| Course title | Biometry |
| Course code | BiolB005 |
| Credit points (ECTS) | 4 |
| Total Hours in Course | 108 |
| Number of hours for lectures | 16 |
| Number of hours for seminars and practical classes | 28 |
| Number of hours for laboratory classes | 0 |
| Independent study hours | 64 |
| Date of course confirmation | 13/12/2023 |
| Responsible Unit | Institute of Computer Systems and Data Science |
| Course developers | |
| Dr. agr., prof. Līga Paura |
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| There is no prerequisite knowledge required for this course | |
| Replaced course | |
| Biol1015 [GBIO1015] Biometry |
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| Course abstract | |
| The course aims are to provide students with parametric data analyses methods, as also check the model assumptions. During the studies, the real examples related to agriculture sciences are using. Statistical software is provides for problem tasks solving. The course topics are the following: descriptive statistic, hypothesis testing for two samples a mean, analyse of variance (ANOVA), correlation and regression. The aim of the study course is to provide students with in-depth knowledge in the application of data analysis methods in experimental data processing. | |
| Learning outcomes and their assessment | |
| After completing the course student will have:
Knowledge: able to demonstrate in-depth knowledge and understanding of data processing methods and their application in experimental data processing (tests and homework) Skills: able to formulate hypotheses; choose a data processing method according to the research and the define hypothesis; interpret the obtained results, formulate conclusions (tests, practical works, homework); Competences: in cooperation with the supervisor, is able to carry out bachelor's thesis research; able to apply data processing methods in the development of projects and bachelor thesis (homework developed independently). |
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| Course Content(Calendar) | |
| 1.Introduction to biometry. Data classification. [L – 1h]
2.Statistical parameters for quantitative data. [L – 1h; P – 1h] 3.Measures of Central Tendency and Variation, Standard error. [L – 1h; P – 2h] 4.Presentation of statistical parameters in study works. [L – 1h; P – 1h] 5.Correlation. [L – 1h; P – 2h] 6.Simple linear regression. [L – 2h; P – 2h] 1st test. Statistical parameters, Correlation and regression. [P – 2h] 7.Hypothesis testing. Null and alternative hypothesis. [L – 1h] 8.Empirical and normal distribution. [L – 1h; P – 1h] 9.Two paired samples – t-test. [L – 1h; P – 2h] 10.Two independent samples – F-test. [L – 1h; P – 1h] 11.Two independent samples – t-test. [L – 1h; P – 2h] 12.One way ANOVA. [L – 1h; P – 1h] 13.Two way ANOVA without replication. [L – 1h; P – 2h] 14.Two way ANOVA with replication. Interaction effect. [L – 1h; P – 2h] 2 nd test. Two samples tests, One-way and two-way ANOVA. [P – 2h] Part time extramural studies: All topics, that are included for full time studies are accomplished, but the number of contact hours is one-half of the hours specified in the calendar. |
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| Requirements for awarding credit points | |
| Examination. Examination include a theoretical subjects acquired during the study course and a practical tasks on the course subjects. All practical work and two tests should be executed. Four home works have been developed. | |
| Description of the organization and tasks of students’ independent work | |
| Independent work: 4 home works are 20% from the final mark, 0.5 each home work. Home works executed only at specified time. Home works topics: 1st – statistical parameters, correlation and regression; 2nd – two samples evaluation; 3rd – one way ANOVA; 4th – two way ANOVA, students select the data themselves. | |
| Criteria for Evaluating Learning Outcomes | |
| Exam evaluation depends on the cumulative assessment of two tests with calculations (80%) and independent works (20%). Tests can be written only at specified time and once. Students who have a cumulative assessment of the study course less than 4 or wish to improve it (have at least 4) have examination. The exam includes practical part (80%) and theory (20%). The exam will be during the period of individual studies and examinations. | |
| Compulsory reading | |
| 1. Essentials of Statistics in Agricultural Sciences / edited by Pradeep Mishra, Fozia Homa. Oakville, ON ;Palm Bay, Florida : Apple Academic Press, [2019] xiv, 526 p. 2. Sokal R.R. , Rohlf F.J. Biometry: the principles and practice of statistics in biological research. New York, NY: W.H. Freeman and Co., 2012. 937 p. | |
| Further reading | |
| 1. Liepa I. Biometrija: mācību grāmata. Rīga: Zvaigzne, 1974. 335 lpp.
2. Arhipova I., Bāliņa S. Statistika ekonomikā. Risinājumi ar SPSS un Microsoft Excel: mācību grāmata. Rīga: Datorzinību centrs, 2006. 362 lpp. |
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| Notes | |
| Study course for Bachelor study programme “Agriculture” | |