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Statistical Inference (2)

Course Description: For interval estimation (two populations): Confidence interval estimators, pivotal methods - Hypothesis testing: Type I error and Type II error, power of a test - Most powerful test - Neyman-Pearson Lemma - Asymptotic tests, unbiased test - Uniformly most powerful test - Monotone tests, Neyman-Pearson theory - Likelihood ratio tests - Asymptotic distributions of likelihood ratio statistics - Sequential likelihood ratio test - Bayesian hypothesis testing.
Credit hours: 3
Prerequisites: STAT126
Objectives of the course :

This course aims to familiarize students with the types of statistical error and how to estimate confidence intervals for parameters, in addition to several important statistical tests.

Course outputs :

Acquiring knowledge through learning some estimation methods and tests related to statistical inference and their applications. Enhancing the ability to use advanced methods in inferential statistics and their applications to arrive at appropriate decision-making. Acquiring cognitive skills through thinking to solve some life problems. Improving the ability to pursue advanced studies in the field of statistical inference when needed.

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  • Last updated: 2026-06-08

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