Bi8660 Data analysis on PC II

Faculty of Science
Spring 2011
Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
Mgr. Tomáš Zdražil (seminar tutor)
Mgr. Lukáš Kohút (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Timetable of Seminar Groups
Bi8660/02: Mon 16:00–17:50 F01B1/709
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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 students obtain: - experience in advanced usage of Statistica software - basics of R software - experience in practacal data analysis
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • Hebák, Petr - Hustopecký, Jiří. Vícerozměrné statistické metody s aplikacemi. Praha : SNTL - Nakladatelství technické literatury, 1987.
  • www.statsoft.com/textbook/stathome.html
  • www.r-project.org
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Follow-Up Courses
Further Comments
Study Materials
The course can also be completed outside the examination period.
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010.

Bi8660 Data analysis on PC II

Faculty of Science
Spring 2010
Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
Mgr. Lukáš Kohút (seminar tutor)
Mgr. Tomáš Zdražil (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Timetable
Tue 16:00–19:50 F01B1/709
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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 students obtain: - experience in advanced usage of Statistica software - basics of R software - experience in practacal data analysis
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • Hebák, Petr - Hustopecký, Jiří. Vícerozměrné statistické metody s aplikacemi. Praha : SNTL - Nakladatelství technické literatury, 1987.
  • www.statsoft.com/textbook/stathome.html
  • www.r-project.org
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Follow-Up Courses
Further Comments
Study Materials
The course can also be completed outside the examination period.
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2011.

Bi8660 Data analysis on PC II

Faculty of Science
Spring 2009
Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
RNDr. Eva Gelnarová (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Timetable of Seminar Groups
Bi8660/01: No timetable has been entered into IS.
Bi8660/02: No timetable has been entered into IS.
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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
Practical statistical techniques on PC. Application and usage of statistical software on PC. Summary statistics and data vizualization in MS Office products. Data analysis in software available on Internet. Introduction to uni-dimensional statistical techniques and associated graphical outputs. Exploratory data analysis. Distribution investigation and distribution fitting, statistical comparison of distribution functions. Normality testing. Parametric and non-parametric two-sample and multiple-sample comparisons. Correlation analysis. Introduction to ANOVA techniques, experimental design.
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • Hebák, Petr - Hustopecký, Jiří. Vícerozměrné statistické metody s aplikacemi. Praha : SNTL - Nakladatelství technické literatury, 1987.
  • www.statsoft.com/textbook/stathome.html
  • www.r-project.org
Assessment methods
The credit for the course is obtained from presence of student on lectures.
Language of instruction
Czech
Follow-Up Courses
Further Comments
The course can also be completed outside the examination period.
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2010, Spring 2011.

Bi8660 Data analysis on PC II

Faculty of Science
Spring 2008
Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
RNDr. Jiří Jarkovský, Ph.D. (seminar tutor)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
RNDr. Eva Gelnarová (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
prof. RNDr. Ladislav Dušek, Ph.D. (seminar tutor)
Guaranteed by
RNDr. Jiří Jarkovský, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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
Practical statistical techniques on PC. Application and usage of statistical software on PC. Summary statistics and data vizualization in MS Office products. Data analysis in software available on Internet. Introduction to uni-dimensional statistical techniques and associated graphical outputs. Exploratory data analysis. Distribution investigation and distribution fitting, statistical comparison of distribution functions. Normality testing. Parametric and non-parametric two-sample and multiple-sample comparisons. Correlation analysis. Introduction to ANOVA techniques, experimental design.
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. Praha: SNTL - Nakladatelství technické literatury, 1987. info
Language of instruction
Czech
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
Note related to how often the course is taught: možno i blokově.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2009, Spring 2010, Spring 2011.

Bi8660 Data analysis on PC II

Faculty of Science
Spring 2007
Extent and Intensity
0/2/0. 2 credit(s) (fasci plus compl plus > 4). Type of Completion: graded credit.
Teacher(s)
RNDr. Jiří Jarkovský, Ph.D. (seminar tutor)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
RNDr. Eva Gelnarová (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
prof. RNDr. Ladislav Dušek, Ph.D. (seminar tutor)
Guaranteed by
RNDr. Jiří Jarkovský, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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
Practical statistical techniques on PC. Application and usage of statistical software on PC. Summary statistics and data vizualization in MS Office products. Data analysis in software available on Internet. Introduction to uni-dimensional statistical techniques and associated graphical outputs. Exploratory data analysis. Distribution investigation and distribution fitting, statistical comparison of distribution functions. Normality testing. Parametric and non-parametric two-sample and multiple-sample comparisons. Correlation analysis. Introduction to ANOVA techniques, experimental design.
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. Praha: SNTL - Nakladatelství technické literatury, 1987. info
Language of instruction
Czech
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
Note related to how often the course is taught: možno i blokově.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2008, Spring 2009, Spring 2010, Spring 2011.

