Statistical Thinking for Decision Makers Treningskurs

Last updated

Kurskode

statdm

Varighet

7 timer (vanligvis 1 dag inkuldert pauser)

Krav

Good maths skills are required. Exposure to basic statistics (i.e. working with people who do the statistical analysis) is required.

Oversikt

Dette kurset er laget for beslutningstakere hvis hovedmål ikke er å gjøre beregningen og analysen, men å forstå dem og kunne velge hva slags statistiske metoder som er relevante i strategisk planlegging av organisasjonen.

For eksempel må en prospektdeltaker ta stilling til hvor mange prøver som må samles inn før de kan ta beslutningen om produktet kommer til å bli lansert eller ikke.

Hvis du trenger lengre kurs som dekker det grunnleggende om statistisk tenking, kan du se på 5 dagers " Statistics for ledere" -opplæring.

Machine Translated

Kursplan

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    • How decisions make forecast outdated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

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