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Course Outline
What Statistics Can Offer to Decision Makers
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Descriptive Statistics
- Basic statistics - determining which statistics (e.g., median, mean, percentiles) are most relevant for different distributions
- Graphs - understanding the significance of accuracy (e.g., how graph creation impacts decision-making)
- Variable types - identifying which variables are easier to manage
- Ceteris paribus - acknowledging that conditions are always in motion
- Third variable problem - identifying the true influencer
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Inferential Statistics
- Probability value - understanding the meaning of the P-value
- Repeated experiments - interpreting results from repeated experimental runs
- Data collection - recognizing that bias can be minimized but not entirely eliminated
- Understanding confidence levels
Statistical Thinking
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Decision making with limited information
- Determining sufficient information levels
- Prioritizing goals based on probability and potential return (benefit/cost ratio, decision trees)
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How errors accumulate
- Butterfly effect
- Black swans
- Understanding the business analogies of Schrödinger's cat and Newton's Apple
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Cassandra Problem - measuring forecasts when the course of action has shifted
- Google Flu Trends - analyzing its failures
- How decisions render forecasts obsolete
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Forecasting - methods and practicality
- ARIMA
- Why naive forecasts are often more responsive
- Determining the appropriate historical range for forecasting
- Why increased data can sometimes lead to worse forecasts
Statistical Methods Useful for Decision Makers
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Describing Bivariate Data
- Univariate vs. 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 sources of information and degrees of freedom
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Logic of Hypothesis Testing
- Understanding falsification - what can be proven and why we often prove the opposite of what we desire
- Interpreting hypothesis testing results
- Testing Means
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Power
- Determining an effective and cost-efficient sample size
- False positives and false negatives - understanding the inherent trade-offs
Requirements
Strong mathematical skills are required. Prior exposure to basic statistics (e.g., working with individuals who perform statistical analysis) is also necessary.
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.
