Difference between revisions of "Bachelor Statistics Lecture"
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Day 2 - Data formats and descriptive stats | Day 2 - Data formats and descriptive stats | ||
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Continuous data | Continuous data | ||
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Data constructs and indices | Data constructs and indices | ||
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Descriptive statistics | Descriptive statistics | ||
Day 3 - Distribution | Day 3 - Distribution | ||
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Normal distribution | Normal distribution | ||
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Other distributions | Other distributions | ||
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Probabilities | Probabilities | ||
Day 4 - Simple tests | Day 4 - Simple tests | ||
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Normal distribution | Normal distribution | ||
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Other distributions | Other distributions | ||
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Probability | Probability | ||
Day 5 - Correlation | Day 5 - Correlation | ||
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Significance, residuals and sum of squares | Significance, residuals and sum of squares | ||
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Reliability and validity | Reliability and validity | ||
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Transformations | Transformations | ||
Day 6 - Regression | Day 6 - Regression | ||
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Causality | Causality | ||
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Prediction | Prediction | ||
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XX | XX | ||
Day 7 - Design 1 - Simple Anaya OR the lab experiment | Day 7 - Design 1 - Simple Anaya OR the lab experiment | ||
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Designing an experiment | Designing an experiment | ||
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Controlled variables | Controlled variables | ||
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Explained variance | Explained variance | ||
Day 8 - Design 2 - Field experiments | Day 8 - Design 2 - Field experiments | ||
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Interaction effects | Interaction effects | ||
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Replicates | Replicates | ||
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Random factors | Random factors | ||
Day 9 - Case studies and natural experiments | Day 9 - Case studies and natural experiments | ||
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Number of variables vs number of samples | Number of variables vs number of samples | ||
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Transferability of single cases | Transferability of single cases | ||
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Meta-Analysis | Meta-Analysis | ||
Day 10 - Bias | Day 10 - Bias | ||
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Bias associated to sampling | Bias associated to sampling | ||
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Bias within analysis | Bias within analysis | ||
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Bias related to interpretation of data and analysis | Bias related to interpretation of data and analysis | ||
Day 11 - Limits of statistics | Day 11 - Limits of statistics | ||
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Mixed methods | Mixed methods | ||
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Statistics and disciplines | Statistics and disciplines | ||
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A qualitative methods view on statistics | A qualitative methods view on statistics | ||
Day 12 - A word on ethics | Day 12 - A word on ethics | ||
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Confusion through statistics | Confusion through statistics | ||
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Statistics serving immoral goals | Statistics serving immoral goals | ||
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How statistics can fuel anger | How statistics can fuel anger | ||
Day 13 - The Big recap | Day 13 - The Big recap | ||
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What did we learn? | What did we learn? | ||
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Why does it matter? | Why does it matter? | ||
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How to go on? | How to go on? | ||
Revision as of 14:14, 28 February 2020
Bachelor Statistics Lecture
Day 1 - Why statistics?
A very short history of statistics.
The Power of statistics
The scientific landscape and statistics
Day 2 - Data formats and descriptive stats
Continuous data
Data constructs and indices
Descriptive statistics
Day 3 - Distribution
Normal distribution
Other distributions
Probabilities
Day 4 - Simple tests
Normal distribution
Other distributions
Probability
Day 5 - Correlation
Significance, residuals and sum of squares
Reliability and validity
Transformations
Day 6 - Regression
Causality
Prediction
XX
Day 7 - Design 1 - Simple Anaya OR the lab experiment
Designing an experiment
Controlled variables
Explained variance
Day 8 - Design 2 - Field experiments
Interaction effects
Replicates
Random factors
Day 9 - Case studies and natural experiments
Number of variables vs number of samples
Transferability of single cases
Meta-Analysis
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 anger
Day 13 - The Big recap
What did we learn?
Why does it matter?
How to go on?