Bi8660 Data analysis on PC II

Faculty of Science
Spring 2006
Extent and Intensity
0/2/0. 2 credit(s) (fasci plus compl plus > 4). Type of Completion: graded credit.
Teacher(s)
RNDr. Jiří Jarkovský, Ph.D. (seminar tutor)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
RNDr. Eva Gelnarová (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
prof. RNDr. Ladislav Dušek, Ph.D. (seminar tutor)
Guaranteed by
RNDr. Jiří Jarkovský, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Timetable of Seminar Groups
Bi8660/Z: No timetable has been entered into IS.
Bi8660/P: No timetable has been entered into IS.
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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
Practical statistical techniques on PC. Application and usage of statistical software on PC. Summary statistics and data vizualization in MS Office products. Data analysis in software available on Internet. Introduction to uni-dimensional statistical techniques and associated graphical outputs. Exploratory data analysis. Distribution investigation and distribution fitting, statistical comparison of distribution functions. Normality testing. Parametric and non-parametric two-sample and multiple-sample comparisons. Correlation analysis. Introduction to ANOVA techniques, experimental design.
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. Praha: SNTL - Nakladatelství technické literatury, 1987. info
Language of instruction
Czech
Further Comments
The course can also be completed outside the examination period.
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011.

Bi8660 Data analysis on PC II

Faculty of Science
Spring 2005
Extent and Intensity
0/2/0. 2 credit(s) (fasci plus compl plus > 4). Type of Completion: graded credit.
Teacher(s)
RNDr. Jiří Jarkovský, Ph.D. (seminar tutor)
prof. RNDr. Ladislav Dušek, Ph.D. (seminar tutor)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
Guaranteed by
RNDr. Jiří Jarkovský, Ph.D.
Department of Botany and Zoology – Biology Section – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Timetable
Wed 14:00–15:50 Kontaktujte učitele
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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
Practical statistical techniques on PC. Application and usage of statistical software on PC. Summary statistics and data vizualization in MS Office products. Data analysis in software available on Internet. Introduction to uni-dimensional statistical techniques and associated graphical outputs. Exploratory data analysis. Distribution investigation and distribution fitting, statistical comparison of distribution functions. Normality testing. Parametric and non-parametric two-sample and multiple-sample comparisons. Correlation analysis. Introduction to ANOVA techniques, experimental design.
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. Praha: SNTL - Nakladatelství technické literatury, 1987. info
Language of instruction
Czech
Further Comments
The course can also be completed outside the examination period.
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011.

Bi8660 Data analysis on PC II

Faculty of Science
Spring 2004
Extent and Intensity
0/2/0. 2 credit(s) (fasci plus compl plus > 4). Type of Completion: graded credit.
Teacher(s)
RNDr. Jiří Jarkovský, Ph.D. (seminar tutor)
prof. RNDr. Ladislav Dušek, Ph.D. (seminar tutor)
prof. RNDr. Luděk Bláha, Ph.D. (seminar tutor)
Mgr. Adam Svobodník, Ph.D. (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
RNDr. Jiří Polách (seminar tutor)
Guaranteed by
RNDr. Jiří Jarkovský, Ph.D.
Department of Botany and Zoology – Biology Section – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Timetable
Fri 16:00–17:50 kamenice
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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
Practical statistical techniques on PC. Application and usage of statistical software on PC. Summary statistics and data vizualization in MS Office products. Data analysis in software available on Internet. Introduction to uni-dimensional statistical techniques and associated graphical outputs. Exploratory data analysis. Distribution investigation and distribution fitting, statistical comparison of distribution functions. Normality testing. Parametric and non-parametric two-sample and multiple-sample comparisons. Correlation analysis. Introduction to ANOVA techniques, experimental design.
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. Praha: SNTL - Nakladatelství technické literatury, 1987. info
Language of instruction
Czech
Further Comments
The course can also be completed outside the examination period.
The course is taught annually.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011.

