Bi8661 Data analysis on PC III

Faculty of Science
Autumn 2009
Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
RNDr. Eva Gelnarová (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Timetable
Thu 16:00–19:50 F01B1/709
Prerequisites
Bi5040 Biostatistics - basic course && Bi7490 Predictive Modelling
This subject is suitable for more advanced students with good knowledge of statistics. More advanced statistical software will be presented.
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
At the end of the course the student will obtain: - overview of usage of R software for statistical analysis - knowledge of decriptive statistics and data visualisation in R software - knowledge of hypothesis testing in R software - knowledge of basic regression modelling in R software - experience in practical data analysis using R software
Syllabus
  • 1. Introduction to R software, basic statistical methods a) Basics of R – history, installation, data loading, data types and structures, function creation, libraries, formatting of outputs, matrix operations b) data visualization – boxplots, histograms, scatter-plots c) data operations, transformation, summary of statistical distributions, correlation coefficients d) Statistical tests – tests of normality, one and two-sample t-test, nonparametric tests e)Analysis of variance. 2. Advanced statistical methods in R a)contingency tables, chi-square test b)linear regression models – estimation of parameters, residual diagnostics, outliers, parameters selection c) generalized linear models – logistic regression, poisson regression d)principal component analysis – eigenvalues. 3. Block based on students requests with possibilities a)analysis of real dataset in R, reporting b) introduction to other software
Literature
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • StatSoft, Inc. (2007). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html
  • www.r-project.org
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Legendre, P., Legendre, L. (1998) Numerical ecology. Elsevier, 2nd ed.
  • ter Braak, C.J.F. (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2008.

Bi8661 Data analysis on PC III

Faculty of Science
Autumn 2008
Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
RNDr. Eva Gelnarová (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Prerequisites (in Czech)
Bi5040 Biostatistics - basic course && Bi7490 Intr. to Stochastic Modelling
This subject is suitable for more advanced students with good knowledge of statistics. More advanced statistical software will be presented.
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
The course is oriented on advanced software for data analyses.
Syllabus
  • 1. Introduction to R software, basic statistical methods a) Basics of R – history, installation, data loading, data types and structures, function creation, libraries, formatting of outputs, matrix operations b) data visualization – boxplots, histograms, scatter-plots c) data operations, transformation, summary of statistical distributions, correlation coefficients d) Statistical tests – tests of normality, one and two-sample t-test, nonparametric tests e)Analysis of variance. 2. Advanced statistical methods in R a)contingency tables, chi-square test b)linear regression models – estimation of parameters, residual diagnostics, outliers, parameters selection c) generalized linear models – logistic regression, poisson regression d)principal component analysis – eigenvalues. 3. Block based on students requests with possibilities a)analysis of real dataset in R, reporting b) introduction to other software
Literature
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • StatSoft, Inc. (2007). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html
  • www.r-project.org
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Legendre, P., Legendre, L. (1998) Numerical ecology. Elsevier, 2nd ed.
  • ter Braak, C.J.F. (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
Assessment methods
The credit is obtain through presence of student on course.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Autumn 2009.

