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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, mean, percentiles, etc.) are most relevant for different data distributions
- Graphs - understanding the significance of accurate visualization (e.g., how graph construction influences decision-making)
- Variable types - identifying which variables are easier to manage
- Ceteris paribus - acknowledging that conditions are always changing
- Third variable problem - strategies for identifying the true influencing factors
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Inferential Statistics
- Probability value - understanding the meaning of the P-value
- Repeated experiments - how to interpret results from repeated trials
- Data collection - recognizing that bias can be minimized but never fully eliminated
- Understanding confidence levels
Statistical Thinking
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Decision making with limited information
- How to determine the sufficient amount of information needed
- 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 Schrödinger's cat and Newton's Apple in a business context
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Cassandra Problem - how to measure a forecast when the course of action changes
- Google Flu Trends - analysis of what went wrong
- How decisions can 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 having more data can sometimes lead to worse forecasts?
Statistical Methods Useful for Decision Makers
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Describing Bivariate Data
- Univariate data vs. 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 we often end up disproving what we hoped to prove (Falsification)
- Interpreting the results of Hypothesis Testing
- Testing Means
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Power
- How to determine an effective and cost-efficient sample size
- False positives and false negatives, and why balancing them is always a trade-off
Requirements
Strong mathematical skills are required. Prior exposure to basic statistics (such as collaborating with professionals who conduct 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.
