Bi8600 Multivariate Methods

Faculty of Science
autumn 2021
Extent and Intensity
2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
RNDr. Eva Koriťáková, Ph.D. (lecturer)
RNDr. Michaela Cvanová, Ph.D. (seminar tutor)
Mgr. Lucie Kubínová (seminar tutor)
RNDr. Simona Littnerová, Ph.D. (seminar tutor)
Guaranteed by
RNDr. Jiří Jarkovský, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Timetable
Tue 12:00–14:50 D29/347-RCX2
Prerequisites
Bi5040 Biostatistics or Bi5045 Biostatistics for Computational Biology and Biomedicine. Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
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 aimed on multivariate data analysis with special emphasis on biological and clinical data. The presented methods extend courses of classical univariate biostatistics: extension of univariate distributions and methods into multivariate space, distance and similarity in multivariate space, cluster analysis, dimensionality reduction throught ordinal methods and discrimination analysis.
Learning outcomes
At the end of the course the student is able to: Prepare a dataset for multivariate analysis correctly; Describe multivariate data; Use multivariate statistical tests; Select appropriate distance or similarity metrics; Compute and visualize association matrices; Apply clustering algorithms and have knowledge of their principles; Apply ordination methods and have knowledge of their principles; Choose appropriate method for multidimensional data analysis based on advantages and limitations of the methods;Interpret results of multivariate analysis.
Syllabus
  • Purpose and aims of multivariate data analysis – examples of multivariate data analysis, advantages and disadvantages of multivariate data analysis, data matrices, tabular and graphical visualization of multivariate data.
  • Matrix operations, inverse matrix, characteristic polynomial, eigenvalues and eigenvectors, singular value decomposition (SVD)
  • Multivariate distributions – random variables, descriptive statistics, confidence interval, outliers
  • Multivariate statistical tests – multivariate t-test, multivariate analysis of variance
  • Distance and similarity metrics in multidimensional space
  • Association matrices – calculation and visualization, descriptive statistics, operations with association matrices (Mantel test, MEANSIM, ANOSIM, association matrix regression)
  • Hierarchical cluster analysis – agglomerative methods, divisive methods.
  • Non-hierarchical cluster analysis, identification of optimal number of clusters.
  • Ordination methods – principles of data reduction, selection and extraction of variables.
  • Ordination methods – principal component analysis (PCA)
  • Ordination methods – correspondence analysis (CA), multidimensional scaling (MDS)
  • Basics of data classification, summary of methods for multivariate data analysis.
Literature
  • 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
  • Zar, J.H. (1998) Biostatistical analysis. Prentice Hall, London. 4th ed.
  • Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London
Teaching methods
Theoretical lectures supplemented by commented examples; students are encouraged to ask questions about discussed topics.
Assessment methods
The final examination is in written form and requires knowledge of multivariate methods principles, prerequisites and application.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020.

Bi8600 Multivariate Methods

Faculty of Science
Autumn 2020
Extent and Intensity
2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
RNDr. Eva Koriťáková, Ph.D. (lecturer)
Mgr. Lucie Kubínová (seminar tutor)
RNDr. Simona Littnerová, Ph.D. (seminar tutor)
Guaranteed by
RNDr. Jiří Jarkovský, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Timetable
Tue 12:00–14:50 D29/347-RCX2
Prerequisites
Bi5040 Biostatistics or Bi5045 Biostatistics for Computational Biology and Biomedicine. Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
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 aimed on multivariate data analysis with special emphasis on biological and clinical data. The presented methods extend courses of classical univariate biostatistics: extension of univariate distributions and methods into multivariate space, distance and similarity in multivariate space, cluster analysis, dimensionality reduction throught ordinal methods and discrimination analysis.
Learning outcomes
At the end of the course the student is able to: Prepare a dataset for multivariate analysis correctly; Describe multivariate data; Use multivariate statistical tests; Select appropriate distance or similarity metrics; Compute and visualize association matrices; Apply clustering algorithms and have knowledge of their principles; Apply ordination methods and have knowledge of their principles; Choose appropriate method for multidimensional data analysis based on advantages and limitations of the methods;Interpret results of multivariate analysis.
Syllabus
  • Purpose and aims of multivariate data analysis – examples of multivariate data analysis, advantages and disadvantages of multivariate data analysis, data matrices, tabular and graphical visualization of multivariate data.
  • Matrix operations, inverse matrix, characteristic polynomial, eigenvalues and eigenvectors, singular value decomposition (SVD)
  • Multivariate distributions – random variables, descriptive statistics, confidence interval, outliers
  • Multivariate statistical tests – multivariate t-test, multivariate analysis of variance
  • Distance and similarity metrics in multidimensional space
  • Association matrices – calculation and visualization, descriptive statistics, operations with association matrices (Mantel test, MEANSIM, ANOSIM, association matrix regression)
  • Hierarchical cluster analysis – agglomerative methods, divisive methods.
  • Non-hierarchical cluster analysis, identification of optimal number of clusters.
  • Ordination methods – principles of data reduction, selection and extraction of variables.
  • Ordination methods – principal component analysis (PCA)
  • Ordination methods – correspondence analysis (CA), multidimensional scaling (MDS)
  • Basics of data classification, summary of methods for multivariate data analysis.
Literature
  • 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
  • Zar, J.H. (1998) Biostatistical analysis. Prentice Hall, London. 4th ed.
  • Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London
Teaching methods
Theoretical lectures supplemented by commented examples; students are encouraged to ask questions about discussed topics.
Assessment methods
The final examination is in written form and requires knowledge of multivariate methods principles, prerequisites and application.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, autumn 2021.

Bi8600 Multivariate Methods

Faculty of Science
Autumn 2019
Extent and Intensity
2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
RNDr. Eva Koriťáková, Ph.D. (lecturer)
Mgr. Lucie Kubínová (seminar tutor)
RNDr. Simona Littnerová, Ph.D. (seminar tutor)
Guaranteed by
RNDr. Jiří Jarkovský, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Timetable
Tue 12:00–14:50 D29/347-RCX2
Prerequisites
Bi5040 Biostatistics or Bi5045 Biostatistics for Computational Biology and Biomedicine. Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
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 aimed on multivariate data analysis with special emphasis on biological and clinical data. The presented methods extend courses of classical univariate biostatistics: extension of univariate distributions and methods into multivariate space, distance and similarity in multivariate space, cluster analysis, dimensionality reduction throught ordinal methods and discrimination analysis.
Learning outcomes
At the end of the course the student is able to: Prepare a dataset for multivariate analysis correctly; Describe multivariate data; Use multivariate statistical tests; Select appropriate distance or similarity metrics; Compute and visualize association matrices; Apply clustering algorithms and have knowledge of their principles; Apply ordination methods and have knowledge of their principles; Choose appropriate method for multidimensional data analysis based on advantages and limitations of the methods;Interpret results of multivariate analysis.
Syllabus
  • Purpose and aims of multivariate data analysis – examples of multivariate data analysis, advantages and disadvantages of multivariate data analysis, data matrices, tabular and graphical visualization of multivariate data.
  • Matrix operations, inverse matrix, characteristic polynomial, eigenvalues and eigenvectors, singular value decomposition (SVD)
  • Multivariate distributions – random variables, descriptive statistics, confidence interval, outliers
  • Multivariate statistical tests – multivariate t-test, multivariate analysis of variance
  • Distance and similarity metrics in multidimensional space
  • Association matrices – calculation and visualization, descriptive statistics, operations with association matrices (Mantel test, MEANSIM, ANOSIM, association matrix regression)
  • Hierarchical cluster analysis – agglomerative methods, divisive methods.
  • Non-hierarchical cluster analysis, identification of optimal number of clusters.
  • Ordination methods – principles of data reduction, selection and extraction of variables.
  • Ordination methods – principal component analysis (PCA)
  • Ordination methods – correspondence analysis (CA), multidimensional scaling (MDS)
  • Basics of data classification, summary of methods for multivariate data analysis.
Literature
  • 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
  • Zar, J.H. (1998) Biostatistical analysis. Prentice Hall, London. 4th ed.
  • Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London
Teaching methods
Theoretical lectures supplemented by commented examples; students are encouraged to ask questions about discussed topics.
Assessment methods
The final examination is in written form and requires knowledge of multivariate methods principles, prerequisites and application.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2020, autumn 2021.

