ENMA 420-520 Mid-Term Review Slides

ENMA 420-520 Mid-Term Review Slides - Statistical Concepts...

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Click to edit Master subtitle style 10/17/09 Statistical Concepts for Engineering Management ENMA 420 / 520 Mid-Term Review 11
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10/17/09 Summary (1 of 6) Qualitative & Quantitative Data Descriptive Statistics Measures of central tendency Measures of variation Measures of rank 22
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10/17/09 Summary (2 of 6) Probability Compound & Complementary Probabilities Conditional Probabilities Rules: Additive, Multiplicative, Baye’s Partitions, Permutations and Combinations Random Numbers & Pseudo-Random 33
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10/17/09 Summary (3 of 6) Distributions for Discrete Random Variables Bernoulli Binomial Multinomial Negative Binomial Geometric Hypergeometric 44
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10/17/09 Summary (4 of 6) Continuous Random Variables Density Functions Uniform Normal Gamma-Type Chi-Square Exponential Weibull 55
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10/17/09 Summary (5 of 6) Joint Probability Distributions Marginal and Conditional Probability Distributions Expected Values Covariance and Coefficient of Correlation Sampling Distributions: Chi-square Distribution 66
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10/17/09 Summary (6 of 6) Point Estimators Finding Interval Estimators: The Pivotal Method Estimate of Population Mean Estimation of the Difference Between Two Population Means: Independent Samples Matched Pairs 77
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10/17/09 Definitions Statistics is the science of data. This involves collecting, classifying, summarizing, organizing, analyzing and interpreting data. Descriptive: organizing, describing, summarizing Inferential: using sample data to make an inference about a large set of data Infer: 88
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10/17/09 Definitions (Cont’d) A statistical population is a data set (usually large, sometimes conceptual) that is our target of interest. A sample is a subset of data selected from the target population The object on which measurements are collected is called the experimental unit 99
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10/17/09 Definitions (Cont’d) A measure of reliability is a statement (usually quantified) about the degree of uncertainty associated with a statistical inference. Quantitative data represents the quantity or amount of something. Qualitative data are those that have no quantitative representation, i.e. 1010
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10/17/09 Describing Qualitative Data Category (or class) frequency: the number of observations that fall in that category (or class) Example: 7 out of 100 observations Category (or class) relative frequency: the proportion of the total number of observations that fall in that category. 1111
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10/17/09 Describing Qualitative Data Graphically: Bar Charts Coal Mine 7 Dam Failure 4 Gas Explosion 28 Lightning 1 Nuclear 1 Oil Fire 4 1212 0 2 4 6 8 10 12
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10/17/09 Describing Qualitative Data Graphically: Pie Charts TASK PERCENT Arc Welding 20 Spot Welding 32 Assembly 7 Material Handling 34 Material Removal 3 Dispensing / Coating 4 1313 Pie Chart of ROBOTS
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10/17/09 Describing Qualitative Data Graphically: Pareto 1414 Gas Explosion 28 28 62% Coal Mine 7 35 78% Oil Fire 4 39 87% Dam Failure 4 43 96% Nuclear 1 44 98% Lightning 1 45 100% 0 5 10 15 20 25 30 35 40 45 50 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
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10/17/09 Describing Quantitative Data Graphically: Dot Plots 1515
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This note was uploaded on 10/17/2009 for the course MET 387 taught by Professor Dean during the Spring '09 term at Old Dominion.

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ENMA 420-520 Mid-Term Review Slides - Statistical Concepts...

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