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Course Outline
What Statistics Can Offer to Decision Makers
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Descriptive Statistics
- Basic statistics - determining which statistical measures (e.g., median, average, percentiles, etc.) are more relevant for different distributions
- Graphs - the significance of accuracy (e.g., how the method of creating a graph influences the decision)
- Variable types - identifying which variables are easier to manage
- Ceteris paribus - acknowledging that conditions are always in motion
- The third variable problem - how to identify the true influencer
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Inferential Statistics
- Probability value - understanding the meaning of the P-value
- Repeated experiments - how to interpret results from repeated experiments
- Data collection - understanding that while bias can be minimized, it cannot be entirely eliminated
- Understanding confidence levels
Statistical Thinking
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Decision-making with limited information
- How to determine the sufficient amount of information
- Prioritizing goals based on probability and potential return (benefit/cost ratio, decision trees)
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How errors accumulate
- The butterfly effect
- Black swans
- Understanding Schrödinger's cat and its equivalent, Newton's Apple, in a business context
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The Cassandra Problem - how to measure a forecast when the course of action has changed
- Google Flu Trends - analyzing what went wrong
- How decisions render forecasts obsolete
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Forecasting - methods and practicality
- ARIMA
- Why naive forecasts are often more responsive
- How far back should a forecast look into the past?
- 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
- Why measurements vary each time they are taken
- 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 it is often the opposite of what we wish to prove (Falsification)
- Interpreting the results of Hypothesis Testing
- Testing Means
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Power
- How to determine an appropriate (and cost-effective) sample size
- False positives and false negatives, and why there is always a trade-off
Requirements
Strong mathematical skills are required. Additionally, exposure to basic statistics (i.e., working with individuals who perform statistical analysis) 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.
