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Course Outline
What Statistics Can Offer Decision Makers
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Descriptive Statistics
- Basic statistics - determining which metrics (e.g., median, average, percentiles, etc.) are most relevant to different data distributions.
- Graphs - understanding the significance of accuracy (e.g., how the presentation of a graph influences decision-making).
- Variable types - identifying which variables are easier to manage.
- Ceteris paribus - recognizing that conditions are always in motion.
- The third variable problem - techniques for identifying the true influencer.
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Inferential Statistics
- Probability value - understanding the meaning 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 sufficient information is available.
- 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 Newton's Apple in a business context.
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The Cassandra Problem - how to measure a forecast when the course of action changes
- Google Flu trends - analyzing where things went wrong.
- How decisions render forecasts outdated.
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Forecasting - methods and practical application
- ARIMA models.
- Why naive forecasts are often more responsive.
- How far back should a forecast look into the past?
- 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
- Why measurements vary each time we take them?
- 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 the outcome is often contrary to what we desire (Falsification).
- Interpreting the results of Hypothesis Testing.
- Testing Means.
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Power
- How to determine an effective and cost-efficient sample size.
- False positives and false negatives, and why there is always a trade-off.
Requirements
Proficient math skills are required. Additionally, prior exposure to basic statistics (such as working with individuals 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.
