Course Outline
What statistics can offer to Decision Makers
- Descriptive Statistics
- Basic statistics - which of the statistics (e.g. median, average, percentiles etc...) are more relevant to different distributions
- Graphs - significance of getting it right (e.g. how the way the graph is created reflects the decision)
- Variable types - what variables are easier to deal with
- Ceteris paribus, things are always in motion
- Third variable problem - how to find the real influencer
- Inferential Statistics
- Probability value - what is the meaning of P-value
- Repeated experiment - how to interpret repeated experiment results
- Data collection - you can minimize bias, but not get rid of it
- Understanding confidence level
Statistical Thinking
- Decision making with limited information
- how to check how much information is enough
- prioritizing goals based on probability and potential return (benefit/cost ratio ration, decision trees)
- How errors add up
- Butterfly effect
- Black swans
- What is Schrödinger's cat and what is Newton's Apple in business
- Cassandra Problem - how to measure a forecast if the course of action has changed
- Google Flu trends - how it went wrong
- How decisions make forecast outdated
- Forecasting - methods and practicality
- ARIMA
- Why naive forecasts are usually more responsive
- How far a forecast should look into the past?
- Why more data can mean worse forecast?
Statistical Methods useful for Decision Makers
- Describing Bivariate Data
- Univariate data and bivariate data
- Probability
- why things differ 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 is always the opposite what we want (Falsification)
- Interpreting the results of Hypothesis Testing
- Testing Means
- Power
- How to determine a good (and cheap) sample size
- False positive and false negative and why it is always a trade-off
Requirements
Good maths skills are required. Exposure to basic statistics (i.e. working with people who do the statistical analysis) is required.
Testimonials (8)
Well thought out and high grade planning materials.
Andrew - Office of Projects Victoria - Department of Treasury & Finance
Course - Forecasting with R
The pace was just right and the relaxed atmosphere made candidates feel at ease to ask questions.
Rhian Hughes - Public Health Wales NHS Trust
Course - Introduction to Data Visualization with Tidyverse and R
We were using road accident data for practicals
Maphahamiso Ralienyane - Road Safety Department
Course - Statistical Analysis using SPSS
Wasn't boring, the trainer could keep the attention, the topics were covered in depth.
Marta - Ministerstwo Zdrowia
Course - Advanced R Programming
Very tailored to needs.
Yashan Wang
Course - Data Mining with R
The subject matter and the pace were perfect.
Tim - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course - Programming with Big Data in R
At the end of the class, we had a great overview of the language, we were provided tools to continue learning and were provided suggestions on how to continue learning. We covered AI/ML information.
Victor Prado - Global Knowledge Network Training Ltd
Course - R
That Haytham started with the basics and gave us enough time to do the examples and ensure that we were at the same page before we moved on to the next topic.