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 - identifying which statistical measures (e.g., median, mean, percentiles, etc.) are most relevant for different data distributions.
- Graphs - the significance of accurate visualization (e.g., how the construction of a graph influences decision-making).
- Variable types - determining which variables are easier to manage.
- Ceteris paribus - understanding that things are always in motion.
- The third variable problem - identifying the true influencer.
-
Inferential Statistics
- Probability value - understanding the meaning of the P-value.
- Repeated experiments - interpreting results from repeated experimental trials.
- Data collection - recognizing that while bias can be minimized, it cannot be entirely eliminated.
- Understanding confidence levels.
Statistical Thinking
-
Decision-making with limited information
- How to assess whether there is sufficient information.
- 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 its analogy to Newton's Apple in a business context.
-
The Cassandra Problem - how to measure a forecast when the course of action has changed
- Google Flu Trends - analyzing where it went wrong.
- How decisions can render forecasts obsolete.
-
Forecasting - methods and practicality
- ARIMA.
- Why naive forecasts are often more responsive.
- How far back should a forecast look?
- Why more data can sometimes lead to worse forecasts.
Statistical Methods Useful for Decision Makers
-
Describing Bivariate Data
- Univariate data versus bivariate data.
-
Probability
- Why results vary each time we measure them.
- 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 it often contradicts our desired outcome (Falsification).
- Interpreting the results of Hypothesis Testing.
- Testing Means.
-
Power
- How to determine a cost-effective sample size.
- False positives and false negatives, and why this is always a trade-off.
Requirements
Proficiency in mathematics is 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.
