Difference between revisions of "Bachelor Statistics Lecture"

From Sustainability Methods
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[[Correlations and regressions|Correlations]]
 
[[Correlations and regressions|Correlations]]
 
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[[Significance in correlations|Significance again]], [[Residuals|residuals]] and sum of squares
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[[Reading correlation plots|Reading correlation plots#Correlations]]
 
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[[Is_the_world_linear?|Linear trends and transformations]]
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[[Significance_in_correlations#Correlations|Significance again]] and [[Residuals#Correlations|residuals]]
 
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[[Reading correlation plots|Reading correlation plots]]
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[[Is_the_world_linear?#Correlations|Linear trends and transformations]]
  
 
=== Day 6 - Regression ===
 
=== Day 6 - Regression ===

Revision as of 08:41, 1 May 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
Reading correlation plots#Correlations
Significance again and residuals
Linear trends and transformations

Day 6 - Regression


Causality
Assumptions & Diagnostics

Interpolation and extrapolation
XX

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


The laboratory 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

The map is not the territory

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?