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- Success Rate¶ Success Rate based outlier detection aggregates success rate data from every host in a cluster.[1]
- Ejection event logging is configured in the Cluster manager outlier detection configuration.[1]
- Weighting factors should be considered when identifying outliers.[2]
- Now the outliers are relatively ignored, so the curve comes closer to the bulk of the points.[2]
- Using the Benjamini and Hochberg method to detect outliers.[2]
- Both of the points with the lowest P values (the two points furthest from the robust best-fit curves) are defined to be outliers.[2]
- It is important to detect outliers because they can significantly alter the results of the data analysis.[3]
- The outlier has had no effect on the median figure.[3]
- The mode is a good representation of the results and has not been affected by the outlier.[3]
- Mean The mean is affected by the outlier and may not be a suitable representation when reporting on this information.[3]
- Origin provides methods and tools to help finding and testing for outliers.[4]
- Dixon's Q test are available, and it's also possible to roughly visualize the outlier using the Q-Q plot.[4]
- To detect an outlier from regression, you may use the standardized residuals.[4]
- Here we will consider only the case of representative outliers, i.e., correct values representing other units in the population.[5]
- As you can see, these four algorithms work differently, so they don’t always agree on which points are outliers.[6]
- Think of an outlier as an outsider.[7]
- An outlier refers to anything that strays from, or isn’t part of, the norm.[7]
- If you like to sleep in a tent in the backyard while your neighbors and family sleep in their beds, you’re probably an outlier.[7]
- You may also be an outlier if you have to travel far to your job.[7]
- There is not a hard and fast rule about how much a data point needs to differ to be considered an outlier.[8]
- In this case, “outliers”, or important variations are defined by existing knowledge that establishes the normal range.[8]
- When using statistical indicators we typically define outliers in reference to the data we are using.[8]
- One of the reasons we want to check for outliers is to confirm the quality of our data.[8]
- There is no rule to identify the outliers.[9]
- Also plotting the data on a number line as a dot plot will help in identifying the outliers.[9]
- Our outliers are in wildly different industries, including pharmaceuticals, beer, construction, and banking.[10]
- That didn’t hold, either: The outliers were not small relative to the full pool of companies we analyzed.[10]
- More than half the outliers were established in their current form after 1980, but the two oldest date from 1903 and 1906.[10]
- Unlike their competitors, outliers appear to make fewer big, high-risk bets—which is also consistent with an options orientation.[10]
- Identifying outliers in a stack of data is simple.[11]
- Click Analyze from a Column data table, and then choose Identify outliers from the list of analyses for Column data.[11]
- Note: This page explains how to identify an outlier from a stack of values in a data table formatted for Column data.[11]
- We developed the ROUT method to detect outliers while fitting a curve with nonlinear regression.[11]
- An observation must always be compared to other observations made on the same phenomenon before actually calling it an outlier.[12]
- An outlier may be due to the variability inherent in the observed phenomenon.[12]
- In some domains, it is common to remove outliers as they often occur due to a malfunctioning process.[12]
- In other fields, outliers are kept because they contain valuable information.[12]
- An outlier is an element of a data set that distinctly stands out from the rest of the data.[13]
- The easiest way to detect outliers is to create a graph.[13]
- Plots such as Box Plots, Scatterplots and Histograms can help to detect outliers.[13]
- Alternatively, we can use mean and standard deviation to list out the outliers.[13]
- Often outliers are discarded because of their effect on the total distribution and statistical analysis of the dataset.[14]
- You could take a guess that 3 might be an outlier and perhaps 61.[15]
- And even if you do, some boxplots may not show outliers.[15]
- Therefore, don’t rely on finding outliers from a box and whiskers chart.[15]
- That said, box and whiskers charts can be a useful tool to display them after you have calculated what your outliers actually are.[15]
- If you’re optimizing your site for revenue, you should care about outliers.[16]
- This article outlines a case in which outliers skewed the results of a test.[16]
- Think your data is immune to outliers?[16]
- But is there a statistical way of detecting outliers, apart from just eyeballing it on a chart?[16]
- Despite all this, as much as you’d like to, it is NOT acceptable to drop an observation just because it is an outlier.[17]
- Her true weight was probably 91, 119, or 190 lbs, but since I didn’t know which one, I dropped the outlier.[17]
- If the outlier does not change the results but does affect assumptions, you may drop the outlier.[17]
- More commonly, the outlier affects both results and assumptions.[17]
- Outliers that arise simply from the skewness of the distribution can be removed, and previously hidden outliers may be forced into view.[18]
- Data outliers can deceive the training process resulting in longer training times and less accurate models.[19]
- In some cases, the presence of outliers are informative and will require further study.[19]
- However, none of these methods will deliver the objective truth about which of the observations are outliers.[19]
- Suppose we have reasons to believe that outliers are due to mechanical errors or problems during measurement.[19]
- An outlier may also be an indication of contamination.[20]
- Data should not be excluded simply because they are identified as outliers.[20]
- Once you have been identified outliers should be further evaluated to determine the reason for their existence.[20]
