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Course Outline
What Statistics Can Offer Decision-Makers
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Descriptive Statistics
- Basic statistics – which measures (e.g. median, mean, percentiles, etc.) are most appropriate for different data distributions
- Graphs – the importance of accurate representation (e.g. how the way a graph is constructed can influence decision-making)
- Variable types – which variables are easier to work with
- Ceteris paribus: acknowledging that, in reality, things are always in motion
- The third variable problem – identifying the true underlying influencer
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Inferential Statistics
- P-values – understanding what a P-value actually means
- Repeated experiments – how to interpret results from repeated trials
- Data collection – you can reduce bias, but never eliminate it entirely
- Understanding confidence levels
Statistical Thinking
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Decision-making with limited information
- How to assess whether you have enough information to make a sound decision
- Prioritising 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
- What Schrödinger's cat and Newton's apple represent in a business context
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The Cassandra Problem – how to evaluate a forecast when the course of action changes
- Google Flu Trends – what went wrong
- How decisions can 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 poorer forecasts?
Statistical Methods Useful for Decision-Makers
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Describing Bivariate Data
- Univariate versus 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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The Logic of Hypothesis Testing
- What can be proven – and why it is often the opposite of what we hope to prove (falsification)
- Interpreting the results of hypothesis tests
- Testing means
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Power
- How to determine an effective (and cost-efficient) sample size
- False positives and false negatives – why there is always a trade-off
Requirements
A solid grasp of mathematics is essential. Prior exposure to basic statistics (for example, through working alongside professionals who conduct statistical analysis) is also 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.
