TIM-8515 V1

Google Colaboratory. (2022). Google.com. https://colab.research.google.com/notebooks/intro.ipynb
Under Google Drive, click New | More | Google Colaboratory. Set up a Google account to use this open-source Python platform to support univariate analysis projects. The Getting Started section under the Table of Contents is helpful to those new to the environment.

Martin, O. (2018, December). Bayesian analysis with Python - Second Edition. Packt Publishing.
Read Sections "Multiple Linear Regression" under Modeling With Linear Regression (Chapter 3) and "Multiple Logistic Regression" under Generalizing Linear Models (Chapter 4)
These two sections of the text describe the similarities and differences between the multiple linear and multiple logistic regression models.

Vo.T.H, P., Czygan, M., Kumar, A., & Raman, K. (2017, March). Python: Data analytics and visualization. Packt Publishing.

• Read Module 2, Chapter 5 - Linear Regression with Python
  This chapter describes the math behind the linear regression model, how to validate the model, and handling issues with collinearity and non-linear relationships.
• Read Module 2, Chapter 6 - Logistic Regression with Python
  This chapter describes the math behind the logistic regression model, concepts such as conditional probability and odds ratio, how to validate the model, and how to interpret the model terms and coefficient values.
• Read Module 2, Chapter 9 – Best Practices for Predictive Modeling
  This chapter details the best practices for working with predictive models, including how to communicate insights gained from the statistical and analytical processes.

Chicco, D. & Jurman, G. (2020). Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Medical Informatics and Decision Making, 20 (16).

Gatsoulis, Y. (2016). A little book of python for multivariate analysis. Creative Commons.
This is a great resource for learning how to work with multivariate data using Python.

Cochran, J.J. (2018, October). INFORMS analytics body of knowledge. Wiley.
Read Chapter 7 – Machine Learning
This chapter explores supervised and unsupervised machine learning algorithms. The steps used in algorithmic modeling to address a supervised learning problem, such as regression analysis are discussed.