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Research Methodology and Biometrics II

Course developers

Prior knowledge

MežZ4106, Research Methodology and Biometrics I

Course abstract

Course solves the problem of the representativeness of inductive cognition, covers the methods of mathematical processing and analysis of empirical data and the possibilities of their practical application in calculating statistical parameters and testing hypotheses.

Learning outcomes and their assessment

1. The student knows the main methods of empirical data processing (seminars, practical classes and Tests).
2. The student is able to choose the most suitable mathematical methods for the specifics of data using logical analysis, as well as is competent to apply the methods in analysis of empirical data, solving research tasks in forest science (seminars, practical classes and Tests).

Course Content(Calendar)

1. Cognitive deductive and inductive solution. Processing of empirical information and mathematical modeling.
2. Measurement errors. Statistical sets, their types and dimensionality. Representativeness of the sample.
3. Statistical indicators, their classification. Averages.
4. Variance. Representativeness indicators.
5. Statistical evaluation. Mean, standard deviation, and variance representation intervals.
6. Classification of theoretical distributions. Normal distribution.
7. Binomial distribution. Poisson distribution. Stjudent distribution.
8. Testing of hypotheses. Zero hypothesis.
9. Test of fit of empirical distribution to normal distribution. Comparison of samples. Pooling of samples.
10. Conditions and interpretation of variance distribution. Statistical complex. Tasks of analysis of variance (ANOVA).
11. Examining the significance of the factor's influence. Comparison of classes.
12. Trait dependency and infrastructure. Types of correlation. Scatter diagram. Correlation strength indices.
13. Representation interval of correlation coefficient. Comparison and aggregation of correlation coefficients.
14. The concept of regression. Tasks of regression analysis. Choice of regression type. Calculation of regression coefficients.
15. Statistical evaluation of regression equation. Non-linear regression.

16. Multiple linear regression. Linearity test, calculation of coefficients and statistical evaluation.

Requirements for awarding credit points

Course assessment - formal test with a grade
The detailed Materials and methods sections of your research (bachelor thesis). The practical works must be attended and 4 tests passed. 6 laboratory works must be completed and defended (assessment: passed / failed). In the end, an accumulative test with a grade; a grade consists of marks from tests and an evaluation of the developed part of your research.
Delayed laboratory works and seminars as well as failed tests must be rewritten within the time set by the Department of Silviculture.

Description of the organization and tasks of students’ independent work

Systematic theory studies, tests and laboratory work. Preparation for seminars and group works, preparation and presentation of individual assignments and resulting work - sections of own research.

Criteria for Evaluating Learning Outcomes

Knowledge, skills and competence are assessed on a 10-point scale. An oral or written answer is successful if at least 50% of the questions are answered correctly.
The final mark of the course consists of 2 test (each 30%) and the study research paper “Materials and Methods” section (40%).
The tests are evaluated according to the set procedure - after the answers to the questions given in the methodological descriptions at the beginning of each study period, the individual assignments are evaluated based how competently the student is able to apply the acquired theoretical knowledge and skills.

Compulsory reading

1. Levine D. M., Ramsey P.P., Smitd R.K. Applied statistics for Engineers and Scientists: Using Microsoft Excel and MINITAB. Upper Saddle River, New Jersey: Prentice Hall, 2001. 671 p.

Further reading

1. Johnson R.A., Wichern D.W. Applied Multivariate Statistical Analysis. 6th ed. Upper Saddle River: Pearson Education, Inc., 2007. 773 p.
2. Zar J.H. Biostatistical Analysis. 4th edition. Upper Saddle River: Prentice Hall, 1999. 663 p. 3. Everitt B.S., Der A. A handbook of statistical analysis using SAS. London: Chapman & Hall, 1996. 157 p.

Periodicals and other sources

1. Biometrics. Journal of the International Biometric Society. ISSN 1541-0420.
2. The Journal of Modern Applied Statistical Methods (United States). ISSN 1538-9472. 3. Biometrika (United Kingdom. ISSN 1464-3510.

Notes

For students of academic bachelor program “Sustainable forestry”