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Mathematical Statistics I
Study Course Description
Course Description Statuss:Approved
Course Description Version:10.00
Study Course Accepted:12.08.2022 11:03:38
| Study Course Information | |||||||||
| Course Code: | SL_008 | LQF level: | Level 6 | ||||||
| Credit Points: | 2.00 | ECTS: | 3.00 | ||||||
| Branch of Science: | Mathematics; Theory of Probability and Mathematical Statistics | Target Audience: | Public Health | ||||||
| Study Course Supervisor | |||||||||
| Course Supervisor: | Vinita Cauce | ||||||||
| Study Course Implementer | |||||||||
| Structural Unit: | Statistics Unit | ||||||||
| The Head of Structural Unit: | |||||||||
| Contacts: | Kapseļu Street 23, 2nd floor, Riga, +371 67060897, statistika rsu[pnkts]lv, www.rsu.lv/statlab | ||||||||
| Study Course Planning | |||||||||
| Full-Time - Semester No.1 | |||||||||
| Lectures (count) | 0 | Lecture Length (academic hours) | 0 | Total Contact Hours of Lectures | 0 | ||||
| Classes (count) | 16 | Class Length (academic hours) | 2 | Total Contact Hours of Classes | 32 | ||||
| Total Contact Hours | 32 | ||||||||
| Study course description | |||||||||
| Preliminary Knowledge: | Secondary school background in mathematics and informatics. Preferably informatics lectures should be taken during first year. | ||||||||
| Objective: | To get basic knowledge about descriptive statistics, hypothesis testing and IBM SPSS options in data processing. | ||||||||
| Topic Layout (Full-Time) | |||||||||
| No. | Topic | Type of Implementation | Number | Venue | |||||
| 1 | Introduction to statistics, the role of statistics in research process. Statistical calculation programs (calculators, programs). Introduction to SPSS. | Classes | 1.00 | computer room | |||||
| 2 | Data type. Variables and levels of measurement. | Classes | 1.00 | computer room | |||||
| 3 | Tables and diagrams in IBM SPSS and Excel. | Classes | 1.00 | computer room | |||||
| 4 | Descriptive statistics in IBM SPSS and Excel: frequency calculation, central tendency estimators, variability estimators. | Classes | 3.00 | computer room | |||||
| 5 | Elements of probability theory. Theoretical data distributions. Normal distribution. Standard normal distribution. | Classes | 1.00 | computer room | |||||
| 6 | Confidence intervals, generatng in SPSS and CI calculators. | Classes | 1.00 | computer room | |||||
| 7 | Hypothesis testing. Two quantitative variables (2 samples). Parametric and nonparametric methods. | Classes | 2.00 | computer room | |||||
| 8 | Hypothesis testing. Differences between three or more groups quantitative variables. Parametric and nonparametric methods. | Classes | 2.00 | computer room | |||||
| 9 | Hypothesis testing. Qualitative data. 2 x 2 crosstables. | Classes | 1.00 | computer room | |||||
| 10 | Hypothesis testing. Qualitative data. R x C crosstable. | Classes | 1.00 | computer room | |||||
| 11 | Course summary. Independent work. | Classes | 1.00 | computer room | |||||
| 12 | Independent work presentation. | Classes | 1.00 | computer room | |||||
| Assessment | |||||||||
| Unaided Work: | Independent Learning: Individual working with literature – preparing for a lesson, clarifying uncertain terms, performing home tasks. | ||||||||
| Assessment Criteria: | Active participation in practice. Own-initiative work on the processing of data at the basic level of the testing of descriptive statistics and hypotheses, which requires calculation and interpretation of results. For each delayed lesson, a summary of the subject using the given literature (min one A4 page). At the end of the study course, a written examination: computerised test with 30 questions on representative name sets and decision-making in data processing – 50%, practical task resolution in the IBM SPSS environment – 30%, independent work -20%. | ||||||||
| Final Examination (Full-Time): | Exam (Written) | ||||||||
| Final Examination (Part-Time): | |||||||||
| Learning Outcomes | |||||||||
| Knowledge: | As a result of successful acquisition of the course, students: • will name and explain the basis for the most important descriptive statistics and hypothesis testing in Latvian and English. • recognise basic situations in the processing of descriptive statistics and hypotheses at basic level. | ||||||||
| Skills: | As a result of successful acquisition of the course, students will be able to: • prepare data for processing in the IBM SPSS environment. • select data based on different criteria in the SPSS environment. • take decisions on the calculation of appropriate descriptive statistics, the design of charts and the verification of hypotheses at basic level. • calculate descriptive statistics, design charts and tables. • perform hypothesis testing at the base level in IBM SPSS environments. • interpret data processing results according to speciality. | ||||||||
| Competencies: | As a result of successful learning of the course, students will be able to correctly interpret static indicators by reading scientific literature in a specialty. | ||||||||
| Bibliography | |||||||||
| No. | Reference | ||||||||
| Required Reading | |||||||||
| 1 | Teibe U. Bioloģiskā statistika. LU, 2007. (akceptējams izdevums) | ||||||||
| Additional Reading | |||||||||
| 1 | A. Field. Discovering Statistics using IBM SPSS Statistics. 5th edition, 2018. | ||||||||
| 2 | Petrie A. & Sabin Caroline. Medical Statistics at a Glance. Willey Blackwell, 2020. | ||||||||
