Regression Analysis: Linear Predictors

This lecture covers regression analysis, focusing on linear predictors and their role in approximating outcomes. Topics include assumptions for regression modeling, interpreting coefficients, transformations of predictors and outcomes, and the use of generalized linear models. The instructor emphasizes the importance of mean-centering predictors, standardization via z-scores, and logarithmic outcomes. Beyond linear regression, the lecture introduces generalized linear models and discusses logistic regression, Poisson regression, and causal inference techniques like 'Difference in Differences'. The session concludes with a summary highlighting the advantages of using linear regression for comparing means and the need for appropriate model specification.

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related lectures (385)

Related concepts (82)

CS-401: Applied data analysis

This course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the dat

Linear regression

In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.

Regression analysis

In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome' or 'response' variable, or a 'label' in machine learning parlance) and one or more independent variables (often called 'predictors', 'covariates', 'explanatory variables' or 'features'). The most common form of regression analysis is linear regression, in which one finds the line (or a more complex linear combination) that most closely fits the data according to a specific mathematical criterion.

Logistic regression

In statistics, the logistic model (or logit model) is a statistical model that models the probability of an event taking place by having the log-odds for the event be a linear combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (the coefficients in the linear combination).

Segmented regression

Segmented regression, also known as piecewise regression or broken-stick regression, is a method in regression analysis in which the independent variable is partitioned into intervals and a separate line segment is fit to each interval. Segmented regression analysis can also be performed on multivariate data by partitioning the various independent variables. Segmented regression is useful when the independent variables, clustered into different groups, exhibit different relationships between the variables in these regions.

Nonlinear regression

In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables. The data are fitted by a method of successive approximations. In nonlinear regression, a statistical model of the form, relates a vector of independent variables, , and its associated observed dependent variables, . The function is nonlinear in the components of the vector of parameters , but otherwise arbitrary.

Regression Analysis: Disentangling Data CS-401: Applied data analysis

Covers regression analysis for disentangling data using linear regression modeling, transformations, interpretations of coefficients, and generalized linear models.

Logistic Regression: Vegetation Prediction MGT-448: Statistical inference and machine learning

Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.

Supervised Learning: Linear Regression BIO-322: Introduction to machine learning for bioengineers

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

Linear Regression and Logistic Regression ME-390: Foundations of artificial intelligence

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Regression: Linear Models MATH-413: Statistics for data science

Explores linear regression, least squares, residuals, and confidence intervals in regression models.