The advanced data analysis techniques include machine learning, forecasting, graph analysis, pattern matching, simulation, and many more. In this blog, we will address univariate outliers through the application of advanced data analysis techniques. Before discussing the univariate outlier, let us understand this with a simple example.
Suppose there is an international footballer you know and also there is an international cricketer you know. But the chances are very minuscule that a player can play cricket and football both at international levels. Well, the univariate outlier identifies these types of extreme cases where this kind of phenomenon can happen. Now, let us understand this a little bit deeper.
Identifying univariate outliers
A univariate outlier is an observation that is significantly different from other observations in a univariate dataset. In other words, it is an extreme value or an observation that is far away from the other observations in a single variable dataset. Univariate outliers can be detected by looking at the distribution of the data and identifying observations that fall outside of the typical range.
There are several ways to identify univariate outliers, including visual inspection of the data, such as box plots, histograms, and scatter plots, and statistical methods, such as the Z-score method and the Interquartile Range (IQR) method.
The Z-score method involves calculating the standard deviation and mean of the data, and then determining which observations fall more than a certain number of standard deviations from the mean. The IQR method involves determining the first and third quartiles (Q1 and Q3) of the data and then identifying observations that fall outside of the range of Q1 - 1.5 * IQR to Q3 + 1.5 * IQR, where IQR is the interquartile range (Q3 - Q1).
It's important to note that identifying outliers is just one step, and it's crucial to investigate the cause of outliers, as they can be caused by errors in data collection or measurement, or they can be legitimate extreme values that are important to understand in the context of the research.
Figure 1: Univariate outlier
Addressing univariate outliers through the application of advanced data analysis techniques
Figure 2: Example of univariate outlier
There are several advanced data analysis techniques that can be used to address univariate outliers:
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Winsorizing: This technique involves replacing the outlier values with a value that is closer to the rest of the data. It helps to reduce the effect of outliers on the overall distribution of the data.
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Trimmed Means: This technique involves removing a certain percentage of the extreme values from the dataset before calculating the mean. This helps to reduce the effect of outliers on the overall mean of the data.
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Robust Statistics: Robust statistics are methods that are less sensitive to outliers than traditional statistics. Methods such as the median and the interquartile range are considered to be robust statistics. These can be used as alternatives to the mean and standard deviation to summarise data.
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Transformation: Transformations can be used to reduce the effect of outliers on data. For example, taking the logarithm of the data can help to reduce the effect of large values on the overall distribution of the data.
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Imputation: This technique involves replacing missing values with estimates. For outlier detection, it can be used to replace the outlier values with estimates based on the rest of the data.
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Re-sampling: Resampling techniques such as bootstrapping can be used to generate new samples from the data, which can be used to estimate the effect of outliers on the overall distribution of the data.
It's important to note that these techniques should be used with caution, as each technique has its own assumptions and limitations, and it's crucial to understand the assumptions and limitations of each technique and choose the most appropriate one that fits the research question and the data characteristics.
In summary, addressing univariate outliers through the application of advanced data analysis techniques, such as winsorizing, trimmed means, robust statistics, transformation, imputation, and re-sampling, can help to reduce the effect of outliers on the overall distribution of the data and make the data more robust. It's important to choose the appropriate technique that fits the research question and the data characteristics. We, at ELK Statistics, can help you to address univariate outliers by applying advanced data analysis techniques at an affordable price. Visit our website https://www.elkconsulting.com.my/ for more information.