Bi8600 Multivariate Methods

Faculty of Science
Autumn 2018
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Mgr. Eva Budinská, Ph.D. (lecturer)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
RNDr. Eva Koriťáková, Ph.D. (lecturer)
Mgr. Lucie Kubínová (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Supplier department: RECETOX – Faculty of Science
Timetable
Mon 17. 9. to Fri 14. 12. Tue 12:00–14:50 D29/347-RCX2
Prerequisites
Bi5040 Biostatistics Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
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 is able to: Prepare a dataset for multivariate analysis correctly; Describe multivariate data; Use multivariate statistical tests; Select appropriate distance or similarity metrics; Compute and visualize association matrices; Apply clustering algorithms and have knowledge of their principles; Apply ordination methods and have knowledge of their principles; Choose appropriate method for multidimensional data analysis based on advantages and limitations of the methods;Interpret results of multivariate analysis.
Syllabus
  • Purpose and aims of multivariate data analysis – examples of multivariate data analysis, advantages and disadvantages of multivariate data analysis, data matrices, tabular and graphical visualization of multivariate data.
  • Matrix operations, inverse matrix, characteristic polynomial, eigenvalues and eigenvectors, singular value decomposition (SVD)
  • Multivariate distributions – random variables, descriptive statistics, confidence interval, outliers
  • Multivariate statistical tests – multivariate t-test, multivariate analysis of variance
  • Distance and similarity metrics in multidimensional space
  • Association matrices – calculation and visualization, descriptive statistics, operations with association matrices (Mantel test, MEANSIM, ANOSIM, association matrix regression)
  • Hierarchical cluster analysis – agglomerative methods, divisive methods.
  • Non-hierarchical cluster analysis, identification of optimal number of clusters.
  • Ordination methods – principles of data reduction, selection and extraction of variables.
  • Ordination methods – principal component analysis (PCA)
  • Ordination methods – correspondence analysis (CA), multidimensional scaling (MDS)
  • Basics of data classification, summary of methods for multivariate data analysis.
Literature
  • 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
  • Zar, J.H. (1998) Biostatistical analysis. Prentice Hall, London. 4th ed.
  • Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London
Teaching methods
Theoretical lectures supplemented by commented examples; students are encouraged to ask questions about discussed topics.
Assessment methods
The final examination is in written form and requires knowledge of multivariate methods principles, prerequisites and application.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2019, Autumn 2020, autumn 2021.

Bi8600 Multivariate Methods

Faculty of Science
autumn 2017
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Mgr. Eva Budinská, Ph.D. (lecturer)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
RNDr. Eva Koriťáková, Ph.D. (lecturer)
Mgr. Lucie Kubínová (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Supplier department: RECETOX – Faculty of Science
Timetable
Mon 18. 9. to Fri 15. 12. Tue 12:00–14:50 D29/347-RCX2
Prerequisites
Bi5040 Biostatistics Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
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 is able to: Prepare a dataset for multivariate analysis correctly; Describe multivariate data; Use multivariate statistical tests; Select appropriate distance or similarity metrics; Compute and visualize association matrices; Apply clustering algorithms and have knowledge of their principles; Apply ordination methods and have knowledge of their principles; Choose appropriate method for multidimensional data analysis based on advantages and limitations of the methods;Interpret results of multivariate analysis.
Syllabus
  • Purpose and aims of multivariate data analysis – examples of multivariate data analysis, advantages and disadvantages of multivariate data analysis, data matrices, tabular and graphical visualization of multivariate data.
  • Matrix operations, inverse matrix, characteristic polynomial, eigenvalues and eigenvectors, singular value decomposition (SVD)
  • Multivariate distributions – random variables, descriptive statistics, confidence interval, outliers
  • Multivariate statistical tests – multivariate t-test, multivariate analysis of variance
  • Distance and similarity metrics in multidimensional space
  • Association matrices – calculation and visualization, descriptive statistics, operations with association matrices (Mantel test, MEANSIM, ANOSIM, association matrix regression)
  • Hierarchical cluster analysis – agglomerative methods, divisive methods.
  • Non-hierarchical cluster analysis, identification of optimal number of clusters.
  • Ordination methods – principles of data reduction, selection and extraction of variables.
  • Ordination methods – principal component analysis (PCA)
  • Ordination methods – correspondence analysis (CA), multidimensional scaling (MDS)
  • Basics of data classification, summary of methods for multivariate data analysis.
Literature
  • 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
  • Zar, J.H. (1998) Biostatistical analysis. Prentice Hall, London. 4th ed.
  • Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London
Teaching methods
Theoretical lectures supplemented by commented examples; students are encouraged to ask questions about discussed topics.
Assessment methods
The final examination is in written form and requires knowledge of multivariate methods principles, prerequisites and application.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.

Bi8600 Multivariate Methods

Faculty of Science
Autumn 2016
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Mgr. Eva Budinská, Ph.D. (lecturer)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
RNDr. Eva Koriťáková, Ph.D. (lecturer)
Mgr. Lucie Kubínová (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Supplier department: RECETOX – Faculty of Science
Timetable
Mon 19. 9. to Sun 18. 12. Tue 12:00–14:50 D29/347-RCX2
Prerequisites
Bi5040 Biostatistics Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
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 is able to: Prepare a dataset for multivariate analysis correctly; Describe multivariate data; Use multivariate statistical tests; Select appropriate distance or similarity metrics; Compute and visualize association matrices; Apply clustering algorithms and have knowledge of their principles; Apply ordination methods and have knowledge of their principles; Choose appropriate method for multidimensional data analysis based on advantages and limitations of the methods;Interpret results of multivariate analysis.
Syllabus
  • Purpose and aims of multivariate data analysis – examples of multivariate data analysis, advantages and disadvantages of multivariate data analysis, data matrices, tabular and graphical visualization of multivariate data.
  • Matrix operations, inverse matrix, characteristic polynomial, eigenvalues and eigenvectors, singular value decomposition (SVD)
  • Multivariate distributions – random variables, descriptive statistics, confidence interval, outliers
  • Multivariate statistical tests – multivariate t-test, multivariate analysis of variance
  • Distance and similarity metrics in multidimensional space
  • Association matrices – calculation and visualization, descriptive statistics, operations with association matrices (Mantel test, MEANSIM, ANOSIM, association matrix regression)
  • Hierarchical cluster analysis – agglomerative methods, divisive methods.
  • Non-hierarchical cluster analysis, identification of optimal number of clusters.
  • Ordination methods – principles of data reduction, selection and extraction of variables.
  • Ordination methods – principal component analysis (PCA)
  • Ordination methods – correspondence analysis (CA), multidimensional scaling (MDS)
  • Basics of data classification, summary of methods for multivariate data analysis.
Literature
  • 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
  • Zar, J.H. (1998) Biostatistical analysis. Prentice Hall, London. 4th ed.
  • Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London
Teaching methods
Theoretical lectures supplemented by commented examples; students are encouraged to ask questions about discussed topics.
Assessment methods
The final examination is in written form and requires knowledge of multivariate methods principles, prerequisites and application.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.

