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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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Simple regression and its graphical representation Standardized regression coefficients Overall accuracy of the model Multiple regression General linear model, categorical variables Interactions Examination of nonlinear relationships Statistical significance tests Assumptions of linear models Stepwise and hierarchical regression Removing the effect of variable, residuals
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Learning activities and teaching methods
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Monologic Lecture(Interpretation, Training), Projection (static, dynamic)
- Preparation for the Exam
- 30 hours per semester
- Homework for Teaching
- 40 hours per semester
- Attendace
- 4 hours per semester
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Learning outcomes
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The topic of the course are linear statistical models. The aim is to connect students existing knowledge of parametric statistics and to show these methods can be perceived as special cases of linear model. Students gain skills of a flexible and confident use of linear models to solve complex statistical problems.
Ability to design a statistical model, to estimate its parameters, to evaluate its quality, to perform related statistical tests, and to check its assumptions. The emphasis is on practical application of acquired skills in the context of psychological research is held.
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Prerequisites
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Knowledge of descriptive statistics and bacis tests of null hypotheses.
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Assessment methods and criteria
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Student performance
Assignments during the semester. Practical exam.
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Recommended literature
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