Difference between revisions of "Bachelor Statistics Lecture"
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Revision as of 12:35, 28 February 2020
Bachelor Statistics Lecture
Day 1 - Why statistics?
A very short history of statistics.
The Power of statistics
The scientific landscape and statistics
Day 2 - Data formats and descriptive stats Continuous data Data constructs and indices Descriptive statistics
Day 3 - Distribution Normal distribution Other distributions Probabilities
Day 4 - Simple tests Normal distribution Other distributions Probability
Day 5 - Correlation Significance, residuals and sum of squares Reliability and validity Transformations
Day 6 - Regression Causality Prediction XX
Day 7 - Design 1 - Simple Anaya OR the lab experiment Designing an experiment Controlled variables Explained variance
Day 8 - Design 2 - Field experiments Interaction effects Replicates Random factors
Day 9 - Case studies and natural experiments Number of variables vs number of samples Transferability of single cases Meta-Analysis
Day 10 - Bias Bias associated to sampling Bias within analysis Bias related to interpretation of data and analysis
Day 11 - Limits of statistics Mixed methods Statistics and disciplines A qualitative methods view on statistics
Day 12 - A word on ethics Confusion through statistics Statistics serving immoral goals How statistics can fuel anger
Day 13 - The Big recap What did we learn? Why does it matter? How to go on?
