Statistical Thinking for Decision Makers培训

课程编码

statdm

课程时长

7 小时 通常来说是1天,包括中间休息。

要求

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

课程概览

本课程是为决策者创建的,其主要目标不是进行计算和分析,而是要了解它们,并能够选择哪种统计方法与组织的战略规划相关。

例如,潜在客户需要决定需要收集多少样本才能决定是否要启动产品。

如果您需要更长的课程,涵盖统计思维的基础知识,请查看为期5天的“经理人Statistics ”培训。

Machine Translated

课程大纲

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    • How decisions make forecast outdated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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