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Course Outline
How Statistics Benefits Decision Makers
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Descriptive Statistics
- Basic statistics - understanding which statistical measures (e.g., median, average, percentiles) are most appropriate for various distributions
- Graphs - the importance of accuracy (e.g., how the creation of a graph influences decision-making)
- Variable types - identifying which variables are easier to manage
- Ceteris paribus - recognizing that conditions are always in motion
- The third variable problem - strategies for identifying the true influencing factor
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Inferential Statistics
- Probability value - understanding the significance of the P-value
- Repeated experiments - interpreting results from multiple trials
- Data collection - minimizing bias is possible, but eliminating it entirely is not
- Understanding confidence levels
Statistical Thinking
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Decision-making with limited information
- Determining the sufficient amount of information needed
- Prioritizing goals based on probability and potential return (benefit-to-cost ratio, decision trees)
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How errors accumulate
- The Butterfly effect
- Black swan events
- Concepts analogous to Schrödinger's cat and Newton's Apple in a business context
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The Cassandra Problem - measuring forecasts when the course of action has changed
- Google Flu Trends - analysis of its inaccuracies
- How decisions can render forecasts obsolete
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Forecasting - methods and practical application
- ARIMA
- Why naive forecasts are often more responsive
- Determining the appropriate historical depth for forecasts
- Why having more data can sometimes lead to worse forecasts
Statistical Methods Useful for Decision Makers
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Describing Bivariate Data
- Univariate data versus bivariate data
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Probability
- Reasons for variation in measurements
- 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
- What can be proven, and why falsification is often counter-intuitive
- Interpreting Hypothesis Testing results
- Testing Means
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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, prior exposure to basic statistics (such as working with colleagues who conduct statistical analyses) is 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.
