Engineering Econometrics
This course will be exclusively quantitative and an application to business/management-related problems. It is connected with problem sets and real-life cases to know the relevance of a particular problem and the decision-making thereof.
Classical Linear Regression Model - Part 4
Explore advanced concepts in classical linear regression, focusing on model interpretation, diagnostics, and practical applications in statistical analysis.
Classical Linear Regression Model - Part 3
Explore advanced concepts of classical linear regression, including model assumptions, estimation techniques, and statistical inference for robust data analysis.
Statistical Inference of Logistic Regression: Confusion Matrix, Errors, and Model Selection
Explore statistical inference in logistic regression, covering confusion matrices, error types, and model selection techniques for improved predictive accuracy.
Understanding Multicollinearity
Explore multicollinearity and its impact on regression analysis, focusing on the Variance Inflation Factor as a key diagnostic tool.
Classical Linear Regression Model - Part 2
Explore advanced concepts of classical linear regression, including model assumptions, parameter estimation, and statistical inference techniques.
Principal Component Analysis and Singular Value Decomposition - Spring 2023
Master dimensionality reduction techniques through PCA and SVD, exploring multicollinearity, vector basis, and practical applications with hands-on demonstrations for effective data analysis.
Preprocessing Data for Machine Learning - Deep Dive
Comprehensive guide to preprocessing data for logistic regression, covering standardization, encoding, data imbalance, correlation, multicollinearity, and missing data handling techniques.
Handling Multicollinearity with Ridge Regression in Python
Implement Ridge Regression using Python and sklearn to handle multicollinearity in data analysis.