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Related lectures (31)

Mixture Models: Simulation-based Estimation

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

Describing Data: Statistics and Hypothesis Testing

Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.

Linear Regression: Estimation and Inference

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Model Selection Criteria: AIC, BIC, Cp

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Dependence and Correlation

Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.

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.

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.

Discrete Choice Analysis

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

Describing Data: Statistics & Uncertainty

Introduces descriptive statistics, uncertainty quantification, and variable relationships, emphasizing the importance of statistical interpretation and critical analysis.

The Nested Logit Model MOOC: Selected Topics on Discrete Choice

Explores the nested logit model for discrete choice and its implications on choice behavior and parameter estimation.

Real Estate Data Analysis: Descriptive Statistics

Explores real estate data analysis through descriptive statistics, correlation, regression, and forecasting methods.

Weighted Least Squares Estimation: IRLS Algorithm

Explores the IRLS algorithm for weighted least squares estimation in GLM.

Logistic Regression: Statistical Inference and Machine Learning

Covers logistic regression, likelihood function, Newton's method, and classification error estimation.

River Hydraulics and Modeling: Semi-Distributed Approach

Explores river hydraulics, modeling, and calibration using a semi-distributed approach for accurate forecasting and water resource management.

Basics of Linear Regression Log in to Mediaspace to watch this video

Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.

Logistic Regression: Probabilistic Interpretation Log in to Mediaspace to watch this video

Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.

Maximum Likelihood Theory & Applications Log in to Mediaspace to watch this video

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

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