- Box plots and probability plots are good tools for screening the data to identify possible outliers.[20]
- In statistics, an outlier is a data point that differs greatly from other values in a data set.[21]
- Employees #2 and #19 are both outliers because their data values exist outside of the general trend in the overall data sample.[21]
- Many parametric statistics, like mean, correlations, and every statistic based on these is sensitive to outliers.[22]
- There is no precise way to define and identify outliers in general because of the specifics of each dataset.[23]
- Even with a thorough understanding of the data, outliers can be hard to define.[23]
- outliers and bad data in your dataset is probably one of the most difficult parts of data cleanup, and it takes time to get right.[23]
- This does not mean that the values identified are outliers and should be removed.[23]
- We have a list of 15 numbers here, and what I want to do is think about the outliers.[24]
- And so some people might say, "Okay, we have three outliers.[24]
- And then we can figure out by that definition, what is going to be an outlier?[24]
- Now to figure out outliers, well, outliers are gonna be anything that is below.[24]
- An outlier in a distribution is a variable that is far removed from the set of variables.[25]
- It is important to be sure that the points we find are outliers.[25]
- A skewed distribution may look like outliers, but are actual tails.[25]
- 4.8A shows an outlier to the far right.[25]
- A careful examination of a set of data to look for outliers causes some difficulty.[26]
- The interquartile range is what we can use to determine if an extreme value is indeed an outlier.[26]
- Multiplying the interquartile range (IQR) by 1.5 will give us a way to determine whether a certain value is an outlier.[26]
- If we subtract 1.5 x IQR from the first quartile, any data values that are less than this number are considered outliers.[26]
- An outlier is an observation that lies outside the overall pattern of a distribution (Moore and McCabe 1999).[27]
- Usually, the presence of an outlier indicates some sort of problem.[27]
- When performing least squares fitting to data, it is often best to discard outliers before computing the line of best fit.[27]
- This is particularly true of outliers along the direction, since these points may greatly influence the result.[27]
- Unfortunately, there are no strict statistical rules for definitively identifying outliers.[28]
- Finding outliers depends on subject-area knowledge and an understanding of the data collection process.[28]
- In this post, I’ll explain what outliers are and why they are problematic, and present various methods for finding them.[28]
- To demonstrate how much a single outlier can affect the results, let’s examine the properties of an example dataset.[28]
- Naive interpretation of statistics derived from data sets that include outliers may be misleading.[29]
- Alternatively, an outlier could be the result of a flaw in the assumed theory, calling for further investigation by the researcher.[29]
- The modified Thompson Tau test is used to find one outlier at a time (largest value of δ is removed if it is an outlier).[29]
- Some work has also examined outliers for nominal (or categorical) data.[29]
- Unfortunately, all analysts will confront outliers and be forced to make decisions about what to do with them.[30]
- It’s essential to understand how outliers occur and whether they might happen again as a normal part of the process or study area.[30]
- Unfortunately, resisting the temptation to remove outliers inappropriately can be difficult.[30]
- In my previous post, I showed five methods you can use to identify outliers.[30]
- The estimates can be very sensitive to the removal of outliers as the following two examples illustrate.[31]
- An outlier is an observation that lies an abnormal distance from other values in a random sample from a population.[32]
- These points are often referred to as outliers.[32]
- Outlier detection criteria A point beyond an inner fence on either side is considered a mild outlier.[32]
- The outlier is identified as the largest value in the data set, 1441, and appears as the circle to the right of the box plot.[32]
소스
- ↑ 1.0 1.1 Outlier detection — envoy 1.17.0-dev-647435 documentation
- ↑ 2.0 2.1 2.2 2.3 Detecting outliers when fitting data with nonlinear regression – a new method based on robust nonlinear regression and the false discovery rate
- ↑ 3.0 3.1 3.2 3.3 Effective Practices
- ↑ 4.0 4.1 4.2 Detecting Outliers
- ↑ Outlier Treatment (Method)
- ↑ Machine Learning in the Elastic Stack [7.10]
- ↑ 7.0 7.1 7.2 7.3 outlier - Dictionary Definition
- ↑ 8.0 8.1 8.2 8.3 What is an Outlier?
- ↑ 9.0 9.1 Outliers
- ↑ 10.0 10.1 10.2 10.3 How the Growth Outliers Do It
- ↑ 11.0 11.1 11.2 11.3 GraphPad Prism 9 Statistics Guide
- ↑ 12.0 12.1 12.2 12.3 Outliers detection in R
- ↑ 13.0 13.1 13.2 13.3 What is Outlier Analysis and How Can It Improve Analysis?
- ↑ Outlier
- ↑ 15.0 15.1 15.2 15.3 Outliers: Finding Them in Data, Formula, Examples. Easy Steps and Video
- ↑ 16.0 16.1 16.2 16.3 How to Deal with Outliers in Your Data
- ↑ 17.0 17.1 17.2 17.3 Outliers: To Drop or Not to Drop
- ↑ meaning in the Cambridge English Dictionary
- ↑ 19.0 19.1 19.2 19.3 Outlier Detection and Treatment: A Beginner's Guide
- ↑ 20.0 20.1 20.2 20.3 5.10 Identification of Outliers
- ↑ 21.0 21.1 What is an Outlier?
- ↑ Outlier — Why is it important?. The tale of the extreme data
- ↑ 23.0 23.1 23.2 23.3 How to Remove Outliers for Machine Learning
- ↑ 24.0 24.1 24.2 24.3 Judging outliers in a dataset (video)
- ↑ 25.0 25.1 25.2 25.3 Outlier - an overview
- ↑ 26.0 26.1 26.2 26.3 Determining Outliers in Statistics
- ↑ 27.0 27.1 27.2 27.3 Outlier -- from Wolfram MathWorld
- ↑ 28.0 28.1 28.2 28.3 5 Ways to Find Outliers in Your Data
- ↑ 29.0 29.1 29.2 29.3 Wikipedia
- ↑ 30.0 30.1 30.2 30.3 Guidelines for Removing and Handling Outliers in Data
- ↑ Outlier - an overview
- ↑ 32.0 32.1 32.2 32.3 7.1.6. What are outliers in the data?
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- [{'LEMMA': 'outlier'}]