Difference between revisions of "Bachelor Statistics Lecture"
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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]] === | ||
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[[Hypothesis_building#Simple tests |Simple tests]] | [[Hypothesis_building#Simple tests |Simple tests]] | ||
| − | === Day 5 - [[ | + | === Day 5 - [[Correlations|Correlations]] === |
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| − | [[Correlations | + | [[Correlations|Correlations on a shoestring]] |
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| − | [[ | + | [[Correlations#Reading_correlation_plots|Reading correlation plots]] |
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| − | [[ | + | [[Correlations#Correlative_relations|Correlative relations]] |
=== Day 6 - Regression === | === Day 6 - Regression === | ||
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=== Day 10 - Bias === | === Day 10 - Bias === | ||
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| − | [[ | + | [[Bias in statistics]] |
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=== Day 11 - [[Statistics and mixed methods|Statistics and mixed methods]] === | === Day 11 - [[Statistics and mixed methods|Statistics and mixed methods]] === | ||
Latest revision as of 13:21, 11 January 2021
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
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
