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Course Outline
What Statistics Can Offer to Decision Makers
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Descriptive Statistics
- Basic statistics - Which statistical measures (e.g., median, average, percentiles) are most relevant for different distributions
- Graphs - The importance of accuracy (e.g., how graph construction influences decision-making)
- Variable types - Which variables are easier to manage
- Ceteris paribus - Understanding that variables are always in motion
- The third variable problem - How to identify the true influencer
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Inferential Statistics
- P-value - Understanding the meaning of the P-value
- Repeated experiments - How to interpret results from repeated trials
- Data collection - Bias can be minimized but not eliminated
- Understanding confidence levels
Statistical Thinking
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Decision-making with limited information
- How to determine sufficient information levels
- Prioritizing goals based on probability and potential return (benefit/cost ratio, decision trees)
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How errors accumulate
- The butterfly effect
- Black swan events
- What Schrödinger's cat and Newton's apple represent in business
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The Cassandra Problem - How to measure a forecast when the course of action has changed
- Google Flu Trends - An analysis of 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?
- Why more data can sometimes lead to worse forecasts
Statistical Methods Useful for Decision Makers
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Describing Bivariate Data
- Univariate and bivariate data
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Probability
- Why measurements differ 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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The Logic of Hypothesis Testing
- What can be proven and why it often contradicts our desires (Falsification)
- Interpreting hypothesis testing results
- Testing Means
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Power
- How to determine an effective (and cost-efficient) sample size
- False positives and false negatives - Understanding the inherent trade-offs
Requirements
Strong mathematical skills are required. Additionally, prior exposure to basic statistics (e.g., 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.