Bi8600 Multivariate Methods

Faculty of Science
Autumn 2015
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Mgr. Eva Budinská, Ph.D. (lecturer)
RNDr. Simona Littnerová, Ph.D. (seminar tutor)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
RNDr. Eva Koriťáková, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Supplier department: RECETOX – Faculty of Science
Timetable
Tue 12:00–14:50 D29/347-RCX2
Prerequisites
Bi5040 Biostatistics Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
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 is able to: Prepare a dataset for multivariate analysis correctly; Describe multivariate data; Use multivariate statistical tests; Select appropriate distance or similarity metrics; Compute and visualize association matrices; Apply clustering algorithms and have knowledge of their principles; Apply ordination methods and have knowledge of their principles; Choose appropriate method for multidimensional data analysis based on advantages and limitations of the methods;Interpret results of multivariate analysis.
Syllabus
  • Purpose and aims of multivariate data analysis – examples of multivariate data analysis, advantages and disadvantages of multivariate data analysis, data matrices, tabular and graphical visualization of multivariate data.
  • Matrix operations, inverse matrix, characteristic polynomial, eigenvalues and eigenvectors, singular value decomposition (SVD)
  • Multivariate distributions – random variables, descriptive statistics, confidence interval, outliers
  • Multivariate statistical tests – multivariate t-test, multivariate analysis of variance
  • Distance and similarity metrics in multidimensional space
  • Association matrices – calculation and visualization, descriptive statistics, operations with association matrices (Mantel test, MEANSIM, ANOSIM, association matrix regression)
  • Hierarchical cluster analysis – agglomerative methods, divisive methods.
  • Non-hierarchical cluster analysis, identification of optimal number of clusters.
  • Ordination methods – principles of data reduction, selection and extraction of variables.
  • Ordination methods – principal component analysis (PCA)
  • Ordination methods – correspondence analysis (CA), multidimensional scaling (MDS)
  • Basics of data classification, summary of methods for multivariate data analysis.
Literature
  • 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
  • Zar, J.H. (1998) Biostatistical analysis. Prentice Hall, London. 4th ed.
  • Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London
Teaching methods
Theoretical lectures supplemented by commented examples; students are encouraged to ask questions about discussed topics.
Assessment methods
The final examination is in written form and requires knowledge of multivariate methods principles, prerequisites and application.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.

Bi8600 Multivariate Methods

Faculty of Science
Autumn 2014
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Mgr. Eva Budinská, Ph.D. (lecturer)
RNDr. Simona Littnerová, Ph.D. (seminar tutor)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
RNDr. Eva Koriťáková, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Supplier department: RECETOX – Faculty of Science
Timetable
Tue 12:00–14:50 D29/347-RCX2
Prerequisites
Bi5040 Biostatistics Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
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 is able to: Prepare a dataset for multivariate analysis correctly; Describe multivariate data; Use multivariate statistical tests; Select appropriate distance or similarity metrics; Compute and visualize association matrices; Apply clustering algorithms and have knowledge of their principles; Apply ordination methods and have knowledge of their principles; Choose appropriate method for multidimensional data analysis based on advantages and limitations of the methods;Interpret results of multivariate analysis.
Syllabus
  • Purpose and aims of multivariate data analysis – examples of multivariate data analysis, advantages and disadvantages of multivariate data analysis, data matrices, tabular and graphical visualization of multivariate data.
  • Matrix operations, inverse matrix, characteristic polynomial, eigenvalues and eigenvectors, singular value decomposition (SVD)
  • Multivariate distributions – random variables, descriptive statistics, confidence interval, outliers
  • Multivariate statistical tests – multivariate t-test, multivariate analysis of variance
  • Distance and similarity metrics in multidimensional space
  • Association matrices – calculation and visualization, descriptive statistics, operations with association matrices (Mantel test, MEANSIM, ANOSIM, association matrix regression)
  • Hierarchical cluster analysis – agglomerative methods, divisive methods.
  • Non-hierarchical cluster analysis, identification of optimal number of clusters.
  • Ordination methods – principles of data reduction, selection and extraction of variables.
  • Ordination methods – principal component analysis (PCA)
  • Ordination methods – correspondence analysis (CA), multidimensional scaling (MDS)
  • Basics of data classification, summary of methods for multivariate data analysis.
Literature
  • 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
  • Zar, J.H. (1998) Biostatistical analysis. Prentice Hall, London. 4th ed.
  • Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London
Teaching methods
Theoretical lectures supplemented by commented examples; students are encouraged to ask questions about discussed topics.
Assessment methods
The final examination is in written form and requires knowledge of multivariate methods principles, prerequisites and application.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.

Bi8600 Multivariate Methods

Faculty of Science
Autumn 2013
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Mgr. Eva Budinská, Ph.D. (lecturer)
RNDr. Simona Littnerová, Ph.D. (seminar tutor)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
RNDr. Eva Koriťáková, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Supplier department: RECETOX – Faculty of Science
Timetable
Tue 10:00–12:50 D29/347-RCX2
Prerequisites
Bi5040 Biostatistics Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
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 is able to: Prepare correct dataset for multivariate analysis; Select appropriate distance or similarity metrics including metrics for biological communities; Apply clustering algorithm and have knowledge of their principles; Apply ordination methods and have knowledge of their principles; Apply linear discriminant analysis and have knowledge of its principles; Have knowledge of advantages and limitations of methods of multivariate analysis; Interpret results of multivariate analysis; Have overview of available software for multivariate analysis of data.
Syllabus
  • Basic mathematical procedures with vectors and matrices. Introduction to mathematical statistics.
  • Correlation structure of multidimensional data. Similarity of parameters and cases (R-mode and Q-mode analysis).
  • Distribution of multidimensional data - basic tests.
  • Cluster analysis. Basic algorithms and finding of optimal metric for analysis. Similarity coefficients.
  • Discrimination analysis - continuous and bivariate data, basic algorithms of discrimination analysis.
  • Logistic regression - comparison with discrimination analysis.
  • Introduction to ordination methods. Multidimensional nominal data. Principal component analysis. Experimental approaches, graphical output. Factor analysis. Correspondence analysis.
  • Canonical correlation. Multivariate processing of species diversity data. Application of Markov chains.
  • Estimating abundance: Mark and recapture techniques, quadrat counts and line transects, distance methods and removal methods.
  • SAR, QSAR, QSAM.
  • Multivariate analysis of variance (MANOVA).
Literature
  • 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
  • Zar, J.H. (1998) Biostatistical analysis. Prentice Hall, London. 4th ed.
  • Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London
Teaching methods
Theoretical lectures supplemented by commented examples; students are encouraged to ask questions about discussed topics.
Assessment methods
The final examination is in written form and requires knowledge of multivariate methods principles, prerequisites and application.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.

