Lecture
Probability and Statistics II: Estimation and Hypothesis Testing
In course
DEMO: velit exercitation laborum
Do minim consectetur labore sunt non nisi ad. Qui qui tempor exercitation amet ad amet. Laborum duis anim adipisicing qui enim anim eiusmod excepteur ad consequat aliquip tempor sit. Ad nostrud ipsum in ipsum et eu est voluptate tempor laboris nisi cillum occaecat. Sint duis quis voluptate cillum. Ipsum eiusmod anim tempor aliquip excepteur est eu est elit occaecat et.
Description
This lecture covers the Central Limit Theorem, confidence intervals, hypothesis testing for population mean, point estimation, qualities of estimators like bias and variance, and efficiency comparison of estimators. It emphasizes the importance of unbiased estimators.
Instructor
ad occaecat fugiat
Lorem aliquip ad officia elit culpa tempor do minim. Pariatur eiusmod excepteur quis veniam incididunt ea. Cupidatat aute elit nisi ex. Aute culpa aute officia pariatur do laborum consequat ad velit incididunt culpa ipsum aliquip commodo. Quis aute eiusmod ut exercitation sit tempor excepteur elit irure proident elit laboris mollit qui. Excepteur magna aliquip exercitation in dolore culpa adipisicing Lorem id veniam in eiusmod officia ut. Sunt id veniam irure mollit.
Official source
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.
Ontological neighbourhood
Related lectures (34)
Inference and Mixed Models
Covers point estimation, confidence intervals, and hypothesis testing for smooth functions using mixed models and spline smoothing.
Statistical Inference
Covers likelihood ratio statistic, confidence intervals, and hypothesis testing concepts.
Estimators and Confidence Intervals
Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.
Confidence Intervals: Definition and Estimation
Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.
Statistical Theory: Inference and Optimality
Explores constructing confidence regions, inverting hypothesis tests, and the pivotal method, emphasizing the importance of likelihood methods in statistical inference.