Bi8661 Data analysis on PC III

Faculty of Science
Autumn 2024

The course is not taught in Autumn 2024

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).
Teacher(s)
RNDr. Eva Gelnarová (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Bi5040 Biostatistics - basic course && Bi7490 Advanced non-parametric method
This subject is suitable for more advanced students with good knowledge of statistics. More advanced statistical software will be presented.
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
At the end of the course the student will obtain: - overview of usage of R software for statistical analysis - knowledge of decriptive statistics and data visualisation in R software - knowledge of hypothesis testing in R software - knowledge of basic regression modelling in R software - experience in practical data analysis using R software
Syllabus
  • 1. Introduction to R software, basic statistical methods a) Basics of R – history, installation, data loading, data types and structures, function creation, libraries, formatting of outputs, matrix operations b) data visualization – boxplots, histograms, scatter-plots c) data operations, transformation, summary of statistical distributions, correlation coefficients d) Statistical tests – tests of normality, one and two-sample t-test, nonparametric tests e)Analysis of variance. 2. Advanced statistical methods in R a)contingency tables, chi-square test b)linear regression models – estimation of parameters, residual diagnostics, outliers, parameters selection c) generalized linear models – logistic regression, poisson regression d)principal component analysis – eigenvalues. 3. Block based on students requests with possibilities a)analysis of real dataset in R, reporting b) introduction to other software
Literature
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • StatSoft, Inc. (2007). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html
  • www.r-project.org
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Legendre, P., Legendre, L. (1998) Numerical ecology. Elsevier, 2nd ed.
  • ter Braak, C.J.F. (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is also listed under the following terms Autumn 2008, Autumn 2009.

Bi8661 Data analysis on PC III

Faculty of Science
Autumn 2023

The course is not taught in Autumn 2023

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).
Teacher(s)
RNDr. Eva Gelnarová (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Bi5040 Biostatistics - basic course && Bi7490 Advanced non-parametric method
This subject is suitable for more advanced students with good knowledge of statistics. More advanced statistical software will be presented.
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
At the end of the course the student will obtain: - overview of usage of R software for statistical analysis - knowledge of decriptive statistics and data visualisation in R software - knowledge of hypothesis testing in R software - knowledge of basic regression modelling in R software - experience in practical data analysis using R software
Syllabus
  • 1. Introduction to R software, basic statistical methods a) Basics of R – history, installation, data loading, data types and structures, function creation, libraries, formatting of outputs, matrix operations b) data visualization – boxplots, histograms, scatter-plots c) data operations, transformation, summary of statistical distributions, correlation coefficients d) Statistical tests – tests of normality, one and two-sample t-test, nonparametric tests e)Analysis of variance. 2. Advanced statistical methods in R a)contingency tables, chi-square test b)linear regression models – estimation of parameters, residual diagnostics, outliers, parameters selection c) generalized linear models – logistic regression, poisson regression d)principal component analysis – eigenvalues. 3. Block based on students requests with possibilities a)analysis of real dataset in R, reporting b) introduction to other software
Literature
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • StatSoft, Inc. (2007). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html
  • www.r-project.org
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Legendre, P., Legendre, L. (1998) Numerical ecology. Elsevier, 2nd ed.
  • ter Braak, C.J.F. (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is also listed under the following terms Autumn 2008, Autumn 2009.

Bi8661 Data analysis on PC III

Faculty of Science
Autumn 2022

The course is not taught in Autumn 2022

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).
Teacher(s)
RNDr. Eva Gelnarová (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Bi5040 Biostatistics - basic course && Bi7490 Advanced non-parametric method
This subject is suitable for more advanced students with good knowledge of statistics. More advanced statistical software will be presented.
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
At the end of the course the student will obtain: - overview of usage of R software for statistical analysis - knowledge of decriptive statistics and data visualisation in R software - knowledge of hypothesis testing in R software - knowledge of basic regression modelling in R software - experience in practical data analysis using R software
Syllabus
  • 1. Introduction to R software, basic statistical methods a) Basics of R – history, installation, data loading, data types and structures, function creation, libraries, formatting of outputs, matrix operations b) data visualization – boxplots, histograms, scatter-plots c) data operations, transformation, summary of statistical distributions, correlation coefficients d) Statistical tests – tests of normality, one and two-sample t-test, nonparametric tests e)Analysis of variance. 2. Advanced statistical methods in R a)contingency tables, chi-square test b)linear regression models – estimation of parameters, residual diagnostics, outliers, parameters selection c) generalized linear models – logistic regression, poisson regression d)principal component analysis – eigenvalues. 3. Block based on students requests with possibilities a)analysis of real dataset in R, reporting b) introduction to other software
Literature
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • StatSoft, Inc. (2007). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html
  • www.r-project.org
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Legendre, P., Legendre, L. (1998) Numerical ecology. Elsevier, 2nd ed.
  • ter Braak, C.J.F. (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is also listed under the following terms Autumn 2008, Autumn 2009.

