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This course provides a broad treatment of statistics, concentrating on specific statistical techniques used in science and industry. The topics include hypothesis testing and estimation. It also includes confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression, and correlation. In addition to that, estimates by method of moments, their properties, maximum likelihood estimates, their properties, Fisher information, Rao-Cramer inequality and efficient estimates Bayes estimates.


This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.


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Course Credit: MIT

Course Curriculum

Module: 01
Introduction 00:45:00
Some Probability Distributions 00:10:00
Method of Moments 00:10:00
Maximum Likelihood Estimators 00:10:00
Consistency of MLE 00:10:00
Rao-Crámer Inequality 00:10:00
Efficient Estimators 00:10:00
Gamma Distribution 00:10:00
Prior and Posterior Distributions 00:10:00
Bayes Estimators 00:10:00
Sufficient Statistic 00:10:00
Module: 02
Jointly Sufficient Statistics 00:10:00
Minimal Jointly Sufficient Statistics 00:10:00
Estimates of Parameters of Normal Distribution 00:10:00
Orthogonal Transformation of Standard Normal Sample 00:10:00
Fisher and Student Distributions 00:10:00
Confidence Intervals for Parameters of Normal Distribution 00:10:00
Testing Hypotheses 00:10:00
Most Powerful Test for Two Simple Hypotheses 00:10:00
Randomized Most Powerful Test 00:10:00
Monotone Likelihood Ratio 00:10:00
One Sided Hypotheses (cont.) 00:10:00
Module: 03
Pearson’s Theorem 00:10:00
Goodness-of-Fit Test 00:10:00
Goodness-of-Fit Test for Composite Hypotheses 00:10:00
Test of Independence 00:10:00
Test of Homogeneity 00:10:00
Kolmogorov-Smirnov Test 00:10:00
Simple Linear Regression 00:10:00
Joint Distribution of the Estimates 00:10:00
Statistical Inference in Simple Linear Regression 00:10:00
Classification Problem 00:10:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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