Bi8660 Data analysis on PC II

Faculty of Science
Spring 2003
Extent and Intensity
0/2/0. 2 credit(s) (fasci plus compl plus > 4). Type of Completion: graded credit.
Teacher(s)
RNDr. Jiří Jarkovský, Ph.D. (seminar tutor)
prof. RNDr. Ladislav Dušek, Ph.D. (seminar tutor)
prof. RNDr. Luděk Bláha, Ph.D. (seminar tutor)
Mgr. Adam Svobodník, Ph.D. (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
RNDr. Jiří Polách (seminar tutor)
Guaranteed by
RNDr. Jiří Jarkovský, Ph.D.
Department of Botany and Zoology – Biology Section – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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
Practical statistical techniques on PC. Application and usage of statistical software on PC. Summary statistics and data vizualization in MS Office products. Data analysis in software available on Internet. Introduction to uni-dimensional statistical techniques and associated graphical outputs. Exploratory data analysis. Distribution investigation and distribution fitting, statistical comparison of distribution functions. Normality testing. Parametric and non-parametric two-sample and multiple-sample comparisons. Correlation analysis. Introduction to ANOVA techniques, experimental design.
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. Praha: SNTL - Nakladatelství technické literatury, 1987. info
Language of instruction
Czech
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
Note related to how often the course is taught: možno i blokově.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011.

Bi8660 Data analysis on PC II

Faculty of Science
Spring 2024

The course is not taught in Spring 2024

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).
Teacher(s)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
RNDr. Eva Gelnarová (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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 students obtain: - experience in advanced usage of Statistica software - basics of R software - experience in practacal data analysis
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • Hebák, Petr - Hustopecký, Jiří. Vícerozměrné statistické metody s aplikacemi. Praha : SNTL - Nakladatelství technické literatury, 1987.
  • www.statsoft.com/textbook/stathome.html
  • www.r-project.org
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
Note related to how often the course is taught: možno i blokově.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011.

Bi8660 Data analysis on PC II

Faculty of Science
Spring 2023

The course is not taught in Spring 2023

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).
Teacher(s)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
RNDr. Eva Gelnarová (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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 students obtain: - experience in advanced usage of Statistica software - basics of R software - experience in practacal data analysis
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • Hebák, Petr - Hustopecký, Jiří. Vícerozměrné statistické metody s aplikacemi. Praha : SNTL - Nakladatelství technické literatury, 1987.
  • www.statsoft.com/textbook/stathome.html
  • www.r-project.org
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
Note related to how often the course is taught: možno i blokově.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011.

Bi8660 Data analysis on PC II

Faculty of Science
Spring 2022

The course is not taught in Spring 2022

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).
Teacher(s)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
RNDr. Eva Gelnarová (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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 students obtain: - experience in advanced usage of Statistica software - basics of R software - experience in practacal data analysis
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • Hebák, Petr - Hustopecký, Jiří. Vícerozměrné statistické metody s aplikacemi. Praha : SNTL - Nakladatelství technické literatury, 1987.
  • www.statsoft.com/textbook/stathome.html
  • www.r-project.org
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
Note related to how often the course is taught: možno i blokově.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011.

Bi8660 Data analysis on PC II

Faculty of Science
Spring 2021

The course is not taught in Spring 2021

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).
Teacher(s)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
RNDr. Eva Gelnarová (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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 students obtain: - experience in advanced usage of Statistica software - basics of R software - experience in practacal data analysis
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • Hebák, Petr - Hustopecký, Jiří. Vícerozměrné statistické metody s aplikacemi. Praha : SNTL - Nakladatelství technické literatury, 1987.
  • www.statsoft.com/textbook/stathome.html
  • www.r-project.org
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
Note related to how often the course is taught: možno i blokově.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011.

Bi8660 Data analysis on PC II

Faculty of Science
Spring 2020

The course is not taught in Spring 2020

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).
Teacher(s)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
RNDr. Eva Gelnarová (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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 students obtain: - experience in advanced usage of Statistica software - basics of R software - experience in practacal data analysis
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • Hebák, Petr - Hustopecký, Jiří. Vícerozměrné statistické metody s aplikacemi. Praha : SNTL - Nakladatelství technické literatury, 1987.
  • www.statsoft.com/textbook/stathome.html
  • www.r-project.org
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
Note related to how often the course is taught: možno i blokově.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011.

Bi8660 Data analysis on PC II

Faculty of Science
Spring 2019

The course is not taught in Spring 2019

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).
Teacher(s)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
RNDr. Eva Gelnarová (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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 students obtain: - experience in advanced usage of Statistica software - basics of R software - experience in practacal data analysis
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • Hebák, Petr - Hustopecký, Jiří. Vícerozměrné statistické metody s aplikacemi. Praha : SNTL - Nakladatelství technické literatury, 1987.
  • www.statsoft.com/textbook/stathome.html
  • www.r-project.org
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
Note related to how often the course is taught: možno i blokově.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011.

