A

L9-A

BP, MU, LN

SP C1.110

9:00-11:00

 

Calibration I - Part B

L9-B

BP, MU, LN

SP C1.110

15:00-17:00

8-Mar

Calibration II

L10

BP, MU, LN

VU WN-KC159

9:00-13:00

11-Mar

EXTRA/Question hour/

L11

BP, MU, LN

SP C1.112

9:00-13:00

15-Mar

Sampling I

H1

HL

VU WN-KC159

9:00-13:00

18-Mar

Sampling II

H2

HL

SP C0.05

13:00-17:00

22-Mar

Analytical Strategies

H3

HL

VU HG-14A33

9:00-13:00

27-Mar

Exam

 

-

REC C1.03

9:00-12:00

Timetable

The schedule for this course is published on DataNose.

Contact information

Coordinator

  • B.W.J. Pirok MSc