FAIS1_15 Statistics

Faculty of Pharmacy
Spring 2020
Extent and Intensity
2/2/0. 5 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. Bc. Jiří Pazourek, Ph.D. (lecturer)
PharmDr. Pavlína Marvanová, Ph.D. (seminar tutor)
doc. RNDr. Bc. Jiří Pazourek, Ph.D. (seminar tutor)
Guaranteed by
doc. RNDr. Bc. Jiří Pazourek, Ph.D.
Department of Chemical Drugs – Departments – Faculty of Pharmacy
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
Statistical evaluation of results is essential part of all experimental scientific branches. The content of this subject is basic statistics for a graduate student of the study program " Pharmacy".

Lessons concern mainly descriptive statistics, partially also the probability calculus and mathematical statistics with a direct relationship to real scientific tasks of experimental work (evaluation of experimental data, hypotheses formulation and testing).

Practical exercises include introduction to PC´s hardware and software and utilization of spreadsheet programs (MS Excel, Gnumeric).

Syllabus
  • Content of Lectures:
    Our stochastic world. The impact of probability onto our (experimental) data. The meaning of statistics. The roll of dice - random circumstances. The random experiment I: relative frequencies of observations and probability. Distribution function - normal distribution (Gaussian), probability density function.
    Population and samples. Descriptive statistics. Population parameters and their estimations. Student´s distribution, the central limit theorem. Means (average, median, modus), measures of variability: standard deviation, variance.
    The random experiment II: Construction of tables from experimental data, type of variables on scales - nominal, ordinal, interval and ratio scales. Graphs from experimental data: histograms vs. bar graphs, frequency polygons, XY-charts, quantiles, Box-and-whiskers plot I
    Hypotheses and test. Alpha and beta errors. Hypotheses testing. Null and alternative hypothesis, critical values/p-values, level of significance alpha.
    The empirical and the expected distribution - chi-square (Goodness-of-fit) test.
    Tests for outliers removal - Box-and-whiskers plot II: outliers, Grubbs test for outliers, significant digits - rounding off. Confidence interval.
    Parametric and non-parametric tests.
    F-test, two sample t-test for equality of means
    Non-parametric alternatives of one- and two-sample tests
    Relationship between two quantitative variables: Pearson´s chi2-test of independency. Correlation and regression, Spearman´s rank correlation coefficient.
    Linear regression - Pearson´s correlation coefficient. Test of the intercept significance.
    Literature:
    http://www.itl.nist.gov/div898/handbook/eda/eda.htm
    Massart, D.L. et al., Handbook of Chemometrics and Qualimetrics, Elsevier 1997

    Contents of practical lessons:
    1. Applications of PC for pharmacy study. Internet information sources. Scientific databases on-line, info-search with logical operators. ISI Web of Knowledge, Science Direct- reference search on VFU. Practical searching exercise according to key words
    2. How to work with MS Excel. Editing a spreadsheet, basic calculations (formulas), graphical presentation of data. Analytical signal evaluation - the chromatographic peak. Numerical integration.
    3. Evaluation of experimental data by basic descriptive statistics (arithmetic mean, median, modus, quantiles). Calculation: basic statistical characteristics of a data set. Graphical presentation of experimental data: polygon and histogram of frequencies, bar chart, pie chart, xy-graph. Quantiles - box-and-whiskers plots construction. Gnumeric.exe
    4. Null hypotheses. Normality tests: Lilliefors test (Gnumeric.exe). Outliers: the method of inner boundaries - an adopted box-and-whiskers plot.
    5. Experimental data evaluation (random errors). Average. Standard deviation. Rounding off. Confidence interval. Data evaluations (outliers) - outliers removal (Grubbs test).
    6. Modules in MS Excel Data analysis: descriptive statistics, two-sample F-test (comparison of variances), t-test (two-sample test with unequal variances). ANOVA
    7. Non-parametric statistical alternatives: sign test, Wilcoxon rank sum test (Mann-Whitney U-test). Kruskal-Wallis test
    8. Fourfold tables, Fisher exact test. Contingency tables
    9. Spearman´s coefficient rho of rank correlation, Person´s r
    10. Linear regression - construction. Calibration graph construction by the linear regression, interpretation of the model
    11. Linear regression - test of the intercept significance (H0: a=0).
    12. Final test. Examination (MOODLE)
Literature
    recommended literature
  • S.Ashcroft, Ch.Pereira. Practical Statistics for the biological sciences. Palgrave MacMillan, GB, 2003. info
  • Massart. Handbook of Chemometrics and Qualimetrics. Amsterdam, 1997. info
Teaching methods (in Czech)
Monologická (výklad, přednáška, instruktáž)
Assessment methods (in Czech)
Písemná zkouška
Language of instruction
English
Further comments (probably available only in Czech)
Study Materials
The course can also be completed outside the examination period.
Teacher's information
To gain credits, a student must take part at least in ALL but one of the practical
(computer) exercises,
get approved all the written homeworks (MOODLE)
and pass the final MOODLE written test (>51%).
The course is also listed under the following terms Autumn 2020, Autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.
  • Enrolment Statistics (Spring 2020, recent)
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