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Course Outline
The Value of Statistics for Decision Makers
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Descriptive Statistics
- Basic statistics: identifying which statistical measures (e.g., median, average, percentiles) are most relevant for different distributions
- Graphs: understanding the significance of accuracy, such as how visualization methods influence decision-making
- Variable types: determining which variables are easier to manage
- Ceteris paribus: recognizing that conditions are always in motion
- The third variable problem: strategies for identifying the true influencing factors
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Inferential Statistics
- P-value: understanding its meaning
- Repeated experiments: interpreting results from multiple trials
- Data collection: acknowledging that while bias can be minimized, it cannot be entirely eliminated
- 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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The accumulation of errors
- The butterfly effect
- Black swan events
- Analogies: Schrödinger's cat and Newton's Apple in a business context
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The Cassandra Problem: measuring forecasts when the course of action changes
- Case study: Google Flu Trends and its failures
- How decisions render forecasts obsolete
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Forecasting: methods and practical applications
- ARIMA
- Why naive forecasts are often more responsive
- Determining the appropriate historical data range for forecasting
- Why increased data volume can sometimes lead to poorer forecasts
Statistical Methods Useful for Decision Makers
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Describing Bivariate Data
- Distinction between univariate 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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The Logic of Hypothesis Testing
- What can be proven and the concept of falsification (why it often contradicts our desired outcome)
- 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
Strong mathematical skills are essential. Additionally, prior exposure to basic statistics, such as working with teams who conduct statistical analyses, 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.