Bi8661 Data analysis on PC III

Faculty of Science
autumn 2021

The course is not taught in autumn 2021

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).
Teacher(s)
RNDr. Eva Gelnarová (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Bi5040 Biostatistics - basic course && Bi7490 Advanced non-parametric method
This subject is suitable for more advanced students with good knowledge of statistics. More advanced statistical software will be presented.
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
At the end of the course the student will obtain: - overview of usage of R software for statistical analysis - knowledge of decriptive statistics and data visualisation in R software - knowledge of hypothesis testing in R software - knowledge of basic regression modelling in R software - experience in practical data analysis using R software
Syllabus
  • 1. Introduction to R software, basic statistical methods a) Basics of R – history, installation, data loading, data types and structures, function creation, libraries, formatting of outputs, matrix operations b) data visualization – boxplots, histograms, scatter-plots c) data operations, transformation, summary of statistical distributions, correlation coefficients d) Statistical tests – tests of normality, one and two-sample t-test, nonparametric tests e)Analysis of variance. 2. Advanced statistical methods in R a)contingency tables, chi-square test b)linear regression models – estimation of parameters, residual diagnostics, outliers, parameters selection c) generalized linear models – logistic regression, poisson regression d)principal component analysis – eigenvalues. 3. Block based on students requests with possibilities a)analysis of real dataset in R, reporting b) introduction to other software
Literature
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • StatSoft, Inc. (2007). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html
  • www.r-project.org
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Legendre, P., Legendre, L. (1998) Numerical ecology. Elsevier, 2nd ed.
  • ter Braak, C.J.F. (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is also listed under the following terms Autumn 2008, Autumn 2009.

Bi8661 Data analysis on PC III

Faculty of Science
Autumn 2020

The course is not taught in Autumn 2020

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).
Teacher(s)
RNDr. Eva Gelnarová (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Bi5040 Biostatistics - basic course && Bi7490 Advanced non-parametric method
This subject is suitable for more advanced students with good knowledge of statistics. More advanced statistical software will be presented.
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
At the end of the course the student will obtain: - overview of usage of R software for statistical analysis - knowledge of decriptive statistics and data visualisation in R software - knowledge of hypothesis testing in R software - knowledge of basic regression modelling in R software - experience in practical data analysis using R software
Syllabus
  • 1. Introduction to R software, basic statistical methods a) Basics of R – history, installation, data loading, data types and structures, function creation, libraries, formatting of outputs, matrix operations b) data visualization – boxplots, histograms, scatter-plots c) data operations, transformation, summary of statistical distributions, correlation coefficients d) Statistical tests – tests of normality, one and two-sample t-test, nonparametric tests e)Analysis of variance. 2. Advanced statistical methods in R a)contingency tables, chi-square test b)linear regression models – estimation of parameters, residual diagnostics, outliers, parameters selection c) generalized linear models – logistic regression, poisson regression d)principal component analysis – eigenvalues. 3. Block based on students requests with possibilities a)analysis of real dataset in R, reporting b) introduction to other software
Literature
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • StatSoft, Inc. (2007). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html
  • www.r-project.org
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Legendre, P., Legendre, L. (1998) Numerical ecology. Elsevier, 2nd ed.
  • ter Braak, C.J.F. (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is also listed under the following terms Autumn 2008, Autumn 2009.

