Explores the construction and application of Hadamard matrices for efficient estimation of main effects without interactions in the Plackett-Burman Design.
Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.
Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.
Explores heteroskedasticity in econometrics, discussing its impact on standard errors, alternative estimators, testing methods, and implications for hypothesis testing.
Explores mapping non-linear data to higher dimensions using SVM and covers polynomial feature expansion, regularization, noise implications, and curve-fitting methods.
