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Course Outline
The Value of Statistics for Decision Makers
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Descriptive Statistics
- Core statistics - identifying which metrics (e.g., median, mean, percentiles) are most relevant for different data distributions
- Graphs - understanding the significance of accurate visualization and how graph construction influences decision-making
- Variable types - determining which variables are easier to manage
- Ceteris paribus - recognizing that conditions are always changing
- The third variable problem - strategies for identifying the true influencing factor
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Inferential Statistics
- Probability value - interpreting the meaning of the P-value
- Repeated experiments - how to interpret results from repeated trials
- Data collection - strategies to minimize, but not eliminate, bias
- Understanding confidence levels
Statistical Thinking
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Decision making with limited information
- Assessing the sufficiency of available information
- 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
- Understanding Schrödinger's cat and its business parallel, Newton's Apple
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The Cassandra Problem - measuring forecast accuracy when actions change the outcome
- Google Flu Trends - analyzing the failure case
- How decisions render forecasts obsolete
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Forecasting - methods and practical application
- 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 versus bivariate data
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Probability
- Understanding why measurements vary 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 the concept of falsification (often proving the opposite of what we desire)
- Interpreting hypothesis test results
- Testing means
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Power
- Determining an effective and cost-efficient sample size
- False positives and false negatives - understanding the inherent trade-off
Requirements
Participants must possess strong mathematical skills and have prior exposure to basic statistics, such as collaborating with teams that conduct statistical analysis.
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.
