Adjusted Boxplot for skewed distribution — Introduction In the pervious article, I have discussed about outlier detection procedures for mostly normal distributions. The procedures include IQR (Inter-Quartile Range) Standard deviation Z-score Modified Z-score We have gone through the boxplots after these abovementioned procedures are implemented and shown the number of outliers in each case. In real…

Use of Statsmodels, Polyfit, and Linear Regression and Polynomial Features — Introduction Regression is one of the most essential subject for prediction analytics and business forecast. It can be implemented both in linear fashion and by using higher order polynomials. There are instances where the model can be generated using multiple linear regression but many of the real world cases have…

Use of Cook’s Distance in Scikit-Learn and Statsmodel for Regression Outlier Detection — Introduction Outliers are defined as abnormal values in a dataset that don’t go with the regular distribution and have the potential to significantly distort any regression model. Therefore, outliers must be carefully handled in order to get the right insight from the data. Usually, the data collected from real-world, consists…

IQR, Hampel and DBSCAN method — Outliers, one of the buzzwords in the manufacturing industry, has driven engineers and scientists to develop newer algorithms as well as robust techniques for continuous quality improvement. If the data include even if one outlier, it has the potential to dramatically skew the calculated parameters. Therefore, it is of utmost…

Statistics for long tailed distribution Introduction In this article, I am going to show skewed distribution statistics. Most importantly I am going to talk about mean and median which are the basic statistics for skewed distributions. I guess mostly we are familiar with normal distribution. Normal distribution is ideal. Normal…

Statsmodel library model summary explanation — Introduction Regression analysis is the bread and butter for many statisticians and data scientists. We perform simple and multiple linear regression for the purpose of prediction and always want to obtain a robust model free from any bias. …