Bi8600 Multivariate Statistical Methods

Faculty of Science
Autumn 2012
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
RNDr. Simona Littnerová, Ph.D. (seminar tutor)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Supplier department: RECETOX – Faculty of Science
Timetable
Tue 15:00–17:50 F01B1/709
Prerequisites
Bi5040 Biostatistics Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
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 is able: Prepare correct dataset for multivariate analysis; Select appropriate distance or similarity metrics including metrics for biological communities; Apply clustering alggorithm and have knowledge of their principles; Apply ordination methods and have knowledge of their principles; Apply linear discriminant analysis and have knowledge of its principles; Have knowledge of advantages and limitations of methods of multivariate analysis; Interpret results of multivariate analysis; Have overview of available software for multivariate analysis of data.
Syllabus
  • Basic mathematical procedures with vectors and matrices. Introduction to mathematical statistics.
  • Correlation structure of multidimensional data. Similarity of parameters and cases (R-mode and Q-mode analysis).
  • Distribution of multidimensional data - basic tests.
  • Cluster analysis. Basic algorithms and finding of optimal metric for analysis. Similarity coefficients.
  • Discrimination analysis - continuous and bivariate data, basic algorithms of discrimination analysis.
  • Logistic regression - comparison with discrimination analysis.
  • Introduction to ordination methods. Multidimensional nominal data. Principal component analysis. Experimental approaches, graphical output. Factor analysis. Correspondence analysis.
  • Canonical correlation. Multivariate processing of species diversity data. Application of Markov chains.
  • Estimating abundance: Mark and recapture techniques, quadrat counts and line transects, distance methods and removal methods.
  • SAR, QSAR, QSAM.
  • Multivariate analysis of variance (MANOVA).
Literature
  • 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
  • Zar, J.H. (1998) Biostatistical analysis. Prentice Hall, London. 4th ed.
  • Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London
Teaching methods
Theoretical lectures supplemented by commented examples; students are encouraged to ask quaetions about discussed topics.
Assessment methods
The final examination is in written form and requires knowledge of multivariate methods principles, prerequisites and application.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.

Bi8600 Multivariate Statistical Methods

Faculty of Science
Autumn 2011
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
RNDr. Simona Littnerová, Ph.D. (seminar tutor)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Timetable
Tue 15:00–17:50 F01B1/709
Prerequisites
Bi5040 Biostatistics Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
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 is able: Prepare correct dataset for multivariate analysis; Select appropriate distance or similarity metrics including metrics for biological communities; Apply clustering alggorithm and have knowledge of their principles; Apply ordination methods and have knowledge of their principles; Apply linear discriminant analysis and have knowledge of its principles; Have knowledge of advantages and limitations of methods of multivariate analysis; Interpret results of multivariate analysis; Have overview of available software for multivariate analysis of data.
Syllabus
  • Basic mathematical procedures with vectors and matrices. Introduction to mathematical statistics.
  • Correlation structure of multidimensional data. Similarity of parameters and cases (R-mode and Q-mode analysis).
  • Distribution of multidimensional data - basic tests.
  • Cluster analysis. Basic algorithms and finding of optimal metric for analysis. Similarity coefficients.
  • Discrimination analysis - continuous and bivariate data, basic algorithms of discrimination analysis.
  • Logistic regression - comparison with discrimination analysis.
  • Introduction to ordination methods. Multidimensional nominal data. Principal component analysis. Experimental approaches, graphical output. Factor analysis. Correspondence analysis.
  • Canonical correlation. Multivariate processing of species diversity data. Application of Markov chains.
  • Estimating abundance: Mark and recapture techniques, quadrat counts and line transects, distance methods and removal methods.
  • SAR, QSAR, QSAM.
  • Multivariate analysis of variance (MANOVA).
Literature
  • 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
  • Zar, J.H. (1998) Biostatistical analysis. Prentice Hall, London. 4th ed.
  • Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London
Teaching methods
Theoretical lectures supplemented by commented examples; students are encouraged to ask quaetions about discussed topics.
Assessment methods
The final examination is in written form and requires knowledge of multivariate methods principles, prerequisites and application.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.

Bi8600 Multivariate Statistical Methods

Faculty of Science
Autumn 2010
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Timetable
Tue 18:00–19:50 F01B1/709
Prerequisites
Bi5040 Biostatistics Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
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 is able: Prepare correct dataset for multivariate analysis; Select appropriate distance or similarity metrics including metrics for biological communities; Apply clustering alggorithm and have knowledge of their principles; Apply ordination methods and have knowledge of their principles; Apply linear discriminant analysis and have knowledge of its principles; Have knowledge of advantages and limitations of methods of multivariate analysis; Interpret results of multivariate analysis; Have overview of available software for multivariate analysis of data.
Syllabus
  • Basic mathematical procedures with vectors and matrices. Introduction to mathematical statistics.
  • Correlation structure of multidimensional data. Similarity of parameters and cases (R-mode and Q-mode analysis).
  • Distribution of multidimensional data - basic tests.
  • Cluster analysis. Basic algorithms and finding of optimal metric for analysis. Similarity coefficients.
  • Discrimination analysis - continuous and bivariate data, basic algorithms of discrimination analysis.
  • Logistic regression - comparison with discrimination analysis.
  • Introduction to ordination methods. Multidimensional nominal data. Principal component analysis. Experimental approaches, graphical output. Factor analysis. Correspondence analysis.
  • Canonical correlation. Multivariate processing of species diversity data. Application of Markov chains.
  • Estimating abundance: Mark and recapture techniques, quadrat counts and line transects, distance methods and removal methods.
  • SAR, QSAR, QSAM.
  • Multivariate analysis of variance (MANOVA).
Literature
  • 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
  • Zar, J.H. (1998) Biostatistical analysis. Prentice Hall, London. 4th ed.
  • Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London
Teaching methods
Theoretical lectures supplemented by commented examples; students are encouraged to ask quaetions about discussed topics.
Assessment methods
The final examination is in written form and requires knowledge of multivariate methods principles, prerequisites and application.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.

