Generalized Linear Models

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Related lectures (30)

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Variance and Covariance: Properties and Examples

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

Estimators and Confidence Intervals

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Review Session: Module 1

Introduces inferential statistics, covering sampling, central tendency, dispersion, histograms, z-scores, and the normal distribution.

Normal Distribution: Characteristics and Examples

Covers the characteristics and importance of the normal distribution, including examples and treatment scenarios.

Estimation and Confidence Intervals

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Probability and Statistics

Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.

Common Distributions: Moment Generating Functions

Explores common probability distributions, special distributions, and entropy concepts.

Probability and Statistics: Data Modeling and Analysis

Explores PDF forms, statistics, boxplots, density curves, and data analysis methods.

Binomial Distributions

Covers the normal distribution, inferential statistics, probability, and the binomial distribution in the context of the 'Dishonest Gambler Problem'.

Normal Distribution: Characteristics and Z-scores

Explores normal distribution characteristics, Z-scores, probability in inferential statistics, sample effects, and binomial distribution approximation.