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Course Outline
The Value of Statistics for Decision Makers
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Descriptive Statistics
- Basic statistics: Identifying which measures (e.g., median, mean, percentiles) are most relevant for different data distributions
- Data visualization: Understanding the significance of accurate representation and how graph design influences decision-making
- Variable types: Determining which variables are easier to manage and interpret
- Constant change: Recognizing that external conditions are rarely static (ceteris paribus)
- The third variable problem: Techniques for identifying the true underlying influencer
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Inferential Statistics
- P-values: Understanding their true meaning and implications
- Repeated experiments: How to interpret results from repeated trials
- Data collection: Acknowledging that while bias can be minimized, it cannot be entirely eliminated
- Confidence levels: Gaining a clear understanding of what they represent
Statistical Thinking
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Decision making under uncertainty
- Determining the sufficient amount of information needed for a decision
- Prioritizing objectives based on probability and potential return (benefit/cost ratio, decision trees)
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Cumulative effects of errors
- The butterfly effect
- Black swan events
- Business analogies for Schrödinger's cat and Newton's apple
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The Cassandra Dilemma: Measuring forecast accuracy when the course of action changes
- Case study: Google Flu Trends and its failures
- How decisions render forecasts obsolete
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Forecasting: Methods and practicality
- ARIMA models
- Why naive forecasts are often more responsive
- Determining the appropriate look-back period for forecasts
- Why having more data does not always lead to better forecasts
Statistical Methods Useful for Decision Makers
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Describing Bivariate Data
- Differentiating between univariate and bivariate data
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Probability
- Understanding why measurements vary each time
- Normal Distributions and normally distributed errors
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Estimation
- Independent information sources and degrees of freedom
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Logic of Hypothesis Testing
- The concept of falsification: What can be proven and why evidence often contradicts our desires
- Interpreting hypothesis testing results
- Testing means
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Statistical Power
- Determining an effective and cost-efficient sample size
- The trade-off between false positives and false negatives
Requirements
Strong mathematical skills are required. Additionally, participants should have prior exposure to basic statistics, such as working with teams that conduct statistical analyses.
7 Hours
Testimonials (3)
knowledge of the trainer, tailor based, all topics covered
eleni - EUAA
Course - Forecasting with R
The variation with exercise and showing.
Ida Sjoberg - Swedish National Debt Office
Course - Econometrics: Eviews and Risk Simulator
The real life applications using Statcan and CER as examples.