Bi8600 Multivariate Statistical Methods

Faculty of Science
Autumn 2009
Extent and Intensity
2/0/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Timetable
Tue 16:00–19:50 BR3
Prerequisites
Bi5040 Biostatistics Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
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 is able: Prepare correct dataset for multivariate analysis; Select appropriate distance or similarity metrics including metrics for biological communities; Apply clustering alggorithm and have knowledge of their principles; Apply ordination methods and have knowledge of their principles; Apply linear discriminant analysis and have knowledge of its principles; Have knowledge of advantages and limitations of methods of multivariate analysis; Interpret results of multivariate analysis; Have overview of available software for multivariate analysis of data.
Syllabus
  • Basic mathematical procedures with vectors and matrices. Introduction to mathematical statistics.
  • Correlation structure of multidimensional data. Similarity of parameters and cases (R-mode and Q-mode analysis).
  • Distribution of multidimensional data - basic tests.
  • Cluster analysis. Basic algorithms and finding of optimal metric for analysis. Similarity coefficients.
  • Discrimination analysis - continuous and bivariate data, basic algorithms of discrimination analysis.
  • Logistic regression - comparison with discrimination analysis.
  • Introduction to ordination methods. Multidimensional nominal data. Principal component analysis. Experimental approaches, graphical output. Factor analysis. Correspondence analysis.
  • Canonical correlation. Multivariate processing of species diversity data. Application of Markov chains.
  • Estimating abundance: Mark and recapture techniques, quadrat counts and line transects, distance methods and removal methods.
  • SAR, QSAR, QSAM.
  • Multivariate analysis of variance (MANOVA).
Literature
  • 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
  • Zar, J.H. (1998) Biostatistical analysis. Prentice Hall, London. 4th ed.
  • Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London
Teaching methods
Theoretical lectures supplemented by commented examples; students are encouraged to ask quaetions about discussed topics.
Assessment methods
The final examination is in written form and requires knowledge of multivariate methods principles, prerequisites and application.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Autumn 2007, Autumn 2008, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.

Bi8600 Multivariate Statistical Methods

Faculty of Science
Autumn 2008
Extent and Intensity
2/0/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Timetable
Tue 14:00–17:50 G2,02003
Prerequisites
Bi5040 Biostatistics Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
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
Basic mathematical procedures with vectors and matrices.
Correlation structure of multidimensional data.
Distribution of multidimensional data - basic tests.
Cluster analysis.
Discriminant analysis.
Logistic regression.
Introduction to ordination methods.
Canonical correlation.
Application of Markov chains.
Estimating species abundance.
Multivariate analysis of variance. Th students will obtain skills in correct application of multivariate statistics on biological data.
Syllabus
  • Basic mathematical procedures with vectors and matrices. Introduction to mathematical statistics.
  • Correlation structure of multidimensional data. Similarity of parameters and cases (R-mode and Q-mode analysis).
  • Distribution of multidimensional data - basic tests.
  • Cluster analysis. Basic algorithms and finding of optimal metric for analysis. Similarity coefficients.
  • Discrimination analysis - continuous and bivariate data, basic algorithms of discrimination analysis.
  • Logistic regression - comparison with discrimination analysis.
  • Introduction to ordination methods. Multidimensional nominal data. Principal component analysis. Experimental approaches, graphical output. Factor analysis. Correspondence analysis.
  • Canonical correlation. Multivariate processing of species diversity data. Application of Markov chains.
  • Estimating abundance: Mark and recapture techniques, quadrat counts and line transects, distance methods and removal methods.
  • SAR, QSAR, QSAM.
  • Multivariate analysis of variance (MANOVA).
Literature
  • ter Braak, C.J.F. (1996). Unimodal models to relace species to environment. DLO-Agricultural Mathematics Group, Wageningen
  • Legendre, P., Legendre, L. (1998) Numerical ecology. Elsevier, 2nd ed.
  • Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London
  • Zar, J.H. (1998) Biostatistical analysis. Prentice Hall, London. 4th ed.
Assessment methods
The final examination is in written form and requires knowledge of multivariate methods principles, prerequisites and application.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Autumn 2007, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.

Bi8600 Multivariate Statistical Methods

Faculty of Science
Autumn 2007
Extent and Intensity
2/0/0. 2 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
Mgr. Klára Komprdová, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Timetable
Wed 9:00–13:50 F01B1/709
Prerequisites
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
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
Basic mathematical procedures with vectors and matrices.
Correlation structure of multidimensional data.
Distribution of multidimensional data - basic tests.
Cluster analysis.
Discrimination analysis.
Logistic regression.
Introduction to ordination methods.
Canonical correlation.
Application of Markov chains.
Estimating species abundance.
Multivariate analysis of variance.
Syllabus
  • Basic mathematical procedures with vectors and matrices. Introduction to mathematical statistics.
  • Correlation structure of multidimensional data. Similarity of parameters and cases (R-mode and Q-mode analysis).
  • Distribution of multidimensional data - basic tests.
  • Cluster analysis. Basic algorithms and finding of optimal metric for analysis. Similarity coefficients.
  • Discrimination analysis - continuous and bivariate data, basic algorithms of discrimination analysis.
  • Logistic regression - comparison with discrimination analysis.
  • Introduction to ordination methods. Multidimensional nominal data. Principal component analysis. Experimental approaches, graphical output. Factor analysis. Correspondence analysis.
  • Canonical correlation. Multivariate processing of species diversity data. Application of Markov chains.
  • Estimating abundance: Mark and recapture techniques, quadrat counts and line transects, distance methods and removal methods.
  • SAR, QSAR, QSAM.
  • Multivariate analysis of variance (MANOVA).
Literature
  • MELOUN, Milan and Jiří MILITKÝ. Statistické zpracování experimentálních dat. [1. vyd.]. Praha: Plus, 1994, 839 s. ISBN 80-85297-56-6. info
  • Statistické zpracování experimentálních dat :v chonometrii, biometrii, ekonometrii a v dalších oborech přírodních , technických a společenských věd. Edited by Milan Meloun. 2. vyd. Praha: East Publishing, 1998, xxi, 839 s. ISBN 80-7219-003-2. info
  • HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. 1. vyd. Praha: SNTL - Nakladatelství technické literatury, 1987, 452 s. URL info
  • B. Flury and H. Riedwyl (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
  • LEGENDRE, Pierre and Louis LEGENDRE. Numerical ecology. 2nd engl. ed. Amsterdam: Elsevier, 1998, xv, 853 s. ISBN 0-444-89249-4. info
  • J. H. Zar (1984). Biostatistical analysis. Prentice Hall. New Jersey.
  • G. W. Snedecor, W. G. Cochran (1971). Statistical methods. Iowa State University Press.
  • HAVRÁNEK, Tomáš. Statistika pro biologické a lékařské vědy. 1. vyd. Praha: Academia, 1993, 476 s. ISBN 8020000801. info
  • J. Benedík, L. Dušek (1993) Sbírka příkladů z biostatistiky. Nakladatelství KONVOJ, Brno.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.

