Difference between revisions of "Bachelor Statistics Lecture"
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== Overview of the lecture structure == | == Overview of the lecture structure == | ||
| − | === Day 1 - Why statistics | + | === Day 1 - [[Why_statistics_matters|Why statistics matters]] === |
| + | <br> | ||
| + | [[Why_statistics_matters#Statistics_as_a_part_of_science|Statistics as a part of science]] | ||
<br> | <br> | ||
[[Why statistics matters#A very short history of statistics|A very short history of statistics]] | [[Why statistics matters#A very short history of statistics|A very short history of statistics]] | ||
<br> | <br> | ||
| − | [[ | + | [[Why_statistics_matters#Key_concepts_of_statistics|Key concepts of statistics]] |
<br> | <br> | ||
| − | + | [[The Power of Statistics|The Power of Statistics]] | |
=== Day 2 - [[Data formats]] and descriptive stats === | === Day 2 - [[Data formats]] and descriptive stats === | ||
Revision as of 12:49, 27 March 2020
Contents
-
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 - Distribution
- 1.4 Day 4 - Simple tests
- 1.5 Day 5 - Correlation
- 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
Statistics as a part of science
A very short history of statistics
Key concepts of statistics
The Power of Statistics
Day 2 - Data formats and descriptive stats
Day 3 - Distribution
Normal distribution
Other distributions
Probabilities
Day 4 - Simple tests
Hypothesis testing
Confidence and Uncertainty
Ranks and other messy problems
Day 5 - Correlation
Significance again, residuals and sum of squares
Reliability and validity
Linear trends and transformations
Day 6 - Regression
Causality
Assumptions & Diagnostics
Interpolation and extrapolation
XX
Day 7 - Design 1 - Simple Anova OR the lab experiment
Designing experiments for testing a hypothesis
How to design a study
Explained and unexplained variance
Day 8 - Design 2 - Field experiments
Interaction effects
Replicates
Random factors
Day 9 - Case studies and natural experiments
Number of samples vs. number of variables
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?
