Difference between revisions of "Bachelor Statistics Lecture"
From Sustainability Methods
| Line 64: | Line 64: | ||
[[Designing_studies|Designing studies]] | [[Designing_studies|Designing studies]] | ||
| − | === Day 8 - Design 2 - [[ | + | === Day 8 - Design 2 - [[Field experiments|Field experiments]] === |
=== Day 9 - [[Case studies and Natural experiments|Case studies and natural experiments]] === | === Day 9 - [[Case studies and Natural experiments|Case studies and natural experiments]] === | ||
Revision as of 07:48, 30 July 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 - 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 - Statistics and mixed methods
- 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
Ordinal data
Descriptive statistics
Day 3 - Data distribution and Probability
Normal distribution
Non-normal distributions
A matter of Probability
Day 4 - Hypothesis building and simple tests
Hypothesis testing
Validity
Simple tests
Day 5 - Correlations
Correlations on a shoestring
Reading correlation plots
Correlative relations
Day 6 - Regression
Causality
Residuals
Significance in regressions
Is the world linear?
Prediction
Day 7 - Design 1 - Simple Anova OR the lab experiment
The laboratory experiment
How do I compare more than two groups?
Designing studies
Day 8 - Design 2 - Field experiments
Day 9 - Case studies and natural experiments
Day 10 - Bias
Bias
Bias in analyzing data
A world beyond bias?
