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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) are most appropriate for different distributions
- Graphs - understanding the significance of accurate visualization (e.g., how graph design influences decision-making)
- Variable types - identifying which variables are easier to manage
- Ceteris paribus, things are always in motion
- Third variable problem - strategies for identifying the true influencer
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Inferential Statistics
- Probability value - understanding the meaning of P-value
- Repeated experiments - interpreting results from repeated experiments
- Data collection - minimizing bias, though never entirely eliminating it
- Understanding confidence levels
Statistical Thinking
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Decision making with limited information
- Determining how much information is sufficient
- Prioritizing goals based on probability and potential return (benefit/cost ratio, decision trees)
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How errors accumulate
- Butterfly effect
- Black swans
- What is Schrödinger's cat and what is Newton's Apple in business
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Cassandra Problem - measuring a forecast when the course of action has changed
- Google Flu trends - analyzing what went wrong
- How decisions make forecasts outdated
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Forecasting - methods and practicality
- ARIMA
- Why naive forecasts are usually 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 data 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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Logic of Hypothesis Testing
- What can be proven, and why it is often the opposite of what we want (Falsification)
- Interpreting the results of Hypothesis Testing
- Testing Means
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Power
- Determining a good (and cost-effective) sample size
- False positive and false negative, and why it is always a trade-off
Requirements
Strong mathematical skills are required. Additionally, prior exposure to basic statistics, such as 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.
