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1. Top results related to definition of outlier analysis

   • Outlier Analysis in R

     This article will focus on the outlier analysis, including Outlier detection and removal in the R programming language.

     What are outliers?

     Data points far from the dataset’s other points are considered outliers. This refers to the data values dispersed among other data values and upsetting the dataset’s general distribution. Outlier detection is a statistical approach used to find outliers in datasets. Measurement errors, incorrect data entry, or really anomalous data values are just a few of the causes of outliers.

     Effects of an outlier on the model:

     • The format of the data appears to be skewed.
     • Modifies the mean, variance, and other statistical characteristics of the data’s overall distribution.
     • Leads to the model’s accuracy level being biased.

     Steps involving Outlier detection:

     Step 1: In this step, we will be, by default creating the data containing the outliner inside it using the rnorm() function and generating 500 different data points. Further, we will be adding 10 random outliers...

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   • Outlier Analysis for Ordinal Data

     Top Answer

     Answered Mar 20, 2014 · 1 votes

     your data looks something like this:

     x = rep(1:7, c(3, 4,17, 21, 48, 118, 93)) y = c( rep(1:7,c(1,2,0,0,0,0,0)), rep(1:7,c(2,0,1,1,0,0,0)), rep(1:7,c(10,3,2,1,0,0,1)), rep(1:7,c(15,3,1,1,1,0,0)), rep(1:7,c(20,10,2,10,3,2,1)), rep(1:7,c(40,20,20,30,3,4,1)), rep(1:7,c(50,25,10,5,3,0,0)))

     the plot:

     library(car)sp(x,y, jitter = list(x=0.8, y=0.8), smoother=F, reg.line = F)-

     If you just want to know whether a given value is an outlier in your data (i.e. a univariate outlier analysis), you can use:

     library(outliers)grubbs.test(x)-

     or simply use boxplot which values are plotted as outliers:

     boxplot(x, plot=F)$out-

     If you need multivariate outliers, you can use the mvoutlier package (see functions ?chisq.plot and ?pcout):

     library(mvoutlier)pcout(x=data.frame(x,y))-

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     2/5

   • Outlier Analysis on a 2D array in Matlab

     Top Answer

     Answered Feb 11, 2018 · 1 votes

     Normally, this operation on Matlab is performed using the isoutlier function. The mean-variance approach for detecting outliers (the one your are refering to in your question) is applied on the observations when the method parameter is passed as mean to the function call:

     method — Method for determining outliers 'median' (default) | 'mean' | 'quartiles' | 'grubbs' | 'gesd'

     'mean' Returns true for elements more than three standard deviations from the mean. This method is faster but less robust than 'median'.

     Unfortunately, it is based on 3 standard deviations away from the mean instead of only 1 standard deviation away from the mean. Hence, a custom code must be implemented. Given the matrix of observations A, this should work fine:

     m = nanmean(A,1);s = nanstd(A,1); lb = m - s;ub = m + s;is_outlier = ((A < lb) | (A > ub));

     The is_outlier variable will be a matrix of logical values of the same size of A, in which true values indicate an outlier:

     A = [ 10.836126 6.342457 6.359280 4.865735 11.332891; 13.228078 17.779455 11.021488 10.057800 13.371285; 11.350769 15.052270 10.556633 7.909101 7.627935; 11.841888 4.655980 8.363947 10.808192 12.761605; 2.534398 6.189042 6.987835 8.412396 8.166305];m = nanmean(A,1);s = nanstd(A,1); lb = m - s;ub = m + s;is_outlier = ((A < lb) | (A > ub))is_outlier = 0 0 1 1 0 0 1 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 0 0 1

     The only doubt I have concerns the means and variances you provided. They differ from the ones you calculate on the given data:

     A = [ 10.836126 6.342457 6.359280 4.865735 11.332891; 13.228078 17.779455 11.021488 10.057800 13.371285; 11.350769 15.052270 10.556633 7.909101 7.627935; 11.841888 4.655980 8.363947 10.808192 12.761605; 2.534398 6.189042 6.987835 8.412396 8.166305];m = nanmean(A,1)s = nanstd(A,1)m = 9.9582518 10.0038408 8.6578366 8.4106448 10.6520042s = 3.79639885205053 5.33862997268428 1.86284269833871 2.062972979429 2.35083838144151

     So if you have to use those instead, you can simply proceed as follows:

     A = [ 10.836126 6.342457 6.359280 4.865735 11.332891; 13.228078 17.779455 11.021488 10.057800 13.371285; 11.350769 15.052270 10.556633 7.909101 7.627935; 11.841888 4.655980 8.363947 10.808192 12.761605; 2.534398 6.189042 6.987835 8.412396 8.166305];m = [9.9900 10.0057 10.0180 10.0051 9.9312];s = [3.0257 3.0208 3.0119 2.9924 2.9868];lb = m - s;ub = m + s;is_outlier = ((A < lb) | (A > ub))is_outlier = 0 1 1 1 0 1 1 0 0 1 0 1 0 0 0 0 1 0 0 0 1 1 1 0 0

     Once you know which elements of A must be considered as outliers, you can proceed treating them as you prefer. For example, you could set them to NaN as follows:

