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Lecturer(s)
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Dostál Daniel, PhDr. Ph.D.
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Course content
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The course will cover the following topics: Statistical models Simple regression analysis and the regression curve Parameter estimates, the least squares method Determining model quality Qualitative independent variables, general linear model Interactions Curvilinear dependencies Null hypothesis tests Stepwise and hierarchical regression
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Learning activities and teaching methods
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Lecture, Demonstration
- Attendace
- 25 hours per semester
- Homework for Teaching
- 30 hours per semester
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Learning outcomes
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This course introduces students to the basics of statistical modelling. After attending this course, student will see various statistical procedures as special cases of the general linear model. This approach enables more focused insight into the principles and assumptions of statistical hypothesis testing and parameter estimation with easier to grasp and more natural way for humanities and social sciences students.
The course provides students with one versatile tool for dealing with quantitative problems in social science research. After finishing this course students will be able to abandon usage of the most bivariate tests and replace them with more complex regression models.
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Prerequisites
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The course assumes basic knowledge of descriptive statistics (e.g. mean and standard deviation) and statistical inference (the logic of the null hypotheses testing with p-values).
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Assessment methods and criteria
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Student performance
- Attendance at practical lessons - Successfully completing assignments
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Recommended literature
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Robert I. Kabacoff. (2011). R in Action. Shelter Island.
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