Basics of Linear Regression

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (45)

Linear Regression: Basics and Estimation Log in to Mediaspace to watch this video

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Applied Biostatistics: Bivariate Data and Regression Analysis Log in to Mediaspace to watch this video

Covers bivariate data analysis, correlation, and regression techniques, including interpretation of coefficients and least squares geometry.

Linear Regression: Ozone Data Analysis Log in to Mediaspace to watch this video

Explores linear regression analysis of ozone data using statistical models.

Comparison Statistics: Hypothesis Testing and ANOVA Log in to Mediaspace to watch this video

Explores comparison statistics, hypothesis testing, ANOVA, correlation, and linear regression.

Regression: Simple and Multiple Linear Log in to Mediaspace to watch this video

Covers simple and multiple linear regression, including least squares estimation and model diagnostics.

Applied Biostatistics: Bivariate Data Analysis Log in to Mediaspace to watch this video

Explores bivariate data analysis in applied biostatistics, covering correlation, regression, model selection, and diagnostics.

Heteroskedasticity: Ch. 4a Log in to Mediaspace to watch this video

Explores heteroskedasticity in econometrics, discussing its impact on standard errors, alternative estimators, testing methods, and implications for hypothesis testing.

Linear Regression: Fundamentals and Applications Log in to Mediaspace to watch this video

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

Inference and Mixed Models Log in to Mediaspace to watch this video

Covers point estimation, confidence intervals, and hypothesis testing for smooth functions using mixed models and spline smoothing.

Instrumental Variables: Addressing Measurement Error and Reverse Causality Log in to Mediaspace to watch this video

Explores how instrumental variables correct biases from measurement error and reverse causality in regression models.

Estimating Parameters: Confidence Intervals Log in to Mediaspace to watch this video

Explores estimating parameters through confidence intervals in linear regression and statistics.

Linear Regression: Basics and Applications Log in to Mediaspace to watch this video

Explores linear regression using the method of least squares to fit data points with the equation y = ax + b.

Jacamar Data Analysis Log in to Mediaspace to watch this video

Covers jacamar data analysis, smoking data models, and challenges with log-linear models in visual impairment data.

Generalized Linear Models: Exponential Families and Model Construction Log in to Mediaspace to watch this video

Covers exponential families, model construction, and canonical link functions in Generalized Linear Models.

Social Capital in Online Networks Log in to Mediaspace to watch this video

Explores motivations behind using Facebook, Dunbar's number, social capital types, and internet's impact on social capital.

Feature Expansion: Kernels and KNN Log in to Mediaspace to watch this video

Covers feature expansion, kernels, and K-nearest neighbors, including non-linearity, SVM, and Gaussian kernels.

Venice Sea Levels Analysis Log in to Mediaspace to watch this video

Analyzes Venice sea levels from 1887 to 2017, focusing on extreme values and fitting various models to the data.

Estimators: Consistency and Efficiency Log in to Mediaspace to watch this video

Explores the criteria for good estimators, emphasizing consistency and efficiency in estimation.

Extreme Value Theory: Point Processes Log in to Mediaspace to watch this video

Covers the application of extreme value theory to point processes and the estimation of extreme events from equally-spaced time series.

Diagonalization of Matrices and Least Squares Log in to Mediaspace to watch this video

Explores diagonalization of matrices, similarity relations, and eigenvectors in linear algebra.

Page 2 of 3