Week 2 Graphical-Numerical Statistics and Probability concepts

Week 2 Graphical-Numerical Statistics and Probability concepts

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Engr 9397 Week 2 Exploratory Data Analysis using numerical and graphical methods Basic Probability concepts
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Why take measurements? “You manage what you measure” “What gets measured gets done” Measures are indicators of performance and play a critical role in controlling quality Ensures alignment between objectives, strategies and actions and enables a team to: justifies change to a process quantify performance (results) and improvements that are realized from process changes Determines optimal use of resources/assets Measures encompass values of both customer and organization
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Statistics – some quotes “Statistics are like bikinis: what they reveal is suggestive but what they conceal is vital” “Numbers are like people, torture them enough and they will tell you anything” “There are three kinds of lies: lies, damned lies and statistics” “Statistics will prove anything, event the truth” “Statistics is the art of lying by means of figures” “Statistics is never having to say you are certain” “One should use statistics as a drunken man uses lampposts–for support rather than for illumination”
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Why study statistics? What are statistics? It helps us to make informed decisions based on data collected in the face of uncertainty and variation Statistics describes a set of tools and techniques used to describe, organize, model and interpret data
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Why are Statistics useful in Quality Engineering? Collection, tabulation, analysis, interpretation and presentation of numerical quality measurements Aids in making decisions about actions and changes to a product, process or service Statistics enables one to make decisions about a population using sample data Better decision better design improved performance lower cost better use of resources bigger profits better world!
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Example–Arctic Pipeline Engineering The decision to build an arctic subsea pipeline is a very complex (and expensive) one and involves the use of empirical data and probability to determine the risk justifies the cost of the project. How many factors can we think of?
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Some Basic Terminology Population: collection of all possible elements, object of interest Sample: population subset representative sampling allows for predictions of entire population and a degree of confidence to be assigned Random: unpredictable result, but a known probability of occurrence Bias : not random, sample does not adequately represent the population Inference: process of drawing a conclusion using rules
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Types of Variables Variable / Continuous data: uncountable quality characteristics that can be measured the real number scale (ex. length, speed) Discrete / Categorical data: countable quality characteristics that are measured using whole numbers and are either present or absent (ex. # parts (attribute data ), # of failures) Mixed Data A mix of discrete and continuous data, often occurs in engineering applications as a result of a zero lower bound value (quantity of water, min/max measures)
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This note was uploaded on 03/29/2011 for the course ENGR 9397 taught by Professor Susanhunt during the Winter '11 term at Memorial University.

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Week 2 Graphical-Numerical Statistics and Probability concepts

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