Is it justified to remove outliers?
However when an outlier has occurred from an error, the outlier is altering the data in false way, and can actually be beneficial to remove it. For example if a participant in a reaction time investigation, continually hits the button, even when there is no stimulus, their data is not going to be reliable or accurate. In a situation like this, I think it is justified to remove the outlier, as long as there has been made reference to it in the investigation report.
Why need to remove outlier?
There may be many truly valid reasons to remove data-points. These include outliers caused by measurement errors, incorrectly entered data-points or impossible values in real life. If you feel that any outlier are erroneous data points and you can validate this, then you should feel free to remove them.
Should I remove outliers from my data?
Outliers can greatly influence our overall statistics and sometimes removing them from our data is the best option. However an outlier should only be removed if the result has not been gathered from the population that you were aiming to sample.
How to find outlier in R?
we have loaded the dataset into the R environment using the read.csv () function.
How do you remove an outlier?
Remove outliers. To remove outliers from historical transactional data, follow these steps: Click Master planning > Setup > Demand forecasting > Outlier removal. Click New to create a query that defines which transactions to exclude from the historical data. Select the company for which the query applies, and then enter a name and description.