Bi8660 Data analysis on PC II

Faculty of Science
spring 2018

The course is not taught in spring 2018

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: z (credit).
Teacher(s)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
RNDr. Eva Gelnarová (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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 students obtain: - experience in advanced usage of Statistica software - basics of R software - experience in practacal data analysis
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • Hebák, Petr - Hustopecký, Jiří. Vícerozměrné statistické metody s aplikacemi. Praha : SNTL - Nakladatelství technické literatury, 1987.
  • www.statsoft.com/textbook/stathome.html
  • www.r-project.org
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
Note related to how often the course is taught: možno i blokově.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011.

Bi8660 Data analysis on PC II

Faculty of Science
Spring 2017

The course is not taught in Spring 2017

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
RNDr. Eva Gelnarová (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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 students obtain: - experience in advanced usage of Statistica software - basics of R software - experience in practacal data analysis
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • Hebák, Petr - Hustopecký, Jiří. Vícerozměrné statistické metody s aplikacemi. Praha : SNTL - Nakladatelství technické literatury, 1987.
  • www.statsoft.com/textbook/stathome.html
  • www.r-project.org
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
Note related to how often the course is taught: možno i blokově.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011.

Bi8660 Data analysis on PC II

Faculty of Science
Spring 2016

The course is not taught in Spring 2016

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
RNDr. Eva Gelnarová (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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 students obtain: - experience in advanced usage of Statistica software - basics of R software - experience in practacal data analysis
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • Hebák, Petr - Hustopecký, Jiří. Vícerozměrné statistické metody s aplikacemi. Praha : SNTL - Nakladatelství technické literatury, 1987.
  • www.statsoft.com/textbook/stathome.html
  • www.r-project.org
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
Note related to how often the course is taught: možno i blokově.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011.

Bi8660 Data analysis on PC II

Faculty of Science
Spring 2015

The course is not taught in Spring 2015

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
RNDr. Eva Gelnarová (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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 students obtain: - experience in advanced usage of Statistica software - basics of R software - experience in practacal data analysis
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • Hebák, Petr - Hustopecký, Jiří. Vícerozměrné statistické metody s aplikacemi. Praha : SNTL - Nakladatelství technické literatury, 1987.
  • www.statsoft.com/textbook/stathome.html
  • www.r-project.org
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
Note related to how often the course is taught: možno i blokově.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011.

Bi8660 Data analysis on PC II

Faculty of Science
Spring 2014

The course is not taught in Spring 2014

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
RNDr. Eva Gelnarová (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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
there are 6 fields of study the course is directly associated with, display
Course objectives
At the end of the course students obtain: - experience in advanced usage of Statistica software - basics of R software - experience in practacal data analysis
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • Hebák, Petr - Hustopecký, Jiří. Vícerozměrné statistické metody s aplikacemi. Praha : SNTL - Nakladatelství technické literatury, 1987.
  • www.statsoft.com/textbook/stathome.html
  • www.r-project.org
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
Note related to how often the course is taught: možno i blokově.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011.

Bi8660 Data analysis on PC II

Faculty of Science
Spring 2013

The course is not taught in Spring 2013

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
RNDr. Eva Gelnarová (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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
there are 6 fields of study the course is directly associated with, display
Course objectives
At the end of the course students obtain: - experience in advanced usage of Statistica software - basics of R software - experience in practacal data analysis
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • Hebák, Petr - Hustopecký, Jiří. Vícerozměrné statistické metody s aplikacemi. Praha : SNTL - Nakladatelství technické literatury, 1987.
  • www.statsoft.com/textbook/stathome.html
  • www.r-project.org
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
Note related to how often the course is taught: možno i blokově.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011.

Bi8660 Data analysis on PC II

Faculty of Science
Spring 2012

The course is not taught in Spring 2012

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
RNDr. Eva Gelnarová (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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
there are 6 fields of study the course is directly associated with, display
Course objectives
At the end of the course students obtain: - experience in advanced usage of Statistica software - basics of R software - experience in practacal data analysis
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • Hebák, Petr - Hustopecký, Jiří. Vícerozměrné statistické metody s aplikacemi. Praha : SNTL - Nakladatelství technické literatury, 1987.
  • www.statsoft.com/textbook/stathome.html
  • www.r-project.org
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
Note related to how often the course is taught: možno i blokově.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011.