Bi8661 Data analysis on PC III

Faculty of Science
Autumn 2019

The course is not taught in Autumn 2019

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).
Teacher(s)
RNDr. Eva Gelnarová (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Bi5040 Biostatistics - basic course && Bi7490 Advanced non-parametric method
This subject is suitable for more advanced students with good knowledge of statistics. More advanced statistical software will be presented.
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
At the end of the course the student will obtain: - overview of usage of R software for statistical analysis - knowledge of decriptive statistics and data visualisation in R software - knowledge of hypothesis testing in R software - knowledge of basic regression modelling in R software - experience in practical data analysis using R software
Syllabus
  • 1. Introduction to R software, basic statistical methods a) Basics of R – history, installation, data loading, data types and structures, function creation, libraries, formatting of outputs, matrix operations b) data visualization – boxplots, histograms, scatter-plots c) data operations, transformation, summary of statistical distributions, correlation coefficients d) Statistical tests – tests of normality, one and two-sample t-test, nonparametric tests e)Analysis of variance. 2. Advanced statistical methods in R a)contingency tables, chi-square test b)linear regression models – estimation of parameters, residual diagnostics, outliers, parameters selection c) generalized linear models – logistic regression, poisson regression d)principal component analysis – eigenvalues. 3. Block based on students requests with possibilities a)analysis of real dataset in R, reporting b) introduction to other software
Literature
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • StatSoft, Inc. (2007). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html
  • www.r-project.org
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Legendre, P., Legendre, L. (1998) Numerical ecology. Elsevier, 2nd ed.
  • ter Braak, C.J.F. (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is also listed under the following terms Autumn 2008, Autumn 2009.

Bi8661 Data analysis on PC III

Faculty of Science
Autumn 2018

The course is not taught in Autumn 2018

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).
Teacher(s)
RNDr. Eva Gelnarová (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Bi5040 Biostatistics - basic course && Bi7490 Advanced non-parametric method
This subject is suitable for more advanced students with good knowledge of statistics. More advanced statistical software will be presented.
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
At the end of the course the student will obtain: - overview of usage of R software for statistical analysis - knowledge of decriptive statistics and data visualisation in R software - knowledge of hypothesis testing in R software - knowledge of basic regression modelling in R software - experience in practical data analysis using R software
Syllabus
  • 1. Introduction to R software, basic statistical methods a) Basics of R – history, installation, data loading, data types and structures, function creation, libraries, formatting of outputs, matrix operations b) data visualization – boxplots, histograms, scatter-plots c) data operations, transformation, summary of statistical distributions, correlation coefficients d) Statistical tests – tests of normality, one and two-sample t-test, nonparametric tests e)Analysis of variance. 2. Advanced statistical methods in R a)contingency tables, chi-square test b)linear regression models – estimation of parameters, residual diagnostics, outliers, parameters selection c) generalized linear models – logistic regression, poisson regression d)principal component analysis – eigenvalues. 3. Block based on students requests with possibilities a)analysis of real dataset in R, reporting b) introduction to other software
Literature
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • StatSoft, Inc. (2007). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html
  • www.r-project.org
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Legendre, P., Legendre, L. (1998) Numerical ecology. Elsevier, 2nd ed.
  • ter Braak, C.J.F. (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is also listed under the following terms Autumn 2008, Autumn 2009.

Bi8661 Data analysis on PC III

Faculty of Science
autumn 2017

The course is not taught in autumn 2017

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
RNDr. Eva Gelnarová (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Bi5040 Biostatistics - basic course && Bi7490 Advanced non-parametric method
This subject is suitable for more advanced students with good knowledge of statistics. More advanced statistical software will be presented.
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
At the end of the course the student will obtain: - overview of usage of R software for statistical analysis - knowledge of decriptive statistics and data visualisation in R software - knowledge of hypothesis testing in R software - knowledge of basic regression modelling in R software - experience in practical data analysis using R software
Syllabus
  • 1. Introduction to R software, basic statistical methods a) Basics of R – history, installation, data loading, data types and structures, function creation, libraries, formatting of outputs, matrix operations b) data visualization – boxplots, histograms, scatter-plots c) data operations, transformation, summary of statistical distributions, correlation coefficients d) Statistical tests – tests of normality, one and two-sample t-test, nonparametric tests e)Analysis of variance. 2. Advanced statistical methods in R a)contingency tables, chi-square test b)linear regression models – estimation of parameters, residual diagnostics, outliers, parameters selection c) generalized linear models – logistic regression, poisson regression d)principal component analysis – eigenvalues. 3. Block based on students requests with possibilities a)analysis of real dataset in R, reporting b) introduction to other software
Literature
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • StatSoft, Inc. (2007). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html
  • www.r-project.org
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Legendre, P., Legendre, L. (1998) Numerical ecology. Elsevier, 2nd ed.
  • ter Braak, C.J.F. (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is also listed under the following terms Autumn 2008, Autumn 2009.

