Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
What Statistics Can Offer to Decision Makers
-
Descriptive Statistics
- Basic statistics - determining which measures (e.g., median, mean, percentiles) are most relevant for different distributions
- Graphs - understanding the significance of accuracy (e.g., how graph construction influences decision-making)
- Variable types - identifying which variables are easier to manage
- Ceteris paribus - recognizing that things are always in motion
- Third variable problem - strategies for identifying the true influencing factor
-
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
-
Decision making with limited information
- How to assess whether sufficient information is available
- Prioritizing goals based on probability and potential return (benefit/cost ratio, decision trees)
-
How errors accumulate
- The butterfly effect
- Black swan events
- Understanding Schrödinger's cat and Newton's Apple in a business context
-
Cassandra Problem - measuring forecast accuracy when the course of action changes
- Google Flu Trends - analyzing what went wrong
- How decisions can render forecasts obsolete
-
Forecasting - methods and practicality
- ARIMA models
- Why naive forecasts are often more responsive
- Determining the optimal historical look-back period for forecasts
- Why having more data can sometimes lead to worse forecasts
Statistical Methods Useful for Decision Makers
-
Describing Bivariate Data
- Univariate data versus bivariate data
-
Probability
- Why measurements vary each time they are taken
- Normal Distributions and normally distributed errors
-
Estimation
- Independent sources of information and degrees of freedom
-
Logic of Hypothesis Testing
- What can be proven, and why the outcome is often the opposite of what we wish (Falsification)
- Interpreting the results of Hypothesis Testing
- Testing Means
-
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
Strong mathematical skills are required. Additionally, prior exposure to basic statistics (such as working 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.
