distributionssummarystatistics(英文版)-文庫吧資料
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【正文】 ly outliers with identified causes can be removed from the data set. – Examples: ? Data entry errors. ? Process excursions. Multimodal Ou t lier s Discussion Scenarios 1: Skewed left. 2: Symmetric. 3: Outliers or extremely bimodal. 1 2 3 Discussion Scenarios (Continued) Scenario 6 ? Below is a graph of particle count data. – Do there appear to be any outliers? – Yes. – Can these outliers be removed? – Only if cause(s) have been identified. Practice Items 1. What is an outlier? ? An outlier is an observation in the data set that has values that are different than the rest of the distribution. 2. What do we look for when looking at the shape of a distribution? ? Symmetric, skewed, how many modes, are outliers present. 3. What are some examples of skewed distributions? ? Anything with either 0 or 100% as the ideal target。 yield ideal target = 100%。 Salary, housing prices, high yielding processes. 4. Name the properties that describe a distribution. ? Center, shape, and spread. Measures of Center ? Median: – Half the values are higher and half are lower. – Middle value of the sample size n is odd, average of the two middle values if n is even. – Example 1: ? 5,9,6,2,4,6 ? Sort: 2,4,5,6,6,9 ? Median = (5+6)/2 = – Example 2: ? 5,9,6,2,4,22 ? Sort: 2,4,5,6,6,22 ? Median = (5+6)/2 = ? Mean: – Arithmetic average. – Sum of all values divided by the number of values. – Example 1: ? 5,9,6,2,4,6 ? X = (5+9+6+2+4+6)/6 = – Example 2: ? 5,9,6,2,4,22 ? X = (5+9+6+2+4+22)/6 = 8 nxxn1ii??? Measures of Center (Continued) ? For symmetric distributions, the mean and median will be equivalent. ? The mean is influenced by extreme values. – For skewed distributions,
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