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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- How decisions make forecast outdated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.
7 Hours
Testimonials (5)
The variation with exercise and showing.
Ida Sjoberg - Swedish National Debt Office
Course - Econometrics: Eviews and Risk Simulator
the trainer had patience, and was eager to make sure we all understood the topics, the classes were fun to attend
Mamonyane Taoana - Road Safety Department
Course - Statistical Analysis using SPSS
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
Michael the trainer is very knowledgeable and skillful about the subject of Big Data and R. He is very flexible and quickly customize the training meeting clients' need. He is also very capable to solve technical and subject matter problems on the go. Fantastic and professional training!.
Xiaoyuan Geng - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course - Programming with Big Data in R
I enjoyed the Excel sheets provided having the exercises with examples. This meant that if Tamil was held up helping other people, I could crack on with the next parts.
