Difference between revisions of "Bachelor Statistics Lecture"

From Sustainability Methods
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=== Day 11 - [[Statistics and mixed methods|Statistics and mixed methods]] ===
 
=== Day 11 - [[Statistics and mixed methods|Statistics and mixed methods]] ===
  
=== Day 12 - A word on ethics ===
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=== Day 12 - [[A word on ethics|A word on ethics]] ===
  
 
=== Day 13 - The Big recap ===
 
=== Day 13 - The Big recap ===

Revision as of 09:31, 21 June 2020

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
Data constructs and indices
Descriptive statistics

Day 3 - Data distribution and Probability


Normal distribution
Other distributions
Probability

Day 4 - Hypothesis building and simple tests


Hypothesis testing
Confidence, uncertainty and reliability
Simple tests - a primer

Day 5 - Correlations


Correlations on a shoestring
Reading correlation plots
Correlative relations

Day 6 - Regression


Causality
Residuals
Significance in regressions
Is the world linear?
Interpolation and extrapolation

Day 7 - Design 1 - Simple Anova OR the lab experiment


The laboratory experiment
How do I compare more than two groups?
How to design a study

Day 8 - Design 2 - Field experiments

Day 9 - Case studies and natural experiments

Day 10 - Bias


Groundwork
Different biases
How to deal with biases?

Day 11 - Statistics and mixed methods

Day 12 - A word on ethics

Day 13 - The Big recap


What did we learn?
Why does it matter?
How to go on?