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Data Processing and Analysis

Code 8631
Year 2
Semester S1
ECTS Credits 6
Workload T(30H)/TP(30H)
Scientific area Industrial Chemistry
Entry requirements Students need to have previous basic knowledge of mathematics required to realize calculus, understand the concept of mathematical function and variables under the scope of statistical and probability calculations.
Mode of delivery Presencial teaching (with frequency of theoretical and practical lessons) --
Work placements Not applicable
Learning outcomes This course aims to enable students with a set of skills/knowledge corresponding to acquisition of the following
capabilities to:
analyze, evaluate and present experimental results in statistical terms;
identify, quantify and minimize origins and sources of error in chemistry;
demonstrate the ability to intervene in the procedures for measurement and quantification commonly used inlaboratories in order to increase their reliability;
demonstrate the ability to interpret and compare experimental results obtained in various contexts, before and after being subjected to statistical analysis;
within the analyzes of probabilities be able to calculate the possibility to obtain certain results and experimental measurements using previous measurement or results;
plan, deliver and treat the results of analytical determinations based on calibration curves or by standard addition method.
Syllabus 1. Application of statistics to chemistry. Introduction and Review of Concepts
Nature and context of analytical problems.
Importance of statistical treatment of experimental results.
Experience planning.
Revision of concepts: expression of concentration.
Presentation of statistical tests in Excel
2. Probabilities and Combinatorial Analysis
Probability and most common distributions (discrete and continuous): normal, t, Chi-Square and F.
Hypothesis tests: null and alternative hypothesis, type I and II errors, F and t test (paired and unpaired).
3. Errors
Errors in analytical chemistry (origins and consequences).
Types of errors (random and systematic).
Mean and standard deviation.
Distribution of errors.
Presentation of results and confidence intervals.
Propagation of random and systematic errors.
4. Variance Analysis: ANOVA
Comparison of multiple means.
Analysis of variance: one-way and two way.
Other significance tests: t-test, F-test, aberrant points and chi-square test.
Main Bibliography 1. Main titles (in native language)
Natália Cordeiro e Alexandre Magalhães
Introdução à Estatística – Uma perspectiva química
Bento José Ferreira Murteira
Probabilidade e Estatística
MacGraw-Hill Portugal
António Robalo
Estatística (vol I, II e III)
Edições Sílabo
2. Complementary titles(in english)
Thomas P. Ryan
Modern Engineering Statistics
Philip Rowe
Essential Statistics for the Pharmaceutical Sciences
Douglas A. Skoog, Donald M. West and F. James Holler.
Analytical Chemistry: An introduction
Saunders College Publishing
J.C. Miller and J.N. Miller
Statistical for analytical chemistry
Ellis Horwood and Prentice Hall
Teaching Methodologies and Assessment Criteria Considering that laboratory and practical classes of the industrial chemistry (course) are heavily included in other
classes, here we intend that students acquired the knowledge and the skills required to improve and implement the
statistical analysis of experimental data and quantifications performed. Beside the theoretical support from classes we
implement several opportunities for the use of the taught issues in the context of laboratory activities, analysis and
control of quality, methods and results validation for research and development activities. These last issues are
achieved through theoretical practical classes, which include several examples and exercises.
Language Portuguese. Tutorial support is available in English.
Last updated on: 2021-10-15

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