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Under which circumstances would it be appropriate to remove outlying data points from the analysis and conclusions in a scientific study?
Answer: If an outlying data point causes the analysis and conclusion of a scientific study to become error then it would be appropriate to remove the underlying data points from the analysis and conclusion in a scientific study.
What should outliers be replaced with?
Outlier treatment is the process of removing or replacing conversions, visits, or visitors with a “normal” data point. Removal involves eliminating the data point from the sample. Replacement involves swapping the data point for the mean or median of the sample.
When to eliminate the outliers in data analysis?
When to eliminate the outliers? The outliers can be eliminated easily, if you are sure that there are mistakes in the collection and/or in the reporting of data.
Can a conversion be determined as an outlier?
This is because a singular conversion or a 1 or 0 cannot be determined as an outlier, but the value within that conversion can. This is similar to visits and visitors, which are made up of many actions that can be used to define data point.
How to detect and remove outliers in SciPy?
In most of the cases a threshold of 3 or -3 is used i.e if the Z-score value is greater than or less than 3 or -3 respectively, that data point will be identified as outliers. We will use Z-score function defined in scipy library to detect the outliers. from scipy import stats import numpy as np z = np.abs (stats.zscore (boston_df))
Where are most of the outliers on the plot?
Looking at the plot above, we can most of data points are lying bottom left side but there are points which are far from the population like top right corner. The Z-score is the signed number of standard deviations by which the value of an observation or data point is above the mean value of what is being observed or measured.