View source for Statistics for Decision Makers - 01.01 - Introduction

{{Cat|Statistics for Decision Makers|01.01}}
<slideshow style="nobleprog" headingmark="。" incmark="…" scaled="false" font="Trebuchet MS" footer="[http://www.NobleProg.co.uk www.NobleProg.co.uk]" subfooter="Training Courses Worldwide">
;title: 01.01 - Introduction
;author: Bernard Szlachta (NobleProg Ltd) bs@nobleprog.co.uk
</slideshow>

== Inference and Decision。 ==
[[File:ClipCapIt-140531-144340.PNG|450px]]
* Inference is about finding facts
* Decisions are about taking courses of action, which can be based on inference findings
 Is inference not followed by a decision idle?

== Decisions and Rules。 ==
:[[File:search.jpg]]

* A Decision is a choice between alternatives
* Rules are the conditions which guide or determine decisions

== BPMN, Decisions and Statistics。 ==
[[File:Decisions, Rules and Inference.png]]

== Statistics, Decisions and Rules。 ==
* The Decision Maker encounters a decision point in a business process
* Making those decisions is mostly a question of judgement
* Finding evidence using the scientific method 
* The ultimate goal is to fully automate decision making by creating rules which use statistical methods

== Hiring Example。 ==
; Business Process
: Recruitment
; Decision
: Invite a candidate for interview or not?
; Variables
: Candidate has relevant experience
: Salary expectation
: Availability
; Inference
: What is the probability that the candidate will be hired and make a good employee?
; Rules
<source lang="java">
 Rule "Should the candidate be interviewed?"
 When 
     P-value < 0.01 
 Then 
     Interview Candidate
 End
</source>

== Traditional Rules Example。 ==
[[File:ClipCapIt-140531-221624.PNG]]

<source lang="java">
 Rule "Should the candidate be interviewed?"
 When 
     ExpectedSalary < 100000 and
     AvailableDate < 2015-01-01
     ExperienceScore >= 1000
 Then 
     Interview Candidate
 End
</source>

== When to use Statistical Inference to make decisions。 ==
* Deterministic solutions are usually cheaper and easier to understand and maintain

Use Statistical Models if:
* the loss related to errors caused by deterministic solutions justifies the cost of maintaining a statistical module
* there are no easy deterministic solutions
* the model needs to learn (e.g. neural networks)
* the rules are in the heads of managers who change often and need to relearn the rules each time from experience

== Technical Statistics。 ==
* Database Indexes (e.g. Oracle)
* Search Engine
* Network Routers
* .....

== Overview of the methods。 ==
{| class="wikitable" style="background-color:white;"
|-
! Describe !! Sample !! Measure !! Predict
|-
| 
* mean
* standard deviation
* distribution
| 
* simple random sampling
* stratified random sample
* determining sample size (from power)
| 
* validity
* reliability 
| 
* hypothesis testing
* regression/forecasting

|}

== Questions to the audience。… ==
* Why things differ each time we measure them?
* [[wikipedia:Brownian motion|Brownian Motion]]
* [[wikipedia:Uncertainty principle|Heisenberg Uncertainty Principle ]]

== Quiz。==
[[Statistics_for_Decision_Makers_-_01.01_-_Introduction#Quiz|Please find the Quiz here]]

== Quiz ==

<quiz display=simple >

{A sentence "If the P-value is greater than 0.5 we consider the test inconclusive" is an example of

|type="()"}

- A decision
+ A rule

{A company is surveying people about their propensity to recommend a product to a friend.
It turned out that only a couple of people replied to the survey, but the difference in propensity increased 70%. What can the managers do?

|type="()"}

- They must find more respondents and repeat the survey, otherwise the test is insignificant
- They can simply assume that the propensity increased 70%
+ They should calculate what the chances are you would get such an increase by pure chance if there were no difference in reality (i.e. use a statistical method)

</quiz>