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Determining Outliers in Given Observations using Linear Regression Model
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 2 |
| Year of Publication: 2022 |
| Authors: Shubham Kunhare, Sachin Patel |
10.5120/ijca2022921980
|
Shubham Kunhare, Sachin Patel . Determining Outliers in Given Observations using Linear Regression Model. International Journal of Computer Applications. 184, 2 ( Mar 2022), 52-56. DOI=10.5120/ijca2022921980
Abstract
Outliers is a type of sample which are specially very far from the all other object. There is no scientific definition for an outlier. Defining whether or not an observation is an outlier it is a particular matter. It can also be defined and clarified as a member of data that really differs from the given data set. Outlier discovery is the method of identifying and eliminating outliers from a given set of data. There are no identical approaches are available to outlier, these are mainly reliant upon the data set. Outlier discovery is division of data mining and it has many applications in data study. It is significant to keep outliers in mind when observing at pools of data because they can occasionally affect how the objects are look on the whole. They can extremely change the results of the data analysis and numerical modeling. In the proposed work we found the outliers for linear data set. We used linear regression method to found the outliers. We used the value of correlation coefficient to check correctness of the proposed work. First we compute the value of correlation coefficient with outlier and then also check after eliminating the outlier, how the value is affected.
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