Bi8600 Multivariate Statistical Methods

Faculty of Science
Spring 2007
Extent and Intensity
2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
RNDr. Eva Gelnarová (assistant)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Timetable
Tue 9:00–12:50 PUK
Prerequisites
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
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
Basic mathematical procedures with vectors and matrices.
Correlation structure of multidimensional data.
Distribution of multidimensional data - basic tests.
Cluster analysis.
Discrimination analysis.
Logistic regression.
Introduction to ordination methods.
Canonical correlation.
Application of Markov chains.
Estimating species abundance.
Multivariate analysis of variance.
Syllabus
  • Basic mathematical procedures with vectors and matrices. Introduction to mathematical statistics.
  • Correlation structure of multidimensional data. Similarity of parameters and cases (R-mode and Q-mode analysis).
  • Distribution of multidimensional data - basic tests.
  • Cluster analysis. Basic algorithms and finding of optimal metric for analysis. Similarity coefficients.
  • Discrimination analysis - continuous and bivariate data, basic algorithms of discrimination analysis.
  • Logistic regression - comparison with discrimination analysis.
  • Introduction to ordination methods. Multidimensional nominal data. Principal component analysis. Experimental approaches, graphical output. Factor analysis. Correspondence analysis.
  • Canonical correlation. Multivariate processing of species diversity data. Application of Markov chains.
  • Estimating abundance: Mark and recapture techniques, quadrat counts and line transects, distance methods and removal methods.
  • SAR, QSAR, QSAM.
  • Multivariate analysis of variance (MANOVA).
Literature
  • MELOUN, Milan and Jiří MILITKÝ. Statistické zpracování experimentálních dat. [1. vyd.]. Praha: Plus, 1994, 839 s. ISBN 80-85297-56-6. info
  • Statistické zpracování experimentálních dat :v chonometrii, biometrii, ekonometrii a v dalších oborech přírodních , technických a společenských věd. Edited by Milan Meloun. 2. vyd. Praha: East Publishing, 1998, xxi, 839 s. ISBN 80-7219-003-2. info
  • HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. 1. vyd. Praha: SNTL - Nakladatelství technické literatury, 1987, 452 s. URL info
  • B. Flury and H. Riedwyl (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
  • LEGENDRE, Pierre and Louis LEGENDRE. Numerical ecology. 2nd engl. ed. Amsterdam: Elsevier, 1998, xv, 853 s. ISBN 0-444-89249-4. info
  • J. H. Zar (1984). Biostatistical analysis. Prentice Hall. New Jersey.
  • G. W. Snedecor, W. G. Cochran (1971). Statistical methods. Iowa State University Press.
  • HAVRÁNEK, Tomáš. Statistika pro biologické a lékařské vědy. 1. vyd. Praha: Academia, 1993, 476 s. ISBN 8020000801. info
  • J. Benedík, L. Dušek (1993) Sbírka příkladů z biostatistiky. Nakladatelství KONVOJ, Brno.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.

Bi8600 Multivariate Statistical Methods

Faculty of Science
Spring 2006
Extent and Intensity
2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
RNDr. Eva Gelnarová (assistant)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Timetable of Seminar Groups
Bi8600/1: No timetable has been entered into IS.
Bi8600/2: No timetable has been entered into IS.
Prerequisites
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
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
Basic mathematical procedures with vectors and matrices.
Correlation structure of multidimensional data.
Distribution of multidimensional data - basic tests.
Cluster analysis.
Discrimination analysis.
Logistic regression.
Introduction to ordination methods.
Canonical correlation.
Application of Markov chains.
Estimating species abundance.
Multivariate analysis of variance.
Syllabus
  • Basic mathematical procedures with vectors and matrices. Introduction to mathematical statistics.
  • Correlation structure of multidimensional data. Similarity of parameters and cases (R-mode and Q-mode analysis).
  • Distribution of multidimensional data - basic tests.
  • Cluster analysis. Basic algorithms and finding of optimal metric for analysis. Similarity coefficients.
  • Discrimination analysis - continuous and bivariate data, basic algorithms of discrimination analysis.
  • Logistic regression - comparison with discrimination analysis.
  • Introduction to ordination methods. Multidimensional nominal data. Principal component analysis. Experimental approaches, graphical output. Factor analysis. Correspondence analysis.
  • Canonical correlation. Multivariate processing of species diversity data. Application of Markov chains.
  • Estimating abundance: Mark and recapture techniques, quadrat counts and line transects, distance methods and removal methods.
  • SAR, QSAR, QSAM.
  • Multivariate analysis of variance (MANOVA).
Literature
  • MELOUN, Milan and Jiří MILITKÝ. Statistické zpracování experimentálních dat. [1. vyd.]. Praha: Plus, 1994, 839 s. ISBN 80-85297-56-6. info
  • Statistické zpracování experimentálních dat :v chonometrii, biometrii, ekonometrii a v dalších oborech přírodních , technických a společenských věd. Edited by Milan Meloun. 2. vyd. Praha: East Publishing, 1998, xxi, 839 s. ISBN 80-7219-003-2. info
  • HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. 1. vyd. Praha: SNTL - Nakladatelství technické literatury, 1987, 452 s. URL info
  • B. Flury and H. Riedwyl (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
  • LEGENDRE, Pierre and Louis LEGENDRE. Numerical ecology. 2nd engl. ed. Amsterdam: Elsevier, 1998, xv, 853 s. ISBN 0-444-89249-4. info
  • J. H. Zar (1984). Biostatistical analysis. Prentice Hall. New Jersey.
  • G. W. Snedecor, W. G. Cochran (1971). Statistical methods. Iowa State University Press.
  • HAVRÁNEK, Tomáš. Statistika pro biologické a lékařské vědy. 1. vyd. Praha: Academia, 1993, 476 s. ISBN 8020000801. info
  • J. Benedík, L. Dušek (1993) Sbírka příkladů z biostatistiky. Nakladatelství KONVOJ, Brno.
Language of instruction
Czech
Further Comments
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2007, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.

Bi8600 Multivariate Statistical Methods

Faculty of Science
Spring 2005
Extent and Intensity
2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
RNDr. Eva Gelnarová (assistant)
RNDr. Jiří Jarkovský, Ph.D. (assistant)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
Department of Botany and Zoology – Biology Section – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Timetable
Wed 16:00–17:50 Kontaktujte učitele
Prerequisites
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
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
Basic mathematical procedures with vectors and matrices.
Correlation structure of multidimensional data.
Distribution of multidimensional data - basic tests.
Cluster analysis.
Discrimination analysis.
Logistic regression.
Introduction to ordination methods.
Canonical correlation.
Application of Markov chains.
Estimating species abundance.
Multivariate analysis of variance.
Syllabus
  • Basic mathematical procedures with vectors and matrices. Introduction to mathematical statistics.
  • Correlation structure of multidimensional data. Similarity of parameters and cases (R-mode and Q-mode analysis).
  • Distribution of multidimensional data - basic tests.
  • Cluster analysis. Basic algorithms and finding of optimal metric for analysis. Similarity coefficients.
  • Discrimination analysis - continuous and bivariate data, basic algorithms of discrimination analysis.
  • Logistic regression - comparison with discrimination analysis.
  • Introduction to ordination methods. Multidimensional nominal data. Principal component analysis. Experimental approaches, graphical output. Factor analysis. Correspondence analysis.
  • Canonical correlation. Multivariate processing of species diversity data. Application of Markov chains.
  • Estimating abundance: Mark and recapture techniques, quadrat counts and line transects, distance methods and removal methods.
  • SAR, QSAR, QSAM.
  • Multivariate analysis of variance (MANOVA).
Literature
  • MELOUN, Milan and Jiří MILITKÝ. Statistické zpracování experimentálních dat. [1. vyd.]. Praha: Plus, 1994, 839 s. ISBN 80-85297-56-6. info
  • Statistické zpracování experimentálních dat :v chonometrii, biometrii, ekonometrii a v dalších oborech přírodních , technických a společenských věd. Edited by Milan Meloun. 2. vyd. Praha: East Publishing, 1998, xxi, 839 s. ISBN 80-7219-003-2. info
  • HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. 1. vyd. Praha: SNTL - Nakladatelství technické literatury, 1987, 452 s. URL info
  • B. Flury and H. Riedwyl (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
  • LEGENDRE, Pierre and Louis LEGENDRE. Numerical ecology. 2nd engl. ed. Amsterdam: Elsevier, 1998, xv, 853 s. ISBN 0-444-89249-4. info
  • J. H. Zar (1984). Biostatistical analysis. Prentice Hall. New Jersey.
  • G. W. Snedecor, W. G. Cochran (1971). Statistical methods. Iowa State University Press.
  • HAVRÁNEK, Tomáš. Statistika pro biologické a lékařské vědy. 1. vyd. Praha: Academia, 1993, 476 s. ISBN 8020000801. info
  • J. Benedík, L. Dušek (1993) Sbírka příkladů z biostatistiky. Nakladatelství KONVOJ, Brno.
Language of instruction
Czech
Further Comments
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2003, Spring 2004, Spring 2006, Spring 2007, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.

