Binary and Multinomial Logistic Regression
This project was inspired by an assignment for GGRC42, a course taught by Dr. Steven Farber in the fall of 2021. The main focus of the project was to develop binary and multinomial logistic regression models for two datasets, one of which was the well-known iris dataset.
By fitting these models, I was able to draw useful insights and provide explanations and interpretations of the results. Through this project, I aimed to showcase my skills in data analysis and modeling and demonstrate my understanding of logistic regression.
The use of binary and multinomial logistic regression models allowed for the analysis of data with two or more outcome categories, which is particularly useful in many fields, including medicine, social sciences, and marketing. By applying these models to real-world datasets, I was able to gain practical experience in the field of data analysis.