Difference between revisions of "Bachelor Statistics Lecture"
From Sustainability Methods
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== Overview of the lecture structure == | == Overview of the lecture structure == | ||
| + | This course provides an introduction to statistics on a bachelor level. | ||
=== Day 1 - [[Why_statistics_matters|Why statistics matters]] === | === Day 1 - [[Why_statistics_matters|Why statistics matters]] === | ||
Revision as of 08:21, 23 September 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 - Statistics and mixed methods
- 1.12 Day 12 - A word on ethics
- 1.13 Day 13 - The Big recap
Overview of the lecture structure
This course provides an introduction to statistics on a bachelor level.
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 in statistics
Bias in analyzing data
A world beyond bias?
