Mathematical Statistics I
Study Course Implementer
Kapseļu Street 23, 2nd floor, Riga, +371 67060897, statistika@rsu.lv, www.rsu.lv/statlab
About Study Course
Objective
Preliminary Knowledge
Learning Outcomes
Knowledge
1.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
1.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.
Competences
1.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.
Assessment
Individual work
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Individual work |
-
|
-
|
|
Independent Learning: Individual working with literature – preparing for a lesson, clarifying uncertain terms, performing home tasks.
|
||
Examination
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Examination |
-
|
-
|
|
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%.
|
||
Study Course Theme Plan
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Introduction to statistics, the role of statistics in research process. Statistical calculation programs (calculators, programs). Introduction to SPSS.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Data type. Variables and levels of measurement.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Tables and diagrams in IBM SPSS and Excel.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Descriptive statistics in IBM SPSS and Excel: frequency calculation, central tendency estimators, variability estimators.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Descriptive statistics in IBM SPSS and Excel: frequency calculation, central tendency estimators, variability estimators.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Descriptive statistics in IBM SPSS and Excel: frequency calculation, central tendency estimators, variability estimators.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Elements of probability theory. Theoretical data distributions. Normal distribution. Standard normal distribution.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Confidence intervals, generatng in SPSS and CI calculators.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Hypothesis testing. Two quantitative variables (2 samples). Parametric and nonparametric methods.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Hypothesis testing. Two quantitative variables (2 samples). Parametric and nonparametric methods.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Hypothesis testing. Differences between three or more groups quantitative variables. Parametric and nonparametric methods.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Hypothesis testing. Differences between three or more groups quantitative variables. Parametric and nonparametric methods.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Hypothesis testing. Qualitative data. 2 x 2 crosstables.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Hypothesis testing. Qualitative data. R x C crosstable.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Course summary. Independent work.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
2
|
Topics
|
Independent work presentation.
|
Bibliography
Required Reading
Teibe U. Bioloģiskā statistika. LU, 2007. (akceptējams izdevums)
Additional Reading
A. Field. Discovering Statistics using IBM SPSS Statistics. 5th edition, 2018.
Petrie A. & Sabin Caroline. Medical Statistics at a Glance. Willey Blackwell, 2020.