Confidence Intervals: Gaussian Estimation

Related lectures (32)

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

Explains margins of error, coverage, and pivots in constructing confidence intervals for scalar parameters.

Explores the MSE quality measure for estimators and the bias-variance tradeoff.

Covers the fundamental concepts of statistics and their application in data science.

Covers the Central Limit Theorem, confidence intervals, hypothesis testing, and qualities of estimators.

Explores point estimation methods like MOM and MLE, discussing bias, variance, and examples.

Explores Maximum Likelihood Estimation, covering assumptions, properties, distribution, shrinkage estimation, and loss functions.

Explores estimation measures of performance, including the Cramér-Rao bound and maximum likelihood estimation.

Explores type I and type II errors, critical values, and confidence intervals in statistical significance.

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

Covers the construction of confidence intervals for a normal distribution with unknown mean and variance.

Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.

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