Introducing Linear Algebra
Comprehensive exploration of linear algebra concepts, from basic operations to advanced topics, with practical applications using Julia and Python programming languages.
Regression
Explore point estimation, maximum likelihood, and distribution for regression coefficients, focusing on ordinary least squares and slope estimator analysis.
Learn Statistics with Python
Master Python for statistical analysis: design matrices, linear models, F distribution, and OLS. Hands-on tutorials using Orange and Patsy libraries for practical data science skills.
Modern Linear Algebra Using Python Instead of a Textbook
Explore linear algebra concepts through hands-on Python programming, covering matrix operations, decompositions, and applications in machine learning and data analysis.
Simultaneous Equation Model - Part III: Simultaneity Bias and OLS Implications
Explore simultaneity bias and its impact on OLS estimation in simultaneous equation models. Gain insights into addressing this econometric challenge.
Gauss-Markov Theorem
Explore the fundamental principles and applications of the Gauss-Markov theorem in statistical analysis and linear regression modeling.
Diagnostics to Improve Linear Model Fit - OLS Analysis and Residual Plots
Explore techniques to enhance linear model fit through residual analysis, normality checks, and outlier detection in OLS regression.
Hands-on with Python: Estimating Regression Coefficients with OLS and Abline - Part 2
Estimate regression coefficients using OLS and abline in Python, gaining practical experience with statistical analysis techniques.
Multivariate Statistical Analysis
Comprehensive exploration of multivariate statistical techniques, covering theory, problem-solving, and implementation in R and Excel. Ideal for data analysis across various fields.