Quantile Regression Model Theory & Application | Statistical Models

Quantile Regressions are used when the extreme observations are important for us to study. Linear Regression does a bad job when we are interested in knowing the different section of the population differently. For example if we are interested in knowing the change in consumption pattern of population with increase in income differently for low income group and high income group then linear regression wont be useful. Quantile regression is useful in such cases where we can get to know things at different quantile of interest For courses on Credit risk modelling, Market Risk Analytics, Marketing Analytics, Supply chain Analytics and Data Science/ML projects contact [email protected] For Study Packs : http://analyticuniversity.com/ Complete Data Science Course : http://bit.ly/34Sucmb Access All Coursera Plus courses @ $400 : https://bit.ly/2ZL51Dd Discounted courses on Udemy (for $11): http://bit.ly/2LYU6hp Free access to Skillshare: http://bit.ly/2thklJu Coursera : Data Science : http://bit.ly/37nABr6 Data Science Python : http://bit.ly/2ZK5oMm Recommended Data Science Books on Amazon : Python for Data Science: https://geni.us/PythonDataScience R for Data Science : https://geni.us/DataScienceR Machine Learning using Tensorflow: https://geni.us/MLinTensorflow Data Science from Scratch: https://geni.us/DataSciencefromScratch Python programming: https://geni.us/LearnPython Artificial Inteligence: https://geni.us/LearnAI Data Vizualization : https://geni.us/DataViz 20% discounts on below live courses : use coupon YOUTUBE20 Data Science Live Training : AI and Tensorflow: http://bit.ly/2tOnOzA Python : http://bit.ly/2QkH1QQ