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Statistics and statistical methods play a major role in the work environment in areas such as business, science, finance, economics, engineering to mention just a few. The [course_title] gives an introduction to descriptive statistics and basic probability theory for discrete and continuous probability models. Introductory theory for estimation and for statistical hypothesis testing in the most common situations is presented. Emphasis is made on both theoretical understanding and applications.

Assessment

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.

Certification

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Course Credit: Harvard University

 

Course Curriculum

Module: 01
Lecture 1: Probability and Counting | Statistics 110 00:46:00
Lecture 2: Story Proofs, Axioms of Probability | Statistics 110 00:45:00
Lecture 3: Birthday Problem, Properties of Probability | Statistics 110 00:49:00
Lecture 4: Conditional Probability | Statistics 110 00:50:00
Lecture 5: Conditioning Continued, Law of Total Probability | Statistics 110 00:50:00
Lecture 6: Monty Hall, Simpson’s Paradox | Statistics 110 00:49:00
Lecture 7: Gambler’s Ruin and Random Variables | Statistics 110 00:52:00
Module: 02
Lecture 8: Random Variables and Their Distributions | Statistics 110 00:50:00
Lecture 9: Expectation, Indicator Random Variables, Linearity | Statistics 110 00:50:00
Lecture 10: Expectation Continued | Statistics 110 00:50:00
Lecture 11: The Poisson distribution | Statistics 110 00:43:00
Lecture 12: Discrete vs. Continuous, the Uniform | Statistics 110 00:50:00
Lecture 13: Normal distribution | Statistics 110 00:51:00
Lecture 14: Location, Scale, and LOTUS | Statistics 110 00:49:00
Module: 03
Lecture 15: Midterm Review | Statistics 110 00:38:00
Lecture 16: Exponential Distribution | Statistics 110 00:18:00
Lecture 17: Moment Generating Functions | Statistics 110 00:51:00
Lecture 18: MGFs Continued | Statistics 110 00:49:00
Lecture 19: Joint, Conditional, and Marginal Distributions | Statistics 110 00:50:00
Lecture 20: Multinomial and Cauchy | Statistics 110 00:49:00
Lecture 21: Covariance and Correlation | Statistics 110 00:49:00
Module: 04
Lecture 22: Transformations and Convolutions | Statistics 110 00:48:00
Lecture 23: Beta distribution | Statistics 110 00:50:00
Lecture 24: Gamma distribution and Poisson process | Statistics 110 00:49:00
Lecture 25: Order Statistics and Conditional Expectation | Statistics 110 00:48:00
Lecture 26: Conditional Expectation Continued | Statistics 110 00:50:00
Lecture 27: Conditional Expectation given an R.V. | Statistics 110 00:50:00
Lecture 28: Inequalities | Statistics 110 00:47:00
Module: 05
Lecture 29: Law of Large Numbers and Central Limit Theorem | Statistics 110 00:50:00
Lecture 30: Chi-Square, Student-t, Multivariate Normal | Statistics 110 00:47:00
Lecture 31: Markov Chains | Statistics 110 00:46:00
Lecture 32: Markov Chains Continued | Statistics 110 00:48:00
Lecture 33: Markov Chains Continued Further | Statistics 110 00:47:00
Lecture 34: A Look Ahead | Statistics 110 00:37:00
Joseph Blitzstein: “The Soul of Statistics” | Harvard Thinks Big 4 00:15:00
Assessment
Submit Your Assignment 00:00:00
Certification 00:00:00

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