| Course ID: | MWW-SE>BIOSTATISTICS |
| Course title: | BIOSTATISTICS AND METHODS OF DATA COLLECTION |
| Semester: | 1 / Winter |
| ECTS: | 2 |
| Lectures/Classes: | 0 / 30 hours |
| Field of study: | Veterinary Medicine |
| Study cycle: | 1st cycle |
| Type of course: | compulsory |
| Prerequisites: | There is a limit of 16 people registered for a particular course Mathematics, computer science |
| Contact person: | dr hab. inż. Heliodor Wierzbicki, prof. nadzw |
| Short description: | The overall purpose of the course is to provide students with theoretical knowledge and practical skills (application of the SAS computer system to perform statistical analyses) concerning biostatistical methods used when collecting and describing a data set (descriptive statistics) and hypotheses testing (parametric and non-parametric tests). Moreover, correlation and linear regression as well as analysis of variance is taught. |
| Full description: | Methods of data collection; measures of central tendency; measures of variability; random variables and their distributions; types of hypotheses (null and alternative hypothesis); significance level; critical value; rejection region; type I and II errors, power of the statistical test; t-test (single sample; two independent samples; two paired samples); chi-square test (one-way classification, two-way classification); Pearson’s correlation coefficient and simple linear regression; analysis of variance. |
| Bibliography: | 1. Beginning statistics v.1,0. Douglas S. Shafer, Zhiyi Zhang. https://2012books.lardbucket.org/pdfs/beginning-statistics.pdf 2. Basics of statistics. Jarkko Isotalo. http://www.mv.helsinki.fi/home/jmisotal/BoS.pdf |
| Learning outcomes: | Knowledge: - student describes biological phenomena using descriptive statistics - student knows how to perform statistical inference to draw conclusions on biological phenomena Skills: - student can prepare data file for the statistical analyses - student is able to use the SAS system to compute descriptive statistics, perform parametric and non-parametric tests, compute correlation and perform regression analysis as well and analysis of variance Social competences: - student can critically evaluate his own and others’ ideas and select the best statistical methods for analyzed problems |
| Assessment methods and assessment criteria: | Grades obtained from short tests during classes and presentation of self-chosen topic. |