# APME_2 - Applied Probability Methods for Engineers Slide...

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Click to edit Master subtitle style Applied Probability Methods for Engineers Slide Set 2

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Click to edit Master subtitle style Chapter 6 Descriptive Statistics
Data and Samples n When we want information about a phenomenon or characteristic, often cannot have complete information n We collect as much data as is practical from the entire set of possibilities, or population n Given a random variable X, we obtain a sample of observations of the phenomenon, x1, x2, …, xn n We analyze sample data and use this analysis to make inferences about the population n To ensure as accurate a representation of the population as possible, we need to ensure that we take a random sample

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Data Presentation n Bar chart Machine Breakdowns
Data Presentation n Pareto chart

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Histograms n Used to show frequency of occurrences (or distribution) of numerical data n Similar to a probability mass function (pmf) n A pmf is a histogram for a population (or entire sample space)
Histograms Positive skewness (right skewed) Negative skewness (left skewed)

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Histograms Bimodal distribution Outliers
Sample Statistics n Sample mean of a data set is arithmetic average n Sample median is the “middle” value (if n even, average value ° n/2R and ° n/2° n Sample mode is the most commonly occurring value n Sample variance 1 / n i i x x n = = ( 29 ( 29 2 2 2 2 2 1 1 2 1 1 1 1 1 n n n n i i i i i i i i x x n x x x nx s n n n = = = = - - - = = = - - -

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Sample Statistics
Boxplots n Used to represent quartiles of data and provides a picture of the distribution

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Coefficient of Variation n Measures relative variability n CV = s/x for sample data n Allows comparing different random variables on equal footing n For a distribution with known parameters, CV = σ/μ n Binomial CV = n Poisson CV = n Exponential CV = 1 n Gamma CV = ( 29 1 / p np - 1/ λ 1/ k
Click to edit Master subtitle style Chapter 7 Statistical Estimation and Sampling Distributions

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Parameters and Statistics n A parameter is a true, fixed value that, in statistical analysis is usually unknown n May be the true mean μ or true variance σ2 of a probability distribution n The true value of a parameter is estimated from sample data n A statistic is a property of sample data taken from a population n A point estimate of some unknown parameter is a statistic that provides a best guess at the parameter value
Parameters and Statistics

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Point Estimates n A point estimate is unbiased if n The bias of a biased estimate is n Suppose we don’t know the probability of success p in a sequence of Bernoulli trials n If there are n trials, we use the point estimate for p n The number of successes, X ~ B(n, p), so that E(X) = np, implying n Therefore is an unbiased estimator of p ˆ θ ( 29 ˆ E θ θ = ( 29 ˆ E θ θ - ˆ / p X n = ( 29 ( 29 ˆ / E p E X n p = = ˆ p
Point estimates n If X1, …, Xn are sample observations from a distribution with mean μ, consider the sample mean n Observe that n This shows that = X is an unbiased estimator of μ 1 / n i i X X n = = ( 29 ( 29 ( 29 ( 29 ( 29 1 1 1/ 1/ / n n i i i i E X n E X n E X n n μ μ = = = = = = ˆ μ

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Point Estimates
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