Statistical outliers | Robust statistics | Statistical charts and diagrams
In statistics, an outlier is a data point that differs significantly from other observations. An outlier may be due to variability in the measurement or it may indicate experimental error; the latter are sometimes excluded from the data set. An outlier can cause serious problems in statistical analyses. Outliers can occur by chance in any distribution, but they often indicate either measurement error or that the population has a heavy-tailed distribution. In the former case one wishes to discard them or use statistics that are robust to outliers, while in the latter case they indicate that the distribution has high skewness and that one should be very cautious in using tools or intuitions that assume a normal distribution. A frequent cause of outliers is a mixture of two distributions, which may be two distinct sub-populations, or may indicate 'correct trial' versus 'measurement error'; this is modeled by a mixture model. In most larger samplings of data, some data points will be further away from the sample mean than what is deemed reasonable. This can be due to incidental systematic error or flaws in the theory that generated an assumed family of probability distributions, or it may be that some observations are far from the center of the data. Outlier points can therefore indicate faulty data, erroneous procedures, or areas where a certain theory might not be valid. However, in large samples, a small number of outliers is to be expected (and not due to any anomalous condition). Outliers, being the most extreme observations, may include the sample maximum or sample minimum, or both, depending on whether they are extremely high or low. However, the sample maximum and minimum are not always outliers because they may not be unusually far from other observations. Naive interpretation of statistics derived from data sets that include outliers may be misleading. For example, if one is calculating the average temperature of 10 objects in a room, and nine of them are between 20 and 25 degrees Celsius, but an oven is at 175 °C, the median of the data will be between 20 and 25 °C but the mean temperature will be between 35.5 and 40 °C. In this case, the median better reflects the temperature of a randomly sampled object (but not the temperature in the room) than the mean; naively interpreting the mean as "a typical sample", equivalent to the median, is incorrect. As illustrated in this case, outliers may indicate data points that belong to a different population than the rest of the sample set. Estimators capable of coping with outliers are said to be robust: the median is a robust statistic of central tendency, while the mean is not. However, the mean is generally a more precise estimator. (Wikipedia).
Definition of an Outlier in Statistics MyMathlab Homework Problem
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From playlist Statistics
Statistics - How to find outliers
This video covers how to find outliers in your data. Remember that an outlier is an extremely high, or extremely low value. We determine extreme by being 1.5 times the interquartile range above Q3 or below Q1. For more videos visit http://www.mysecretmathtutor.com
From playlist Statistics
Determine Outliers by Hand (Even)
This video explains how to determine outliers of a data set by hand with an even number of data values. http://mathispower4u.com
From playlist Statistics: Describing Data
Determine Outliers by Hand (Odd)
This video explains how to determine outliers of a data set by hand with an odd number of data values. http://mathispower4u.com
From playlist Statistics: Describing Data
Assumptions: Calling Out OUTLIERS – Problems and Causes (6-8)
An Outlier is a rare or extreme high or low score that does not fit the overall pattern of the distribution. Single Items Outliers tend to occur on biometrics and demographics. Univariate Outliers are extreme high or low scores on a single scale. Multivariate Outliers are extreme high or l
From playlist Depicting Distributions from Boxplots to z-Scores (WK 6 QBA 237)
Outliers : Data Science Basics
How do we deal with outliers in data science? My Patreon : https://www.patreon.com/user?u=49277905
From playlist Data Science Basics
Characterising a Dataset (1 of 3: Discussing outliers)
More resources available at www.misterwootube.com
From playlist Descriptive Statistics & Bivariate Data Analysis
Finding Outliers using Interquartile Range | Statistics, IQR, Quartiles
How do we find outliers of a data set using the interquartile range? This is done using a simple rule, any value less than Q1-1.5*IQR is an outlier, and any value greater than Q3+1.5*IQR is an outlier. We'll go through the step by step process of finding outliers using IQR in today's video
From playlist Statistics
How to Handle Outliers in your Dataset in Business Statistics (Week 6B)
Outliers can cause big problems in your data. We learn what causes outliers, how to identify them, the problems they cause, and options for dealign with them. Some outliers should stay in the data. Others should be corrected, winsorized, or sometimes discarded. We explore the difference b
From playlist Basic Business Statistics (QBA 237 - Missouri State University)
How to FIX OUTLIERS in a Distribution (6-9)
The nature of the outlier determines how you should correct it. Some outliers can stay in the dataset. Data entry errors can be corrected. Other outliers can be Winsorized by replacing all outlier values with the highest reasonable value. In certain situations, you may choose to use an alt
From playlist Depicting Distributions from Boxplots to z-Scores (WK 6 QBA 237)
Introduction to Outlier Detection Methods - Wolfram Livecoding Session
Andreas Lauschke, a senior mathematical programmer, live-demos key Wolfram Language features useful in data science. In the sixth session, Andreas introduces some methods for outlier detection. This is part 1 of 2. A close look will be taken at box plots as well as caveats (i.e. when not t
From playlist Data Science with Andreas Lauschke
Year 12/AS Statistics Chapter 3.1 (Representations of Data)
This lesson introduces representing data for A-Level! We take a look at what is meant by anomalies and outliers, and introduce some techniques to define an outlier with some worked examples. Later on we return to boxplots from GCSE and show how to add outliers to box plots, as well as how
From playlist Year 12/AS Edexcel (8MA0) Mathematics: FULL COURSE
Boxplots & Outliers in SPSS – Identify and Deal with Outliers (4-8)
The boxplot serves up a great deal of information about both the center and spread of the data, allowing us to identify skewness and outliers, in a form that is both easy to interpret and easy to compare to other distributions. It is the graphical equivalent to the five-number summary. All
From playlist WK4 Statistical Graphing - Online Statistics for the Flipped Classroom
Judging outliers in a dataset | Summarizing quantitative data | AP Statistics | Khan Academy
Using the inter-quartile range (IQR) to judge outliers in a dataset. View more lessons or practice this subject at http://www.khanacademy.org/math/ap-statistics/summarizing-quantitative-data-ap/stats-box-whisker-plots/v/judging-outliers-in-a-dataset?utm_source=youtube&utm_medium=desc&utm_
From playlist Summarizing quantitative data | AP Statistics | Khan Academy
Recorded: Fall 2015 Lecturer: Dr. Erin M. Buchanan This video covers how to check your data for univariate and multivariate outliers (using Mahalanobis distance), as well as how to deal with those outliers by removing or testing with and without outliers. Lecture materials and assignmen
From playlist Learn R + Statistics
VOS: Learning What You Don't Know by Virtual Outlier Synthesis (Paper Explained)
#vos #outliers #deeplearning Sponsor: Assembly AI Check them out here: https://www.assemblyai.com/?utm_source=youtube&utm_medium=social&utm_campaign=yannic1 Outliers are data points that are highly unlikely to be seen in the training distribution, and therefore deep neural networks have t
From playlist Papers Explained
Estremi, outlier e altre rarità.
Cerchiamo di capire la differenza semplice ma fondamentai tra estremi, eventi rari e outlier. Perché alcuni si rimuovono o correggono, ma gli altri vanno studiati e modellizzati. Evitiamo errori sciocchi, troppo comuni anche tra gente che dovrebbe esserne immune per formazione.
From playlist Sproloqui e commenti (in Italian)