Thesis Remove Calculations For Free

It's important to investigate the nature of the outlier before deciding. If it is obvious that the outlier is due to incorrectly entered or measured data, you should drop the outlier: ... If the outlier does not change the results but does affect assumptions, you may drop the outlier.

Outliers can have a big impact on your statistical analyses and skew the results of any hypothesis test if they are inaccurate. These extreme values can impact your statistical power as well, making it hard to detect a true effect if there is one.

Outliers can have a big impact on your statistical analyses and skew the results of any hypothesis test if they are inaccurate. These extreme values can impact your statistical power as well, making it hard to detect a true effect if there is one.

Removal of outliers creates a normal distribution in some of my variables, and makes transformations for the other variables more effective. Therefore, it seems that removal of outliers before transformation is the better option.

Outliers can be treated in different ways, such as trimming, capping, discretization, or by treating them as missing values. Emperical relations are used to detect outliers in normal distributions, and Inter-Quartile Range (IQR) is used to do so in skewed distributions.

We can conclude that removing outliers increases the accuracy of the model. Even though it increased the accuracy with a significant amount in k-NN, it decreased in decision trees.

Answer and Explanation: If the data set contains outliers then the deviation from the mean will increase, causing a high standard deviation. So, if the outliers are removed, the standard deviation will be lower, likely representing the real population characteristics.