Course code Biol1014

Credit points 3

# Basics of Biometry

Total Hours in Course81

Number of hours for lectures16

Number of hours for seminars and practical classes16

Independent study hours49

Date of course confirmation04.09.2019

Responsible UnitInstitute of Computer Systems and Data Science

### Course developer

Datoru sistēmu un datu zinātnes institūts

## Līga Zvirgzdiņa

Dr. oec.

### Course abstract

Students acquire widely used mathematical statistics techniques for analysis of biological data. Students learn about systematization of biological experiment data and their graphic display. Students achieve a competence about sampling, calculation and evaluation of descriptive statistics, statistical parameters and hypothesis testing, as well as, about interpretation of results.

### Learning outcomes and their assessment

Upon successful completion of this course:
1. Students acquire knowledge and critical understanding of mathematical statistics methods and their practical application for the processing of observation data in biology. – tests
2. Students are able to show understanding of the corresponding concept and regularities, to perform necessary caluculations and operations. Students are able to use appropriate software for calculations. - practical and laboratory work
3. Working in a group or doing work independently, student is able to apply the mathematical statistics methods corresponding to the specialty problem situation, to make a professional assessment and interpretation of the intermediate and the final results in the course paper, students scientific work or in the development and presentation of internship practice report. – independent studies

### Course Content(Calendar)

1. Data collection. Sampling. Population. Sample. Organizing data. Organizing categorical and numerical data. Frequency distribution. Relative frequency distribution. Percentage distribution. Cumulative distribution.(4h)
2. Visualizing data. Visualizing of results of biological studies.(4h)
3. Numerical descriptive measures. Central tendency. Variation and shape. Exploring numerical data. Descriptive statistics for grouped and ungrouped data. Pitfalls and ethical issues. The importance of statistical indicators for the characterization of the results of biological studies.(5h)
4. The probability distribution. The probability distribution for a discrete random variable. Binomial distribution. Poisson distribution. Continuous random variable. The Normal distribution. Standardized normal random variable. The importance of Normal distribution in biology.(2h)
Test 1. Descriptive statistics.
5. Hypothesis testing. Fundamentals of hypothesis testing methodology. The basic principles of hypothesis testing in biology. The assumptions of each hypothesis-testing procedure, how to evaluate them. Pitfalls involved in hypothesis testing. Statistical conclusions. Region of nonrejection, region of rejection. Hypothesis testing methods.(2h)
6. Hypothesis testing for mean. Level of significance. Confidence interval for mean in biology.(2h)
7. Two-Sample Tests. The means of two independent populations. The means of two related populations. Comparing the Means (T-tests). The proportions of two independent populations. The variances of two independent populations. F-test for the ratio of two variances (F-test). Interpretation of results in biology.(4h)
8. Chi-square test for the difference between two proportions. Chi-square test of independence. Interpretation of results in biology. (2h)
9. Analysis of variance. The basic concepts of experimental design. One-way analysis of variance for differences among the means of several groups. Two-way analysis of variance and the interaction effect. Interpretation of results in biology.(3h)
10. Correlation and regression. Calculation and evaluation of the coefficient of correlation. Simple linear regression. The importance of correlation and regression in the biological studies.(2h)
Test 2. Hypothesis testing methods. Correlation and regression.

### Requirements for awarding credit points

The course is completed without additional knowledge examination if the results of the semester are summarized as 2 successful written tests (average grade at least 4)

### Description of the organization and tasks of students’ independent work

Each week tasks for independent work are assigned.

### Criteria for Evaluating Learning Outcomes

During the semester, 2 written tests, each scored with 8 as a maximum . Student may use his or her records.
For an justified reason, the test may be written in another time.
For raising the accumulating grade (2 as a maximum), student answers the theory.
Without fulfilling the conditions of accumulating grade, the student shall be responsible, during the individual study and testing period, for all topics learned during the semester, - formal test in the written form.

1. Arhipova I., Bāliņa S. Statistika ar MS Excel ikvienam. 1. daļa Rīga: Datorzinību Centrs, 1999. 163 lpp.
2. Arhipova I., Ramute L., Paura L. Datu statistiskā apstrāde ar MS Excel. Jelgava: LLU izdevniecība, 1998. 159 lpp.
3. Arhipova I., Ramute L., Žuka L. Matemātiskās statistikas uzdevumu risināšana ar MS Excel. I Jelgava: LLU izdevniecība, 1997. 121 lpp.
4. Arhipova I., Ramute L., Žuka L. Matemātiskās statistikas uzdevumu risināšana ar MS Excel. II Jelgava: LLU izdevniecība. 1997. 98 lpp.