Learn · Essentials of Data Science With R Software-1: Probability and Statistical Inference
Lecture 21 of 72Week 4
Lecture 21: Computation of Probability using R
Prof. Shalabh · IIT Kanpur
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- Essentials of Data Science With R Software-1: Probability and Statistical Inference, IIT Kanpur (at NPTEL)
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All the lectures
- Lecture 01: Data Science- Why, What, and How?
- Lecture 02: Installation and Working with R
- Lecture 03: Installation and Working with R Studio
- Lecture 04: Calculations with R as a Calculator
- Lecture 05: Calculations with Data Vectors
- Lecture 06: Built-in Commands and Bivariate Plots
- Lecture 07: Logical Operators and Selection of Sample
- Lecture 8: Introduction to Probability
- Lecture 9: Sample Space and Events
- Lecture 10: Set Theory and Events using Venn Diagrams
- Lecture 11: Relative Frequency and Probability
- Lecture 12: Probability and Relative Frequency - An Example
- Lecture 13: Axiomatic Definition of Probability
- Lecture 14: Some Rules of Probability
- Lecture 15: Basic Principles of Counting- Ordered Set, Unordered Set, and Permutations
- Lecture 16: Basic Principles of Counting- Combination
- Lecture 17: Conditional Probability
- Lecture 18: Multiplication Theorem of Probability
- Lecture 19: Bayes' Theorem
- Lecture 20: Independent Events
- Lecture 21: Computation of Probability using R
- Lecture 22: Random Variables - Discrete and Continuous
- Lecture 23: Cumulative Distribution and Probability Density Function
- Lecture 24: Discrete Random Variables, Probability Mass Function and Cumulative Distribution Function
- Lecture 25: Expectation of Variables
- Lecture 26: Moments and Variance
- Lecture 27: Data Based Moments and Variance in R Software
- Lecture 28: Skewness and Kurtosis
- Lecture 29: Quantiles and Tschebyschev's Inequality
- Lecture 30: Degenerate and Discrete Uniform Distributions
- Lecture 31: Discrete Uniform Distribution in R
- Lecture 32: Bernoulli and Binomial Distribution
- Lecture 33: Binomial Distribution in R
- Lecture 34: Poisson Distribution
- Lecture 35: Poisson Distribution in R
- Lecture 36: Geometric Distribution
- Lecture 37: Geometric Distribution in R
- Lecture 38: Continuous Random Variables and Uniform Distribution
- Lecture 39: Normal Distribution
- Lecture 40: Normal Distribution in R
- Lecture 41: Normal Distribution - More Results
- Lecture 42: Exponential Distribution
- Lecture 43: Bivariate Probability Distribution for Discrete Random Variables
- Lecture 44: Bivariate Probability Distribution in R Software
- Lecture 45: Bivariate Probability Distribution for Continuous Random Variables
- Lecture 46: Examples in Bivariate Probability Distribution Functions
- Lecture 47: Covariance and Correlation
- Lecture 48: Covariance and Correlation - Examples and R Software
- Lecture 49: Bivariate Normal Distribution
- Lecture 50: Chi square Distribution
- Lecture 51: t - Distribution
- Lecture 52: F - Distribution
- Lecture 53: Distribution of Sample Mean, Convergence in Probability and Weak Law of Large Numbers
- Lecture 54: Central Limit Theorem
- Lecture 55: Needs for Drawing Statistical Inferences
- Lecture 56: Unbiased Estimators
- Lecture 57: Efficiency of Estimators
- Lecture 58: Cramér-Rao Lower Bound and Efficiency of Estimators
- Lecture 59: Consistency and Sufficiency of Estimators
- Lecture 60: Method of Moments
- Lecture 61: Method of Maximum Likelihood and Rao Blackwell Theorem
- Lecture 62: Basic Concepts of Confidence Interval Estimation
- Lecture 63: Confidence Interval for Mean in One Sample with Known Variance
- Lecture 64: Confidence Interval for Mean and Variance
- Lecture 65: Basics of Tests of Hypothesis and Decision Rules
- Lecture 66: Test Procedures for One Sample Test for Mean with Known Variance
- Lecture 67: One Sample Test for Mean with Unknown Variance
- Lecture 68: Two Sample Test for Mean with Known and Unknown Variances
- Lecture 69: Test of Hypothesis for Variance in One and Two Samples
- Live Session 01-04-2021
- Live Session 03-12-2022
- Live Session 08-03-2024