Difference between revisions of "Bachelor Statistics Lecture"
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| − | === Day 9 - | + | === Day 9 - [[Data size and complexity|Case studies and natural experiments]] === |
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=== Day 10 - Bias === | === Day 10 - Bias === | ||
Revision as of 17:40, 1 June 2020
Contents
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1 Overview of the lecture structure
- 1.1 Day 1 - Why statistics matters
- 1.2 Day 2 - Data formats and descriptive stats
- 1.3 Day 3 - Data distribution and Probability
- 1.4 Day 4 - Hypothesis building and simple tests
- 1.5 Day 5 - Correlations
- 1.6 Day 6 - Regression
- 1.7 Day 7 - Design 1 - Simple Anova OR the lab experiment
- 1.8 Day 8 - Design 2 - Field experiments
- 1.9 Day 9 - Case studies and natural experiments
- 1.10 Day 10 - Bias
- 1.11 Day 11 - Limits of statistics
- 1.12 Day 12 - A word on ethics
- 1.13 Day 13 - The Big recap
Overview of the lecture structure
Day 1 - Why statistics matters
The Power of Statistics
Statistics as a part of science
A very short history of statistics
Key concepts of statistics
Day 2 - Data formats and descriptive stats
Continuous data
Data constructs and indices
Descriptive statistics
Day 3 - Data distribution and Probability
Normal distribution
Other distributions
Probability
Day 4 - Hypothesis building and simple tests
Hypothesis testing
Confidence, uncertainty and reliability
Simple tests - a primer
Day 5 - Correlations
Correlations on a shoestring
Reading correlation plots
Correlative relations
Day 6 - Regression
Causality
Residuals
Significance in regressions
Is the world linear?
Interpolation and extrapolation
Day 7 - Design 1 - Simple Anova OR the lab experiment
The laboratory experiment
How do I compare more than two groups?
How to design a study
Day 8 - Design 2 - Field experiments
Day 9 - Case studies and natural experiments
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 ignorance
Day 13 - The Big recap
What did we learn?
Why does it matter?
How to go on?
