References
Arthur, David, and Sergei Vassilvitskii. 2006. “K-Means++: The
Advantages of Careful Seeding.”
Baumer, Benjamin S, Daniel T Kaplan, and Nicholas J Horton. 2017.
Modern Data Science with r. Chapman; Hall/CRC.
Bayes, Thomas. 1958. Essay Toward Solving a Problem in the Doctrine
of Chances. Biometrika Office.
Breiman, L, JH Friedman, R Olshen, and CJ Stone. 1984.
“Classification and Regression Trees.”
Gareth, James, Witten Daniela, Hastie Trevor, and Tibshirani Robert.
2013. An Introduction to Statistical Learning: With Applications in
r. Spinger.
Grolemund, Garrett. 2014. Hands-on Programming with r: Write Your
Own Functions and Simulations. " O’Reilly Media, Inc.".
James, Gareth, Daniela Witten, Trevor Hastie, Robert Tibshirani, et al.
2013. An Introduction to Statistical Learning. Vol. 112. 1.
Springer.
Kutner, Michael H, Christopher J Nachtsheim, John Neter, and William Li.
2005. Applied Linear Statistical Models. 5th ed. McGraw-Hill
Education.
Lantz, Brett. 2019. Machine Learning with r: Expert Techniques for
Predictive Modeling. Packt publishing ltd.
Messerli, Franz H. 2012. “Chocolate Consumption, Cognitive
Function, and Nobel Laureates.” N Engl J Med 367 (16):
1562–64.
Moro, Sérgio, Paulo Cortez, and Paulo Rita. 2014. “A Data-Driven
Approach to Predict the Success of Bank Telemarketing.”
Decision Support Systems 62: 22–31.
Pearl, Judea, and Dana Mackenzie. 2018. The Book of Why: The New
Science of Cause and Effect. Basic Books. https://www.basicbooks.com/titles/judea-pearl/the-book-of-why/9781541644649/.
Sutton, Richard S, Andrew G Barto, et al. 1998. Reinforcement
Learning: An Introduction. Vol. 1. 1. MIT press Cambridge.
Wheelan, Charles. 2013. Naked Statistics: Stripping the Dread from
the Data. WW Norton & Company.
Wickham, Hadley, Garrett Grolemund, et al. 2017. R for Data
Science. Vol. 2. O’Reilly Sebastopol, CA.
Wolfe, Douglas A, and Grant Schneider. 2017. Intuitive Introductory
Statistics. Springer.