ViewTube

I am following the book by Ronald Walpole, Raymond Myers, Sharon Myers, and Keying Ye on "Probability and Statistics for Engineers and Scientists". I hope you will enjoy this playlist. If you feel something is missing then do suggest the topics in the comment section.

Lecture 1: Random experiment, Sample space, Events and its type. Concept and examples.

Lecture 1: Random experiment, Sample space, Events and its type. Concept and examples.

Lecture 2: First counting principle or multiplication rule in probability.

Lecture 2: First counting principle or multiplication rule in probability.

Lecture 3: Axiomatic approach to probability. Proof of some important properties in probability

Lecture 3: Axiomatic approach to probability. Proof of some important properties in probability

Lecture 4: Motivation to study conditional probability (see pinned comment)

Lecture 4: Motivation to study conditional probability (see pinned comment)

Lecture 5 : Independent Events | Concept, Properties and Examples | Probability course

Lecture 5 : Independent Events | Concept, Properties and Examples | Probability course

Complement in Conditional Probability | P(A' | B) = 1 - P(A | B)

Complement in Conditional Probability | P(A' | B) = 1 - P(A | B)

Lecture 6 : Total Probability | Concept and Examples | Probability course

Lecture 6 : Total Probability | Concept and Examples | Probability course

Lecture 7: Bayes' theorem | Concept and Example | Probability course

Lecture 7: Bayes' theorem | Concept and Example | Probability course

Lecture 8: Introduction to Random variable and its types.

Lecture 8: Introduction to Random variable and its types.

Lecture 9: Classify the given problems into Discrete or Continuous random variable

Lecture 9: Classify the given problems into Discrete or Continuous random variable

Lecture 10: Probability Mass function & Cumulative Distribution function | discrete random variable

Lecture 10: Probability Mass function & Cumulative Distribution function | discrete random variable

Lecture 11: An Example on Probability Mass and Cumulative Distribution function | General formula

Lecture 11: An Example on Probability Mass and Cumulative Distribution function | General formula

Lecture 12: Probability Density function and Cumulative Distribution function | Concept & Examples

Lecture 12: Probability Density function and Cumulative Distribution function | Concept & Examples

What is the probability of getting 1 at even number of trials?

What is the probability of getting 1 at even number of trials?

Lecture 13: Joint Probability Distribution and Marginal Distribution for Discrete Random Variable

Lecture 13: Joint Probability Distribution and Marginal Distribution for Discrete Random Variable

Lecture 14: Joint Probability Distribution and Marginal Distribution for Continuous Random Variable

Lecture 14: Joint Probability Distribution and Marginal Distribution for Continuous Random Variable

Lecture 15: Conditional Probability Distribution and Statistical Independence of Random Variables

Lecture 15: Conditional Probability Distribution and Statistical Independence of Random Variables

Lecture 16: Mean / Expected value of a Random variable | Concept and Examples.

Lecture 16: Mean / Expected value of a Random variable | Concept and Examples.

Lecture 17: Variance and Standard Deviation of a Random Variable | Concept and Examples.

Lecture 17: Variance and Standard Deviation of a Random Variable | Concept and Examples.

Lecture 18: Properties of Expectation of a random variable along with examples.

Lecture 18: Properties of Expectation of a random variable along with examples.

Lecture 19: Covariance of a random variable | Concept, Examples and some important properties

Lecture 19: Covariance of a random variable | Concept, Examples and some important properties

Lecture 20: Properties of Variance of a random variable along with some examples.

Lecture 20: Properties of Variance of a random variable along with some examples.

Lecture 21: Binomial Process, Trial, Random Variable and Binomial distribution.

Lecture 21: Binomial Process, Trial, Random Variable and Binomial distribution.

Lecture 22: Mean and variance of binomial distribution | Formula and Examples

Lecture 22: Mean and variance of binomial distribution | Formula and Examples

Lecture 23: Hypergeometric distribution | Concept, Examples, connection with Binomial distribution

Lecture 23: Hypergeometric distribution | Concept, Examples, connection with Binomial distribution

Puzzle number 1 | Spoiler at the end #quiz #quiztime #probability #iq

Puzzle number 1 | Spoiler at the end #quiz #quiztime #probability #iq

How to use Hypergeometric distribution online calculator? #probability #statistics #datascience

How to use Hypergeometric distribution online calculator? #probability #statistics #datascience

Lecture 24: Negative Binomial Distribution | Concept and Examples

Lecture 24: Negative Binomial Distribution | Concept and Examples

Lecture 25 : Geometric Distribution | Special case of Negative Binomial Distribution

Lecture 25 : Geometric Distribution | Special case of Negative Binomial Distribution

Lecture 26: Poisson Distribution | Concept and Examples using table

Lecture 26: Poisson Distribution | Concept and Examples using table

Lecture 27: Proof of --- Limit of binomial distribution is Poisson Distribution.

Lecture 27: Proof of --- Limit of binomial distribution is Poisson Distribution.

Puzzle number 7 #statistics #math #mathematics #datascience #machinelearning #data #distribution

Puzzle number 7 #statistics #math #mathematics #datascience #machinelearning #data #distribution

Lecture 28: Normal Distribution of a Random Variable (Part 1)

Lecture 28: Normal Distribution of a Random Variable (Part 1)

Lecture 29: Standard Normal Distribution or Z-Distribution| Conceptual lecture | Part 2

Lecture 29: Standard Normal Distribution or Z-Distribution| Conceptual lecture | Part 2

Lecture 30: Examples on Normal Distribution & Standard Normal Distribution / Z- Distribution(Part 3)

Lecture 30: Examples on Normal Distribution & Standard Normal Distribution / Z- Distribution(Part 3)

Lecture 31: Normal Approximation to Binomial via Calculus

Lecture 31: Normal Approximation to Binomial via Calculus

Lecture 32: Gamma Distribution | Concept, Mean, Variance and Examples

Lecture 32: Gamma Distribution | Concept, Mean, Variance and Examples

Conceptual lecture on Chi Squared distribution

Conceptual lecture on Chi Squared distribution

Statistic | Sampling Distribution | Population | Sample | iiDs along with examples.

Statistic | Sampling Distribution | Population | Sample | iiDs along with examples.

Understanding The Central Limit Theorem Using Examples

Understanding The Central Limit Theorem Using Examples

Problems on Central Limit Theorem: Single Sample (From Walpole and Mayers Textbook)

Problems on Central Limit Theorem: Single Sample (From Walpole and Mayers Textbook)

Sampling Distribution of Variance with the help of Chi Square Distribution

Sampling Distribution of Variance with the help of Chi Square Distribution

Difference between Sample means using central limit theorem

Difference between Sample means using central limit theorem

Introduction to t-distribution and its connection with Z-distribution

Introduction to t-distribution and its connection with Z-distribution

Introduction to F-distribution and when to use it along with examples

Introduction to F-distribution and when to use it along with examples