1 / 6

Applied Math Notes

What are outliers?. In a set of data, values that are widely separated from the rest of the data are called outliers.Outliers are not just the greatest or the least values, but values that are very different from the pattern established by the rest of the data.There are two ways to determine whether a set of data contains outliers

ostinmannual
Download Presentation

Applied Math Notes

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

    1. Applied Math Notes 4.3 Outliers Objective: Determine whether a set of data contains outliers.

    3. Testing for Outliers using Quartiles and IQR If a data value is more than 1.5 interquartile ranges below the lower quartile, then it is a lo-end outlier. Q1 – 1.5IQR is the lo-end cutoff value using quartiles If a data value is more than 1.5 interquartile ranges above the upper quartile, then it is a hi-end outlier. Q3 + 1.5IQR is the hi-end cutoff value using quartiles

    4. Example 1: Finding outliers using quartiles Use the following data set to determine whether there are any outliers using quartiles: 16, 25, 24, 19, 33, 25, 34, 46, 37, 33, 42, 40, 37, 34, 49, 73

    5. Testing for Outliers using the Mean and Standard Deviation If a data value is more than 3 standard deviations below the mean, then it is a lo-end outlier. µ - 3s is the lo-end cutoff value using mean and standard deviation. If a data value is more than 3 standard deviations above the mean, then it is a hi-end outlier. µ + 3s is the hi-end cutoff value using mean and standard deviation.

    6. Example 2: Finding outliers using mean and standard deviation. Use the mean and standard deviation to identify any outliers from the following data set: 61, 92, 83, 63, 93, 69, 72, 74, 75, 32, 80, 83, 86, 88, 75, 78, 88, 89, 97, 74

More Related