Bi8600 Multivariate Statistical Methods

Faculty of Science
Spring 2004
Extent and Intensity
2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (assistant)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
Department of Botany and Zoology – Biology Section – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Timetable
Fri 14:00–15:50 kamenice
Prerequisites
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
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
Basic mathematical procedures with vectors and matrices.
Correlation structure of multidimensional data.
Distribution of multidimensional data - basic tests.
Cluster analysis.
Discrimination analysis.
Logistic regression.
Introduction to ordination methods.
Canonical correlation.
Application of Markov chains.
Estimating species abundance.
Multivariate analysis of variance.
Syllabus
  • Basic mathematical procedures with vectors and matrices. Introduction to mathematical statistics.
  • Correlation structure of multidimensional data. Similarity of parameters and cases (R-mode and Q-mode analysis).
  • Distribution of multidimensional data - basic tests.
  • Cluster analysis. Basic algorithms and finding of optimal metric for analysis. Similarity coefficients.
  • Discrimination analysis - continuous and bivariate data, basic algorithms of discrimination analysis.
  • Logistic regression - comparison with discrimination analysis.
  • Introduction to ordination methods. Multidimensional nominal data. Principal component analysis. Experimental approaches, graphical output. Factor analysis. Correspondence analysis.
  • Canonical correlation. Multivariate processing of species diversity data. Application of Markov chains.
  • Estimating abundance: Mark and recapture techniques, quadrat counts and line transects, distance methods and removal methods.
  • SAR, QSAR, QSAM.
  • Multivariate analysis of variance (MANOVA).
Literature
  • MELOUN, Milan and Jiří MILITKÝ. Statistické zpracování experimentálních dat. [1. vyd.]. Praha: Plus, 1994, 839 s. ISBN 80-85297-56-6. info
  • Statistické zpracování experimentálních dat :v chonometrii, biometrii, ekonometrii a v dalších oborech přírodních , technických a společenských věd. Edited by Milan Meloun. 2. vyd. Praha: East Publishing, 1998, xxi, 839 s. ISBN 80-7219-003-2. info
  • HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. 1. vyd. Praha: SNTL - Nakladatelství technické literatury, 1987, 452 s. URL info
  • B. Flury and H. Riedwyl (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
  • LEGENDRE, Pierre and Louis LEGENDRE. Numerical ecology. 2nd engl. ed. Amsterdam: Elsevier, 1998, xv, 853 s. ISBN 0-444-89249-4. info
  • J. H. Zar (1984). Biostatistical analysis. Prentice Hall. New Jersey.
  • G. W. Snedecor, W. G. Cochran (1971). Statistical methods. Iowa State University Press.
  • HAVRÁNEK, Tomáš. Statistika pro biologické a lékařské vědy. 1. vyd. Praha: Academia, 1993, 476 s. ISBN 8020000801. info
  • J. Benedík, L. Dušek (1993) Sbírka příkladů z biostatistiky. Nakladatelství KONVOJ, Brno.
Language of instruction
Czech
Further Comments
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2003, Spring 2005, Spring 2006, Spring 2007, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.

Bi8600 Multivariate Statistical Methods

Faculty of Science
Spring 2003
Extent and Intensity
2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (assistant)
RNDr. Jan Mužík, Ph.D. (assistant)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
Department of Botany and Zoology – Biology Section – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Prerequisites
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
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
Basic mathematical procedures with vectors and matrices.
Correlation structure of multidimensional data.
Distribution of multidimensional data - basic tests.
Cluster analysis.
Discrimination analysis.
Logistic regression.
Introduction to ordination methods.
Canonical correlation.
Application of Markov chains.
Estimating species abundance.
Multivariate analysis of variance.
Syllabus
  • Basic mathematical procedures with vectors and matrices. Introduction to mathematical statistics.
  • Correlation structure of multidimensional data. Similarity of parameters and cases (R-mode and Q-mode analysis).
  • Distribution of multidimensional data - basic tests.
  • Cluster analysis. Basic algorithms and finding of optimal metric for analysis. Similarity coefficients.
  • Discrimination analysis - continuous and bivariate data, basic algorithms of discrimination analysis.
  • Logistic regression - comparison with discrimination analysis.
  • Introduction to ordination methods. Multidimensional nominal data. Principal component analysis. Experimental approaches, graphical output. Factor analysis. Correspondence analysis.
  • Canonical correlation. Multivariate processing of species diversity data. Application of Markov chains.
  • Estimating abundance: Mark and recapture techniques, quadrat counts and line transects, distance methods and removal methods.
  • SAR, QSAR, QSAM.
  • Multivariate analysis of variance (MANOVA).
Literature
  • MELOUN, Milan and Jiří MILITKÝ. Statistické zpracování experimentálních dat. [1. vyd.]. Praha: Plus, 1994, 839 s. ISBN 80-85297-56-6. info
  • Statistické zpracování experimentálních dat :v chonometrii, biometrii, ekonometrii a v dalších oborech přírodních , technických a společenských věd. Edited by Milan Meloun. 2. vyd. Praha: East Publishing, 1998, xxi, 839 s. ISBN 80-7219-003-2. info
  • HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. 1. vyd. Praha: SNTL - Nakladatelství technické literatury, 1987, 452 s. URL info
  • B. Flury and H. Riedwyl (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
  • LEGENDRE, Pierre and Louis LEGENDRE. Numerical ecology. 2nd engl. ed. Amsterdam: Elsevier, 1998, xv, 853 s. ISBN 0-444-89249-4. info
  • J. H. Zar (1984). Biostatistical analysis. Prentice Hall. New Jersey.
  • G. W. Snedecor, W. G. Cochran (1971). Statistical methods. Iowa State University Press.
  • HAVRÁNEK, Tomáš. Statistika pro biologické a lékařské vědy. 1. vyd. Praha: Academia, 1993, 476 s. ISBN 8020000801. info
  • J. Benedík, L. Dušek (1993) Sbírka příkladů z biostatistiky. Nakladatelství KONVOJ, Brno.
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.

