Covers the basics of linear regression in machine learning, exploring its applications in predicting outcomes like birth weight and analyzing relationships between variables.
Introduces linear regression, covering line fitting, training, gradients, and multivariate functions, with practical examples like face completion and age prediction.
Explores heteroskedasticity in econometrics, discussing its impact on standard errors, alternative estimators, testing methods, and implications for hypothesis testing.
Explores heteroskedasticity and autocorrelation in econometrics, covering implications, applications, testing methods, and hypothesis testing consequences.