Bi8661 Data analysis on PC III

Faculty of Science
Autumn 2016

The course is not taught in Autumn 2016

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
RNDr. Eva Gelnarová (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Bi5040 Biostatistics - basic course && Bi7490 Advanced non-parametric method
This subject is suitable for more advanced students with good knowledge of statistics. More advanced statistical software will be presented.
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
At the end of the course the student will obtain: - overview of usage of R software for statistical analysis - knowledge of decriptive statistics and data visualisation in R software - knowledge of hypothesis testing in R software - knowledge of basic regression modelling in R software - experience in practical data analysis using R software
Syllabus
  • 1. Introduction to R software, basic statistical methods a) Basics of R – history, installation, data loading, data types and structures, function creation, libraries, formatting of outputs, matrix operations b) data visualization – boxplots, histograms, scatter-plots c) data operations, transformation, summary of statistical distributions, correlation coefficients d) Statistical tests – tests of normality, one and two-sample t-test, nonparametric tests e)Analysis of variance. 2. Advanced statistical methods in R a)contingency tables, chi-square test b)linear regression models – estimation of parameters, residual diagnostics, outliers, parameters selection c) generalized linear models – logistic regression, poisson regression d)principal component analysis – eigenvalues. 3. Block based on students requests with possibilities a)analysis of real dataset in R, reporting b) introduction to other software
Literature
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • StatSoft, Inc. (2007). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html
  • www.r-project.org
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Legendre, P., Legendre, L. (1998) Numerical ecology. Elsevier, 2nd ed.
  • ter Braak, C.J.F. (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is also listed under the following terms Autumn 2008, Autumn 2009.

Bi8661 Data analysis on PC III

Faculty of Science
Autumn 2015

The course is not taught in Autumn 2015

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
RNDr. Eva Gelnarová (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Bi5040 Biostatistics - basic course && Bi7490 Advanced non-parametric method
This subject is suitable for more advanced students with good knowledge of statistics. More advanced statistical software will be presented.
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
At the end of the course the student will obtain: - overview of usage of R software for statistical analysis - knowledge of decriptive statistics and data visualisation in R software - knowledge of hypothesis testing in R software - knowledge of basic regression modelling in R software - experience in practical data analysis using R software
Syllabus
  • 1. Introduction to R software, basic statistical methods a) Basics of R – history, installation, data loading, data types and structures, function creation, libraries, formatting of outputs, matrix operations b) data visualization – boxplots, histograms, scatter-plots c) data operations, transformation, summary of statistical distributions, correlation coefficients d) Statistical tests – tests of normality, one and two-sample t-test, nonparametric tests e)Analysis of variance. 2. Advanced statistical methods in R a)contingency tables, chi-square test b)linear regression models – estimation of parameters, residual diagnostics, outliers, parameters selection c) generalized linear models – logistic regression, poisson regression d)principal component analysis – eigenvalues. 3. Block based on students requests with possibilities a)analysis of real dataset in R, reporting b) introduction to other software
Literature
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • StatSoft, Inc. (2007). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html
  • www.r-project.org
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Legendre, P., Legendre, L. (1998) Numerical ecology. Elsevier, 2nd ed.
  • ter Braak, C.J.F. (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is also listed under the following terms Autumn 2008, Autumn 2009.

