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Course Outline
What Statistics Can Offer to Decision Makers
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Descriptive Statistics
- Basic statistics - identifying which metrics (e.g., median, mean, percentiles, etc.) are most relevant for different data distributions
- Graphs - understanding the importance of accuracy (e.g., how the construction of a graph influences decision-making)
- Variable types - determining which variables are easier to manage
- Ceteris paribus - recognizing that conditions are constantly changing
- The third variable problem - strategies for identifying the true influencing factor
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Inferential Statistics
- Probability value - understanding the significance of the P-value
- Repeated experiments - interpreting results from replicated trials
- Data collection - minimizing bias rather than eliminating it entirely
- Understanding confidence levels
Statistical Thinking
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Decision making with limited information
- Determining when sufficient information has been gathered
- Prioritizing objectives based on probability and potential return (benefit/cost ratio, decision trees)
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How errors accumulate
- The butterfly effect
- Black swan events
- Applying concepts like Schrödinger's cat and Newton's Apple to business scenarios
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The Cassandra Problem - measuring forecasts when the course of action shifts
- The Google Flu Trends case study - analyzing its failure
- 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 data vs. bivariate data
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Probability
- 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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The Logic of Hypothesis Testing
- What can be proven, and why hypotheses are often disproven (Falsification)
- Interpreting the results of Hypothesis Testing
- Testing Means
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Power
- Determining an effective and cost-efficient sample size
- False positives and false negatives - understanding the inherent trade-offs
Requirements
Participants must possess strong mathematical skills and prior exposure to basic statistics (e.g., experience collaborating with professionals who 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.
