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Course Outline
What Statistics Can Offer to Decision Makers
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Descriptive Statistics
- Basic statistics - identifying which statistical measures (e.g., median, mean, percentiles) are most relevant for different distributions
- Graphs - understanding the importance of accurate visualization (e.g., how the construction of a graph influences decision-making)
- Variable types - determining which variables are easier to manage
- Ceteris paribus - recognizing that variables are always in motion
- The third variable problem - strategies for identifying the true influencer
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Inferential Statistics
- Probability value - understanding the significance of the P-value
- Repeated experiments - interpreting results from repeated experimental trials
- Data collection - minimizing bias, though it cannot be entirely eliminated
- Understanding confidence levels
Statistical Thinking
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Decision-making with limited information
- How to assess whether the amount of information gathered is sufficient
- 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
- Understanding Schrödinger's cat and Newton's Apple in a business context
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The Cassandra Problem - measuring forecast accuracy when the course of action changes
- Google Flu Trends - an analysis of where it 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 vs. bivariate data
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Probability
- Why results vary each time we measure them
- 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 the outcome often contradicts our expectations (Falsification)
- Interpreting hypothesis testing results
- Testing Means
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Power
- How to determine an adequate yet cost-effective sample size
- False positives and false negatives, and why this is always a trade-off
Requirements
Strong mathematical skills are required. Additionally, prior exposure to basic statistics (such as collaborating with professionals who conduct 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.