Bi8661 Data analysis on PC III

Faculty of Science
Autumn 2014

The course is not taught in Autumn 2014

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
RNDr. Eva Gelnarová (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Bi5040 Biostatistics - basic course && Bi7490 Advanced non-parametric method
This subject is suitable for more advanced students with good knowledge of statistics. More advanced statistical software will be presented.
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
At the end of the course the student will obtain: - overview of usage of R software for statistical analysis - knowledge of decriptive statistics and data visualisation in R software - knowledge of hypothesis testing in R software - knowledge of basic regression modelling in R software - experience in practical data analysis using R software
Syllabus
  • 1. Introduction to R software, basic statistical methods a) Basics of R – history, installation, data loading, data types and structures, function creation, libraries, formatting of outputs, matrix operations b) data visualization – boxplots, histograms, scatter-plots c) data operations, transformation, summary of statistical distributions, correlation coefficients d) Statistical tests – tests of normality, one and two-sample t-test, nonparametric tests e)Analysis of variance. 2. Advanced statistical methods in R a)contingency tables, chi-square test b)linear regression models – estimation of parameters, residual diagnostics, outliers, parameters selection c) generalized linear models – logistic regression, poisson regression d)principal component analysis – eigenvalues. 3. Block based on students requests with possibilities a)analysis of real dataset in R, reporting b) introduction to other software
Literature
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • StatSoft, Inc. (2007). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html
  • www.r-project.org
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Legendre, P., Legendre, L. (1998) Numerical ecology. Elsevier, 2nd ed.
  • ter Braak, C.J.F. (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is also listed under the following terms Autumn 2008, Autumn 2009.

Bi8661 Data analysis on PC III

Faculty of Science
Autumn 2013

The course is not taught in Autumn 2013

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
RNDr. Eva Gelnarová (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Bi5040 Biostatistics - basic course && Bi7490 Advanced non-parametric method
This subject is suitable for more advanced students with good knowledge of statistics. More advanced statistical software will be presented.
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
At the end of the course the student will obtain: - overview of usage of R software for statistical analysis - knowledge of decriptive statistics and data visualisation in R software - knowledge of hypothesis testing in R software - knowledge of basic regression modelling in R software - experience in practical data analysis using R software
Syllabus
  • 1. Introduction to R software, basic statistical methods a) Basics of R – history, installation, data loading, data types and structures, function creation, libraries, formatting of outputs, matrix operations b) data visualization – boxplots, histograms, scatter-plots c) data operations, transformation, summary of statistical distributions, correlation coefficients d) Statistical tests – tests of normality, one and two-sample t-test, nonparametric tests e)Analysis of variance. 2. Advanced statistical methods in R a)contingency tables, chi-square test b)linear regression models – estimation of parameters, residual diagnostics, outliers, parameters selection c) generalized linear models – logistic regression, poisson regression d)principal component analysis – eigenvalues. 3. Block based on students requests with possibilities a)analysis of real dataset in R, reporting b) introduction to other software
Literature
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • StatSoft, Inc. (2007). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html
  • www.r-project.org
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Legendre, P., Legendre, L. (1998) Numerical ecology. Elsevier, 2nd ed.
  • ter Braak, C.J.F. (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is also listed under the following terms Autumn 2008, Autumn 2009.

