Red bus/Blue bus paradox

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (31)

Page 2 of 4

Elements of Statistics: Probability and Random Variables

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Logistic Regression: Vegetation Prediction

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

Probability Models: Fundamentals

Introduces the basics of probability models, covering random variables, distributions, and statistical estimation.

Generalized Linear Models: A Brief Review

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

Variance and Covariance: Properties and Examples

Explores variance, covariance, and practical applications in statistics and probability.

Variational Inference and Neural Networks

Covers variational inference and neural networks for classification tasks.

Probability and Statistics

Covers probability, statistics, independence, covariance, correlation, and random variables.

Linear Models for Classification: Logistic Regression and SVM

Covers linear models for classification, focusing on logistic regression and support vector machines.

Discrete Choice Analysis

Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.

MLE Applications: Binary Choice Models

Explores the application of Maximum Likelihood Estimation in binary choice models, covering probit and logit models, latent variable representation, and specification tests.