This course is a self-contained introduction to statistics with economic applications. Topics such as elements of probability theory, sampling theory, statistical estimation, regression analysis, and hypothesis testing will be discussed in this course. It uses elementary econometrics and other applications of statistical tools to economic data. It also provides a solid foundation in probability and statistics for economists and other social scientists.
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
|Set and Probability Theory||00:10:00|
|Random Variables, Probability Mass Density Function, and Cumulative Distribution Function (Univariate Model)||00:10:00|
|Multiple Random Variables, Bivariate Distribution, Marginal Distribution, Conditional Distribution, Independence, Multivariate Distribution (Multivariate Model)||00:15:00|
|Random Variable and Random Vector Transformations (Univariate and Multivariate Models)||00:10:00|
|Special Distributions (Discrete and Continuous)||00:10:00|
|Graph Representation Special Distributions||00:10:00|
|Random Sample, Law of Large Numbers, Central Limit Theorem||00:10:00|
|Simulations Magnifying Glass||00:05:00|
|Point Estimators and Point Estimation Methods||00:15:00|
|Interval Estimation and Confidence Intervals||00:10:00|
|t-Student versus Standard Normal A Graphical View||00:02:00|
|An Applied Review||00:10:00|
|Submit Your Assignment||00:00:00|
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