Bi8661 Data analysis on PC III

Faculty of Science
Autumn 2012

The course is not taught in Autumn 2012

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
RNDr. Eva Gelnarová (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Bi5040 Biostatistics - basic course && Bi7490 Advanced non-parametric method
This subject is suitable for more advanced students with good knowledge of statistics. More advanced statistical software will be presented.
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
At the end of the course the student will obtain: - overview of usage of R software for statistical analysis - knowledge of decriptive statistics and data visualisation in R software - knowledge of hypothesis testing in R software - knowledge of basic regression modelling in R software - experience in practical data analysis using R software
Syllabus
  • 1. Introduction to R software, basic statistical methods a) Basics of R – history, installation, data loading, data types and structures, function creation, libraries, formatting of outputs, matrix operations b) data visualization – boxplots, histograms, scatter-plots c) data operations, transformation, summary of statistical distributions, correlation coefficients d) Statistical tests – tests of normality, one and two-sample t-test, nonparametric tests e)Analysis of variance. 2. Advanced statistical methods in R a)contingency tables, chi-square test b)linear regression models – estimation of parameters, residual diagnostics, outliers, parameters selection c) generalized linear models – logistic regression, poisson regression d)principal component analysis – eigenvalues. 3. Block based on students requests with possibilities a)analysis of real dataset in R, reporting b) introduction to other software
Literature
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • StatSoft, Inc. (2007). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html
  • www.r-project.org
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Legendre, P., Legendre, L. (1998) Numerical ecology. Elsevier, 2nd ed.
  • ter Braak, C.J.F. (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is also listed under the following terms Autumn 2008, Autumn 2009.

Bi8661 Data analysis on PC III

Faculty of Science
Autumn 2011

The course is not taught in Autumn 2011

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
RNDr. Eva Gelnarová (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Prerequisites
Bi5040 Biostatistics - basic course && Bi7490 Advanced non-parametric method
This subject is suitable for more advanced students with good knowledge of statistics. More advanced statistical software will be presented.
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
At the end of the course the student will obtain: - overview of usage of R software for statistical analysis - knowledge of decriptive statistics and data visualisation in R software - knowledge of hypothesis testing in R software - knowledge of basic regression modelling in R software - experience in practical data analysis using R software
Syllabus
  • 1. Introduction to R software, basic statistical methods a) Basics of R – history, installation, data loading, data types and structures, function creation, libraries, formatting of outputs, matrix operations b) data visualization – boxplots, histograms, scatter-plots c) data operations, transformation, summary of statistical distributions, correlation coefficients d) Statistical tests – tests of normality, one and two-sample t-test, nonparametric tests e)Analysis of variance. 2. Advanced statistical methods in R a)contingency tables, chi-square test b)linear regression models – estimation of parameters, residual diagnostics, outliers, parameters selection c) generalized linear models – logistic regression, poisson regression d)principal component analysis – eigenvalues. 3. Block based on students requests with possibilities a)analysis of real dataset in R, reporting b) introduction to other software
Literature
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • StatSoft, Inc. (2007). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html
  • www.r-project.org
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Legendre, P., Legendre, L. (1998) Numerical ecology. Elsevier, 2nd ed.
  • ter Braak, C.J.F. (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is also listed under the following terms Autumn 2008, Autumn 2009.

Bi8661 Data analysis on PC III

Faculty of Science
Autumn 2010

The course is not taught in Autumn 2010

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
RNDr. Eva Gelnarová (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Prerequisites
Bi5040 Biostatistics - basic course && Bi7490 Predictive Modelling
This subject is suitable for more advanced students with good knowledge of statistics. More advanced statistical software will be presented.
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
At the end of the course the student will obtain: - overview of usage of R software for statistical analysis - knowledge of decriptive statistics and data visualisation in R software - knowledge of hypothesis testing in R software - knowledge of basic regression modelling in R software - experience in practical data analysis using R software
Syllabus
  • 1. Introduction to R software, basic statistical methods a) Basics of R – history, installation, data loading, data types and structures, function creation, libraries, formatting of outputs, matrix operations b) data visualization – boxplots, histograms, scatter-plots c) data operations, transformation, summary of statistical distributions, correlation coefficients d) Statistical tests – tests of normality, one and two-sample t-test, nonparametric tests e)Analysis of variance. 2. Advanced statistical methods in R a)contingency tables, chi-square test b)linear regression models – estimation of parameters, residual diagnostics, outliers, parameters selection c) generalized linear models – logistic regression, poisson regression d)principal component analysis – eigenvalues. 3. Block based on students requests with possibilities a)analysis of real dataset in R, reporting b) introduction to other software
Literature
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • StatSoft, Inc. (2007). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html
  • www.r-project.org
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Legendre, P., Legendre, L. (1998) Numerical ecology. Elsevier, 2nd ed.
  • ter Braak, C.J.F. (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is also listed under the following terms Autumn 2008, Autumn 2009.