     A(is_outlier) = NaN;-

     or you could remove all the columns containing one or more outlier as follows:

     A(any(is_outlier),:) = [];-

     Consider that the more you increase the tolerance from the mean, the more the outliers become rare. Using the built-in isoutlier function, which is based on 3 standard deviations away from the mean, very few outliers are detected:

     A = [ 10.836126 6.342457 6.359280 4.865735 11.332891; 13.228078 17.779455 11.021488 10.057800 13.371285; 11.350769 15.052270 10.556633 7.909101 7.627935; 11.841888 4.655980 8.363947 10.808192 12.761605; 2.534398 6.189042 6.987835 8.412396 8.166305];isoutlier(A) % lb = m - (3 * s) AND ub = m + (3 * s)ans = 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0

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   • definition of base has to precede definition of derived contract

     Top Answer

     Answered May 07, 2021 · 4 votes

     You are instantiating a contract inside another contract which is inhering the callee. Kinda circular reference.

     When you inherit a contract(Project1) from a base contract (Project 2), the inheriting (Project1) should be the one that makes the calls. Your (Project2) contract acts like a base contract. The inheriting contract (Project1) can call or override all the functions in the base contract if needed. So here my suggestions (it's one way among many to solve your problem). See below. I compiled it and deployed and it works. Don't forget to provide an address when deploying. I hope this would help. If you have other questions you reach to me.

     // SPDX-License-Identifier: GPL-3.0pragma solidity >=0.7.0 <0.9.0;pragma experimental ABIEncoderV2;-contract Project2{ struct Apple{ string name; bool applepie; //mapping (address => bool) applepie; } Apple[] public applepies; Apple public newProject; constructor() { //address newProject1Address = address(new Project1(msg.sender)); //uint idx = applepies.length; newProject = Apple("superApple", true); applepies.push(newProject); } function getNewProject() public view returns(string memory){ return newProject.name; } }contract Project1 is Project2{ address public creator; constructor (address creator1){ creator= creator1; } function getDetails(uint index) public view returns (string memory){ Apple storage newv= applepies[index]; //require(newv.applepie = msg.sender); return newv.name; }}

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     4/5

   • Definition of base has to precede definition of derived contract (ERC721 implementation)

     Top Answer

     Answered Apr 02, 2022 · 1 votes

     Do you need to import ERC721Connector contract in your ERC721 contract? If not, you can remove

     import "./ERC721Connector.sol"; // line 4, ERC721.sol-

     from your file and it should work fine. Your imports are causing the issue,

     ERC721Connector tries to import ERC721, but ERC721 also needs ERC721Connector so the compiler says

     Definition of base has to precede definition of derived contract

     Because the base contract doesn't precede the defined contract.

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     5/5

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2. People also ask

   What are outliers in statistics?

   • Outliers stand for data points that are indicative of a much higher variability than other observations in a given dataset. This can result in skewing statistical studies and wrong conclusions after all the variables are not adequately identified and handled.

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   What are outliers in data preprocessing?

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   What is the difference between anomalies and outliers?

   • Anomalies can refer to any data point that deviates from the norm, while outliers specifically denote extreme values that lie far away from the majority of the data points. Many methods can be applied to both anomalies and outliers. Btw, I wrote about a novel anomaly detection method that I developed, feel free to check it out!

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   Why is it important to recognize and understand outliers?

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   Outlier in Statistics: Definition, Meaning, Examples - GeeksforGeeks

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3. www.geeksforgeeks.org › outlierOutlier in Statistics: Definition, Meaning, Examples ...

   www.geeksforgeeks.org › outlier

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   May 29, 2024 · Outliers stand for data points that are indicative of a much higher variability than other observations in a given dataset. This can result in skewing statistical studies and wrong conclusions after all the variables are not adequately identified and handled.

4. towardsdatascience.com › the-ultimate-guide-toThe Ultimate Guide to Finding Outliers in Your Time-Series ...

   towardsdatascience.com › the-ultimate-guide-to

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   May 21, 2024 · Outliers can significantly skew and misrepresent the key statistics of a dataset, such as the mean, variance, and correlation coefficients. Outliers compromise the performance of predictive models. Outliers can mask true trends and cyclic behavior in your time-series data.

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   May 22, 2024 · Our augmented intelligence framework has five steps: define a patient population with a desired clinical outcome, build a predictive model, identify outliers through appropriate measures, investigate outliers through domain content experts, and generate scientific hypotheses.

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   May 30, 2024 · What is Outlier Analysis? Outlier analysis, also called anomaly detection, is key in data mining. It spots unusual data points. These may be errors, natural variations, or rare events. The goal is to maintain data quality and accuracy by finding and fixing these anomalies.

8. medium.com › @viveklashkare7777 › understanding-andUnderstanding and Managing Outliers in Machine Learning ...

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   May 14, 2024 · Outliers are data points that fall far outside the expected range of values in a dataset. They can manifest as unusually high or low values compared to the majority of...

9. www.linkedin.com › advice › 0Effective Outlier Detection Techniques in Data Analytics

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   May 19, 2024 · Effective Outlier Detection Techniques in Data Analytics. Last updated on May 19, 2024. All. Engineering. Data Analytics. How can you detect outliers in your dataset effectively? Powered by AI...