Difference between revisions of "Bachelor Statistics Lecture"
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Day 13 - The Big recap | Day 13 - The Big recap | ||
Revision as of 16:58, 28 February 2020
Overview of the lecture structure
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 ignorance
Day 13 - The Big recap
What did we learn?
Why does it matter?
How to go on?