Bi8661 Data analysis on PC III

Faculty of Science
Autumn 2011 - acreditation

The course is not taught in Autumn 2011 - acreditation

The information about the term Autumn 2011 - acreditation is not made public

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
RNDr. Eva Gelnarová (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Prerequisites
Bi5040 Biostatistics - basic course && Bi7490 Advanced non-parametric method
This subject is suitable for more advanced students with good knowledge of statistics. More advanced statistical software will be presented.
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
At the end of the course the student will obtain: - overview of usage of R software for statistical analysis - knowledge of decriptive statistics and data visualisation in R software - knowledge of hypothesis testing in R software - knowledge of basic regression modelling in R software - experience in practical data analysis using R software
Syllabus
  • 1. Introduction to R software, basic statistical methods a) Basics of R – history, installation, data loading, data types and structures, function creation, libraries, formatting of outputs, matrix operations b) data visualization – boxplots, histograms, scatter-plots c) data operations, transformation, summary of statistical distributions, correlation coefficients d) Statistical tests – tests of normality, one and two-sample t-test, nonparametric tests e)Analysis of variance. 2. Advanced statistical methods in R a)contingency tables, chi-square test b)linear regression models – estimation of parameters, residual diagnostics, outliers, parameters selection c) generalized linear models – logistic regression, poisson regression d)principal component analysis – eigenvalues. 3. Block based on students requests with possibilities a)analysis of real dataset in R, reporting b) introduction to other software
Literature
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • StatSoft, Inc. (2007). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html
  • www.r-project.org
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Legendre, P., Legendre, L. (1998) Numerical ecology. Elsevier, 2nd ed.
  • ter Braak, C.J.F. (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is also listed under the following terms Autumn 2008, Autumn 2009.

Bi8661 Data analysis on PC III

Faculty of Science
Autumn 2010 - only for the accreditation

The course is not taught in Autumn 2010 - only for the accreditation

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
RNDr. Eva Gelnarová (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Prerequisites
Bi5040 Biostatistics - basic course && Bi7490 Advanced non-parametric method
This subject is suitable for more advanced students with good knowledge of statistics. More advanced statistical software will be presented.
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
At the end of the course the student will obtain: - overview of usage of R software for statistical analysis - knowledge of decriptive statistics and data visualisation in R software - knowledge of hypothesis testing in R software - knowledge of basic regression modelling in R software - experience in practical data analysis using R software
Syllabus
  • 1. Introduction to R software, basic statistical methods a) Basics of R – history, installation, data loading, data types and structures, function creation, libraries, formatting of outputs, matrix operations b) data visualization – boxplots, histograms, scatter-plots c) data operations, transformation, summary of statistical distributions, correlation coefficients d) Statistical tests – tests of normality, one and two-sample t-test, nonparametric tests e)Analysis of variance. 2. Advanced statistical methods in R a)contingency tables, chi-square test b)linear regression models – estimation of parameters, residual diagnostics, outliers, parameters selection c) generalized linear models – logistic regression, poisson regression d)principal component analysis – eigenvalues. 3. Block based on students requests with possibilities a)analysis of real dataset in R, reporting b) introduction to other software
Literature
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • StatSoft, Inc. (2007). Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB: http://www.statsoft.com/textbook/stathome.html
  • www.r-project.org
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Legendre, P., Legendre, L. (1998) Numerical ecology. Elsevier, 2nd ed.
  • ter Braak, C.J.F. (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is also listed under the following terms Autumn 2008, Autumn 2009.
  • Enrolment Statistics (recent)