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Biometry

Course developer

Prior knowledge

Mate4014, Mathematics I

Course abstract

Biometry solves the problem of inductive cognition representativeness, examines the methods of collecting empirical information, mathematical data processing and analysis and possibilities to apply them in practice, students learn to calculate descriptive statistics and test hypotheses, analysis of variance, correlation and regression.

Learning outcomes and their assessment

After the completing of study course students know the main empirical data processing methods (1st laboratory work, 1st test), using logical analysis, is able to choose the most suitable mathematical methods for data specifics, as well as is competent in the application and practical implementation of basic empirical data processing methods (2nd, 3rd laboratory work, 2nd test), solving research tasks in forest science (4th, 5th, 6th laboratory work, 3rd, 4th test).

Course Content(Calendar)

LECTURES
1. Cognitive deductive and inductive solution. Empirical information processing and mathematical modeling (1 hour)
2. Measurement errors. Statistical sets, their types and dimensionality. Representativeness of the sample (1 hour)
3. Descriptive statistics, their classification. Averages. Dispersion rates. Representativeness indicators. (1 hour)
Test I. Calculation of descriptive statistics of sample (1 hour)
4. Statistical evaluation. Mean, standard deviation and variance representation intervals. (1 hour)
5. Classification of theoretical distributions. Normal distribution. Binomial distribution. Poisson distribution. Student's distribution. (1 hour)
6. Hypothesis testing. Null hypothesis. (1 hour)
7. Verification of the correspondence between the empirical and normal distributions. Comparison of sample groups. Aggregation of samples. (1 hour)
Test II. Calculation of representation intervals in the general population. Hypothesis testing. (1 hour)
8. Conditions and interpretation of variance distribution. Statistical complex. Tasks of analysis of variance. (1 hour)
9. Checking the significance of the factor effect. Comparison of graduation classes. (1 hour)
Test III. Analysis of variance. (1 hour)
10. Feature dependence and infrastructure. Types of correlation. Scatterplot. Correlation strength indicators. Correlation coefficient representation interval. Comparison and aggregation of correlation coefficients (1 hour)
11. The concept of regression. Regression analysis tasks. Choice of regression type. Calculation of regression coefficients. (1 hour)
12. Statistical evaluation of the regression equation. Nonlinear regression. Multiple linear regression. Linearity test, calculation of coefficients and statistical evaluation. (1 hour)
IV test. Correlation analysis. Regression analysis (1 hour)
LABORATORY WORKS
1. Calculation and analysis of descriptive statistics.
2. Calculation of representation intervals.
3. Hypothesis testing.
4. Analysis of variance.
5. Correlation analysis.
6. Regression analysis.

Requirements for awarding credit points

During the semester, 6 laboratory works must be worked out and defended (passed / not passed).
During the semester, 4 tests must be successfully written (the minimum successful assessment in the test is grade 4).
The study course ends with an accumulative test, the assessment is calculated as the arithmetic mean of the marks obtained in the tests.

Criteria for Evaluating Learning Outcomes

The final evaluation of the study course depends on the marks obtained in the tests (the minimum successful evaluation in the test is 4 points). The test mark is calculated as the arithmetic mean of 4 test marks
The final grade is available to those students who have worked out and defended 6 laboratory works during the semester (assessment - passed).
A student can obtain a successful grade in the test if at least 50% of the questions are answered correctly.
Laboratory work is credited if all assigned practical tasks have been successfully completed, the work has been designed and successfully defended.

Compulsory reading

1. Arhipova I., Bāliņa S. Statistika ekonomikā. Risinājumi ar SPSS un MS Excel. Rīga: Datorzinību centrs, 2003. 354 lpp.
2. Krastiņš O., Ciemiņa I. Statistika: mācību grāmata. Rīga: LR Centrālā statistikas pārvalde, 2003. 267 lpp.
3. Sokal, Robert R. Biometry: the principles and practice of statistics in biological research / Robert R. Sokal and F. James Rohlf. 4th ed. New York, NY: W.H. Freeman and Co., 2012. 937 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. Van Emden, H. F. Statistics for Terrified Biologists / Helmut F. van Emden (The University of Reading, UK). Second edition. Hoboken, NJ: Wiley-Blackwell, 2019. 402 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

The study course is included in the compulsory (A) part of the academic study program “Forest Science”.