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Course Outline
What Statistics Can Offer to Decision Makers
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Descriptive Statistics
- Basic statistics - understanding which statistical measures (e.g., median, average, percentiles) are most relevant to different distributions
- Graphs - the significance of accuracy (e.g., how the construction of a graph influences decision-making)
- Variable types - identifying which variables are easier to manage
- Ceteris paribus - understanding that variables are rarely static
- The third variable problem - techniques for identifying the true influencing factor
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Inferential Statistics
- Probability value - understanding the meaning of the P-value
- Repeated experiments - interpreting results from repeated trials
- Data collection - strategies to minimize bias, acknowledging that it cannot be entirely eliminated
- Understanding confidence levels
Statistical Thinking
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Decision-making with limited information
- Determining sufficient information levels
- 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
- Applying concepts like Schrödinger's cat and Newton's Apple to business scenarios
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The Cassandra Problem - assessing forecasts when the course of action changes
- Google Flu Trends - analyzing what went wrong
- Understanding how decisions can render forecasts obsolete
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Forecasting - methods and practicality
- ARIMA
- Why naive forecasts are often more responsive
- Determining the optimal historical look-back period for forecasts
- Understanding 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
- 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 why falsification often contradicts our expectations
- Interpreting Hypothesis Testing 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-offs
Requirements
Participants must possess strong mathematical skills. Additionally, exposure to basic statistics—such as working with individuals who perform statistical analysis—is required.
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.
