| Statuss(Aktīvs) | Izdruka | Arhīvs(0) | Studiju plāns Vecais plāns | Kursu katalogs | Vēsture |
| Course title | Biometry |
| Course code | Biol1015 |
| Credit points (ECTS) | 4.5 |
| Total Hours in Course | 121.5 |
| Number of hours for lectures | 16 |
| Number of hours for seminars and practical classes | 32 |
| Independent study hours | 72 |
| Date of course confirmation | 04/09/2019 |
| 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 | |
| BiolB005 [GBIOB005] Biometry |
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| Course abstract | |
| Student acquire parametric data analyses methods, as also check the model assumptions violations. 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. | |
| Learning outcomes and their assessment | |
| After completing the course student will have: knowledge and critical understanding about data analysis methods (assessment tests); apply data analyses methods in students research projects; skills to define test hypothesis (home works); to choose according to the research project test hypothesis and data analysis method; to interpret results; to formulate conclusions (tests, home work, practical works); competences in collaboration with supervisor to realize research in bachelor work; to use data analysis methods in research projects and bachelor work (the home works are developed) | |
| Course Content(Calendar) | |
| 1.Introduction to biometry. Data classification. [L – 1h]
2.Frequency distributions for qualitative data. [L – 1h; P – 2h] 3.Frequency distributions for quantitative data. Distribution graphical presentation. [L – 1h; P – 2h] 4.Statistical parameters for quantitative data. [L – 1h; P – 2h] 5.Measures of Central Tendency and Variation, Standard error. [L – 1h; P – 2h] 6.Presentation of statistical parameters in study works. [L – 1h; P – 2h] 7.Correlation. [L – 1h; P – 2h] 8.Simple linear regression. [L – 1h; P – 2h] 1st test. Frequency distributions, Statistical parameters, Correlation and regression. [P – 2h] 9.Hypothesis testing. Null and alternative hypothesis. [L – 1h] 10.Empirical and normal distribution. [L – 1h; P – 2h] 11.Two paired samples – t-test. [L – 1h; P – 2h] 12.Two independent samples – F-test. [L – 1h; P – 2h] 13.Two independent samples – t-test. [L – 1h; P – 2h] 14.One way ANOVA. [L – 1h; P – 2h] 15.Two way ANOVA without replication. [L – 1h; P – 2h] 16.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] |
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| Requirements for awarding credit points | |
| Practical works have been developed. Successfully have been completed three tests (80%). Home works have been developed (20%). Examination. | |
| 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 three tests (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. Krastiņš O., Ciemiņa I. Matemātiskā statistika: mācību grāmata. Rīga: LR Centrālā statistikas pārvalde, 2003.
2. Arhipova I., Bāliņa S. Statistika ekonomikā. Risinājumi ar SPSS un Microsoft Excel: mācību grāmata. Rīga: Datorzinību centrs, 2006. 362 lpp. 3. Liepa I. Biometrija: mācību grāmata. Rīga: Zvaigzne, 1974. 335 lpp. |
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| Further reading | |
| 1.Hirsch R. P. Introduction to biostatistical applications in health research with Microsoft Office Excel. Hoboken, New Jersey: Wiley, 2016. 392 p. 2. Arhipova I., Bāliņa S. Statistika ar Microsoft Excel ikvienam. 1daļa. Rīga: Datorzinību centrs, 2000. 3. Arhipova I., Bāliņa S. Statistika ar Microsoft Excel ikvienam. 2daļa. Rīga: Datorzinību centrs, 2000. 4. Zar J. H. Biostatistical Analysis. New Jersey: Published by Prentice Hall, 1999. | |
| Notes | |
| General study course for professional bachelor study programme “Agriculture” | |