Bi8660 Data analysis on PC II

Faculty of Science
Spring 2008 - for the purpose of the accreditation
Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
RNDr. Jiří Jarkovský, Ph.D. (seminar tutor)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
RNDr. Eva Gelnarová (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
prof. RNDr. Ladislav Dušek, Ph.D. (seminar tutor)
Guaranteed by
RNDr. Jiří Jarkovský, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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
Practical statistical techniques on PC. Application and usage of statistical software on PC. Summary statistics and data vizualization in MS Office products. Data analysis in software available on Internet. Introduction to uni-dimensional statistical techniques and associated graphical outputs. Exploratory data analysis. Distribution investigation and distribution fitting, statistical comparison of distribution functions. Normality testing. Parametric and non-parametric two-sample and multiple-sample comparisons. Correlation analysis. Introduction to ANOVA techniques, experimental design.
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • HEBÁK, Petr and Jiří HUSTOPECKÝ. Vícerozměrné statistické metody s aplikacemi. Praha: SNTL - Nakladatelství technické literatury, 1987. info
Language of instruction
Czech
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
Note related to how often the course is taught: možno i blokově.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011.

Bi8660 Data analysis on PC II

Faculty of Science
spring 2012 - acreditation

The course is not taught in spring 2012 - acreditation

The information about the term spring 2012 - acreditation is not made public

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
RNDr. Eva Gelnarová (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Supplier department: RECETOX – Faculty of Science
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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 students obtain: - experience in advanced usage of Statistica software - basics of R software - experience in practacal data analysis
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • Hebák, Petr - Hustopecký, Jiří. Vícerozměrné statistické metody s aplikacemi. Praha : SNTL - Nakladatelství technické literatury, 1987.
  • www.statsoft.com/textbook/stathome.html
  • www.r-project.org
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
Note related to how often the course is taught: možno i blokově.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011.

Bi8660 Data analysis on PC II

Faculty of Science
Spring 2011 - only for the accreditation

The course is not taught in Spring 2011 - only for the accreditation

Extent and Intensity
0/2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: graded credit.
Teacher(s)
RNDr. Danka Haruštiaková, Ph.D. (seminar tutor)
RNDr. Eva Gelnarová (seminar tutor)
RNDr. Jan Mužík, Ph.D. (seminar tutor)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: RNDr. Jiří Jarkovský, Ph.D.
Prerequisites
Basic knowledge of MS Windows, MS Office and basic statistisc.
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 students obtain: - experience in advanced usage of Statistica software - basics of R software - experience in practacal data analysis
Syllabus
  • 1. Computer aided data analyses - introduction and principles of hierarchical data analysis. 2. Software for data analyses, data manipulation within MS-Windows. 3. Grafical features of statistical softwares - graphical presentation of continuous and cathegorial data, examples - model data files. 4. Exploratory and summary statistics - mean, median, confidence intervals, variance - calculations, presentation and interpretation. 5. Data distribution - graphical presentations (histograms, distribution functions), fitting to model distributions, testing of data normality. 6. One-sample testing (one- and two-tailed comparisons). 7. Two-samples comparisons (independent and dependent samples) - assumptions (normality, homogenity of variances) and testing. Parametric tests (independent and paired t-test), nonparametric tests (Mann-Whitney, median test, Wilcoxon test). 8. Introduction to parametric and neparametric corelation analysis. 9. Binomically distributed data - frequencies comparisons, chi-square and its applications, contingency tables. 10. Introduction to analysis of variance - assumptions, experimental design, calculations and results interpretations. 11. Analysis of model data -examples of complex data analysis (exploratory analysis, graphs and plots. 12. experimental design, hypotheses, selection of appropriate test, calculations and interpretations): two-sample testing, correlations, contingecy tables.
Literature
  • Snedecor, G.W., Cochran, W.G.: Statistical methods, Iowa 1971, Iowa State University Press.
  • Zar, J.H.: Biostatistical analysis. New Jersey 1984, Prentice-Hall
  • Benedík, J., Dušek, L: Sbírka příkladů z biostatistiky. Nakladatelství Konvoj 1993, Brno.
  • Hebák, Petr - Hustopecký, Jiří. Vícerozměrné statistické metody s aplikacemi. Praha : SNTL - Nakladatelství technické literatury, 1987.
  • www.statsoft.com/textbook/stathome.html
  • www.r-project.org
Teaching methods
Practical training using computers
Assessment methods
Individual projects on correct application of statistical methods on example data
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
The course can also be completed outside the examination period.
The course is taught annually.
The course is taught: every week.
Note related to how often the course is taught: možno i blokově.
Teacher's information
http://www.cba.muni.cz/vyuka/
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2003, Spring 2004, Spring 2005, Spring 2006, Spring 2007, Spring 2008, Spring 2009, Spring 2010, Spring 2011.
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