distributionssummarystatistics(英文版)(參考版)
2025-01-08 07:56本頁(yè)面
【正文】 contaminants ideal target = 0ppm。Course Contents 1. Introduction 2. Data types and basic definitions 3. Distributions summary statistics 4. Sampling Single Variable Analysis 5. Control chart 6. Process capability study Lesson 3 ? Distributions Summary Statistics: – L301 ? Describe the shape of distributions. Define outliers and describe their effect on distributions. – L302 ? Define and pute summary statistics for continuous variables: Mean, median, standard deviation, variance, range and interquartile range (IQR). – L303 ? Define and pute summary statistics for discrete variables: Proportions. – L304 ? Describe the Normal and binomial distributions. Q u a l ity C h a r a c te r i s ti cXXX XX XXXXME A S U R E D VA L U ES D I S T R I B U T I O NMo s t Va lu e sa r e H e r eF e w V a l u e sa r e H e r eC e n t e rSp re a dDistribution ? Distribution = Pattern of variation in a set of data. Shape Distribution (Continued) ? A distribution is described by shape, center, and spread. – Shape is determined by: – Symmetry – Modality – Outliers – Common measures of center: – Mean – Median – Common measures of spread: – Range, Interquartile Range (IQR) – Standard Deviation, Variance Distribution Shape ? Symmetric: – Portions above and below the center are roughly mirror images – Example: ? Number of parts inspected/hour. ? Skewness is degree of symmetry: ? Skewed left has a long tail to the left. ? Skewed right has a long tail to the right. – Example: ? Life (in hours) of 60W light bulbs. Skewed right Symmetric Distribution Shape (Continued) ? Modality: – Mode = Most frequent value. – Modality = Number of peaks in a dataset. – Examples: ? Unmatched machines. ? Multiple suppliers. ? Outliers: – Extreme data points. – On
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