課程大綱： Statistical Thinking for Decision Makers培訓
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 Schr?dinger'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How decisions make forecast outdated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
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