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Outlier Analysis - Planning and Top Tools to Use for It

Outlier Analysis
Outliners are some unexceptional values in datasets. These often violated, and distort the outcomes of statistical analysis. Unwillingly, all analysts have to face some unusual problems like that of outliers. It is necessary for them to decide how to deal with such outliers. Estimating the risks related to outliers, you may think it would be better to delete them from the data. But this cannot be done all the time either. Deleting the outliers from analysis is advisable only for certain conditions.

Outliers can provide lots of information on the topic and process of data collection. Knowing the reason for the outliers is an essential. In planning part of outlier analysis, the dissertation writer must detect its reason. They need to know whether the outliers are normal or not. This is because outliers decrease the power of statistical analysis. So we should remove it wisely. Still, complete removal of outliers is near to impossible. Hence during the planning of outlier’s analysis, there are two main parts;

Detection

The detection of outliers is the first step in the planning part of outlier analysis. Hence, there are certain techniques for detecting the characteristics of outliers. All techniques differ from each other based on some parameters. The outlier in one technique may not be an outlier in another one. Hence to detect outliers, the analyst must consult different outlier labelling algorithms. Further, certain features can help an analyst;

Distance-based Detection

The outliers are generally different from all other points in a data set. Distance-based outliers are certain points that don’t follow the general trends as others follow. Commonly, the outliers that are not too far from their data set are easy to avoid. Hence, the distance is a remarkable factor that helps in outlier analysis-detection.

Density-based Detection

Density is another characteristic that helps analysts to locate the outlier. In general cases, the normal datasets have high density. So the location of outlier in the graphical representation will be that of a low-density area. In short, density is the second most characteristic that helps with regards to outlier detection.

Clustering

If the data set contains cluster analysis distance-based detection may not work. In this case, density can also work. The incidence of outliers in a small dataset is very small. But as the sample size increases, its detection becomes difficult. The presence of outliers within cluster analysis results in lose-cluster formation. For tight cluster formation, outliner deletion is important.

Hence, there are many types of outliers with the same purpose to decrease the statistical significance of analysis. So outlier analysis is the most important part of statistical analysis. You can take help from a series of tools for outlier analysis as well.

Removing of the Outliers:

To remove the outliers in a data set, we can use the list of tools. Some of them are as follows;

SPSS and Outliers

There is no command on SPSS that specifically deletes the outliers from a datasheet. You first need to detect outliers based on the above discussion. After that, the select cases option can remove them. Hence, you can remove outliers according to your observation by using the filters.

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STATA and Outliers:

Like SPSS, STATA does not offer its users the option of deleting the outliers in only one command. Here too, you will first need to detect the outliers. The most frequent way of outlier detection is observation. After detection of the outlier, you can seek help from the built-in features of the STATA. STATA is very efficient in the clearing, dropping variables, and outlier dealing features. It also helps its users in presenting the data through various forms that may help in Outlier detection and removal.

A majority of statisticians don’t prefer outlier removal. The outliers are also an important part of the dataset. They hint towards the experimental variables as well. It is not good to delete them because they make our data unpleasant. But still, if you want to remove them, then Winsor, Grubbs, and Bacon will serve as useful tools for achieving the aims.

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Emma Charlotte
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