Bi8600 Multivariate Statistical Methods

Faculty of Science
Autumn 2011 - acreditation

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

Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Prerequisites
Bi5040 Biostatistics Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
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 is able: Prepare correct dataset for multivariate analysis; Select appropriate distance or similarity metrics including metrics for biological communities; Apply clustering alggorithm and have knowledge of their principles; Apply ordination methods and have knowledge of their principles; Apply linear discriminant analysis and have knowledge of its principles; Have knowledge of advantages and limitations of methods of multivariate analysis; Interpret results of multivariate analysis; Have overview of available software for multivariate analysis of data.
Syllabus
  • Basic mathematical procedures with vectors and matrices. Introduction to mathematical statistics.
  • Correlation structure of multidimensional data. Similarity of parameters and cases (R-mode and Q-mode analysis).
  • Distribution of multidimensional data - basic tests.
  • Cluster analysis. Basic algorithms and finding of optimal metric for analysis. Similarity coefficients.
  • Discrimination analysis - continuous and bivariate data, basic algorithms of discrimination analysis.
  • Logistic regression - comparison with discrimination analysis.
  • Introduction to ordination methods. Multidimensional nominal data. Principal component analysis. Experimental approaches, graphical output. Factor analysis. Correspondence analysis.
  • Canonical correlation. Multivariate processing of species diversity data. Application of Markov chains.
  • Estimating abundance: Mark and recapture techniques, quadrat counts and line transects, distance methods and removal methods.
  • SAR, QSAR, QSAM.
  • Multivariate analysis of variance (MANOVA).
Literature
  • 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
  • Zar, J.H. (1998) Biostatistical analysis. Prentice Hall, London. 4th ed.
  • Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London
Teaching methods
Theoretical lectures supplemented by commented examples; students are encouraged to ask quaetions about discussed topics.
Assessment methods
The final examination is in written form and requires knowledge of multivariate methods principles, prerequisites and application.
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.

Bi8600 Multivariate Statistical Methods

Faculty of Science
Autumn 2010 - only for the accreditation
Extent and Intensity
2/1/0. 3 credit(s) (fasci plus compl plus > 4). Recommended Type of Completion: zk (examination). Other types of completion: z (credit).
Teacher(s)
prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Prerequisites
Bi5040 Biostatistics Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
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 is able: Prepare correct dataset for multivariate analysis; Select appropriate distance or similarity metrics including metrics for biological communities; Apply clustering alggorithm and have knowledge of their principles; Apply ordination methods and have knowledge of their principles; Apply linear discriminant analysis and have knowledge of its principles; Have knowledge of advantages and limitations of methods of multivariate analysis; Interpret results of multivariate analysis; Have overview of available software for multivariate analysis of data.
Syllabus
  • Basic mathematical procedures with vectors and matrices. Introduction to mathematical statistics.
  • Correlation structure of multidimensional data. Similarity of parameters and cases (R-mode and Q-mode analysis).
  • Distribution of multidimensional data - basic tests.
  • Cluster analysis. Basic algorithms and finding of optimal metric for analysis. Similarity coefficients.
  • Discrimination analysis - continuous and bivariate data, basic algorithms of discrimination analysis.
  • Logistic regression - comparison with discrimination analysis.
  • Introduction to ordination methods. Multidimensional nominal data. Principal component analysis. Experimental approaches, graphical output. Factor analysis. Correspondence analysis.
  • Canonical correlation. Multivariate processing of species diversity data. Application of Markov chains.
  • Estimating abundance: Mark and recapture techniques, quadrat counts and line transects, distance methods and removal methods.
  • SAR, QSAR, QSAM.
  • Multivariate analysis of variance (MANOVA).
Literature
  • 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
  • Zar, J.H. (1998) Biostatistical analysis. Prentice Hall, London. 4th ed.
  • Flury, B., Riedwyl, H. (1988) Multivariate statistics. A practical approach. Chapman and Hall, London
Teaching methods
Theoretical lectures supplemented by commented examples; students are encouraged to ask quaetions about discussed topics.
Assessment methods
The final examination is in written form and requires knowledge of multivariate methods principles, prerequisites and application.
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.

Bi8600 Multivariate Statistical Methods

Faculty of Science
Autumn 2007 - for the purpose of the accreditation
Extent and Intensity
2/0/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
prof. RNDr. Ladislav Dušek, Ph.D. (lecturer)
RNDr. Jiří Jarkovský, Ph.D. (lecturer)
RNDr. Danka Haruštiaková, Ph.D. (lecturer)
RNDr. Eva Gelnarová (assistant)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: prof. RNDr. Ladislav Dušek, Ph.D.
Prerequisites
Knowledge on basic unidimensional exploratory statistical techniques, analysis of variance, correlation analysis, simple regression.
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
Basic mathematical procedures with vectors and matrices.
Correlation structure of multidimensional data.
Distribution of multidimensional data - basic tests.
Cluster analysis.
Discrimination analysis.
Logistic regression.
Introduction to ordination methods.
Canonical correlation.
Application of Markov chains.
Estimating species abundance.
Multivariate analysis of variance.
Syllabus
  • Basic mathematical procedures with vectors and matrices. Introduction to mathematical statistics.
  • Correlation structure of multidimensional data. Similarity of parameters and cases (R-mode and Q-mode analysis).
  • Distribution of multidimensional data - basic tests.
  • Cluster analysis. Basic algorithms and finding of optimal metric for analysis. Similarity coefficients.
  • Discrimination analysis - continuous and bivariate data, basic algorithms of discrimination analysis.
  • Logistic regression - comparison with discrimination analysis.
  • Introduction to ordination methods. Multidimensional nominal data. Principal component analysis. Experimental approaches, graphical output. Factor analysis. Correspondence analysis.
  • Canonical correlation. Multivariate processing of species diversity data. Application of Markov chains.
  • Estimating abundance: Mark and recapture techniques, quadrat counts and line transects, distance methods and removal methods.
  • SAR, QSAR, QSAM.
  • Multivariate analysis of variance (MANOVA).
Literature
  • MELOUN, Milan and Jiří MILITKÝ. Statistické zpracování experimentálních dat. [1. vyd.]. Praha: Plus, 1994, 839 s. ISBN 80-85297-56-6. info
  • Statistické zpracování experimentálních dat :v chonometrii, biometrii, ekonometrii a v dalších oborech přírodních , technických a společenských věd. Edited by Milan Meloun. 2. vyd. Praha: East Publishing, 1998, xxi, 839 s. ISBN 80-7219-003-2. info
  • HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. 1. vyd. Praha: SNTL - Nakladatelství technické literatury, 1987, 452 s. URL info
  • B. Flury and H. Riedwyl (1988) Multivariate statistics. A practical approach. Chapman and Hall, London.
  • LEGENDRE, Pierre and Louis LEGENDRE. Numerical ecology. 2nd engl. ed. Amsterdam: Elsevier, 1998, xv, 853 s. ISBN 0-444-89249-4. info
  • J. H. Zar (1984). Biostatistical analysis. Prentice Hall. New Jersey.
  • G. W. Snedecor, W. G. Cochran (1971). Statistical methods. Iowa State University Press.
  • HAVRÁNEK, Tomáš. Statistika pro biologické a lékařské vědy. 1. vyd. Praha: Academia, 1993, 476 s. ISBN 8020000801. info
  • J. Benedík, L. Dušek (1993) Sbírka příkladů z biostatistiky. Nakladatelství KONVOJ, Brno.
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Autumn 2010 - only for the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021.
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