# InfoCoBuild

## Probability and Statistics

Probability and Statistics. Instructor: Prof. Somesh Kumar, Department of Mathematics, IIT Kharagpur. The use of statistical reasoning and methodology is indispensable in modern world. It is true for any discipline, be it physical sciences, engineering and technology, economics or social sciences. Much of the advanced research in biology, genetics, and information science relies increasingly on use of statistical tools. It is essential for the students to get acquainted with the subject of probability and statistics at an early stage. The present course has been designed to introduce the subject to undergraduate/postgraduate students in science and engineering. The course contains a good introduction to each topic and an advance treatment of theory at a fairly understandable level to the students at this stage. Each concept has been explained through examples and application oriented problems. (from nptel.ac.in)

 Lecture 48 - F-Distribution

Go to the Course Home or watch other lectures:

 Lecture 01 - Sets, Classes, Collections Lecture 02 - Sequence of Sets Lecture 03 - Rings and Fields, and their Properties Lecture 04 - Sigma-Rings, Sigma-Fields, Monotone Classes Lecture 05 - Random Experiments, Events Lecture 06 - Definitions of Probability Lecture 07 - Properties of Probability Function I: Addition Rule and Continuity Lecture 08 - Properties of Probability Function II: Bonferroni and Boole's Inequalities Lecture 09 - Conditional Probability Lecture 10 - Independence of Events Lecture 11 - Problems in Probability I Lecture 12 - Problems in Probability II Lecture 13 - Random Variables Lecture 14 - Probability Distribution of a Random Variable I Lecture 15 - Probability Distribution of a Random Variable II Lecture 16 - Moments/ Mathematical Expectation Lecture 17 - Characteristics of Distributions I Lecture 18 - Characteristics of Distributions II Lecture 19 - Special Discrete Distributions I Lecture 20 - Special Discrete Distributions II Lecture 21 - Special Discrete Distributions III Lecture 22 - Poisson Process I Lecture 23 - Poisson Process II Lecture 24 - Special Continuous Distributions I Lecture 25 - Special Continuous Distributions II Lecture 26 - Special Continuous Distributions III Lecture 27 - Special Continuous Distributions IV Lecture 28 - Special Continuous Distributions V Lecture 29 - Normal Distribution Lecture 30 - Problems on Normal Distribution Lecture 31 - Problems on Special Distributions I Lecture 32 - Problems on Special Distributions II Lecture 33 - Function of a Random Variable I Lecture 34 - Function of a Random Variable II Lecture 35 - Joint Distributions I Lecture 36 - Joint Distributions II Lecture 37 - Independence of Random Variables, Product Moments Lecture 38 - Linearity Property of Correlation and Examples Lecture 39 - Bivariate Normal Distribution I Lecture 40 - Bivariate Normal Distribution II Lecture 41 - Additive Properties of Distributions I Lecture 42 - Additive Properties of Distributions II Lecture 43 - Transformation of Random Variables Lecture 44 - Distribution of Order Statistics Lecture 45 - Basic Concepts of Sampling Distributions Lecture 46 - Chi-Square Distribution Lecture 47 - Chi-Square Distribution (cont.), t-Distribution Lecture 48 - F-Distribution Lecture 49 - Descriptive Statistics I Lecture 50 - Descriptive Statistics II Lecture 51 - Descriptive Statistics III Lecture 52 - Descriptive Statistics IV Lecture 53 - Introduction to Estimation Lecture 54 - Unbiased and Consistent Estimators Lecture 55 - Least Squares Estimation (LSE), Method of Moments Estimator (MME) Lecture 56 - Examples on MME, Method of Maximum Likelihood Estimation (MLE) Lecture 57 - Examples on MLE I Lecture 58 - Examples on MLE II, Mean Square Error (MSE) Lecture 59 - Uniformly Minimum-Variance Unbiased Estimator (UMVUE), Sufficiency, Completeness Lecture 60 - Rao-Blackwell Theorem and its Applications Lecture 61 - Confidence Intervals I Lecture 62 - Confidence Intervals II Lecture 63 - Confidence Intervals III Lecture 64 - Confidence Intervals IV Lecture 65 - Testing of Statistical Hypothesis: Basic Definitions Lecture 66 - Type I and Type II Errors Lecture 67 - Neyman-Pearson Fundamental Lemma Lecture 68 - Applications of Neyman-Pearson Lemma I Lecture 69 - Applications of Neyman-Pearson Lemma II Lecture 70 - Testing for Normal Mean Lecture 71 - Testing for Normal Variance Lecture 72 - Large Sample Test for Variance and Two Sample Problem Lecture 73 - Paired t-Test Lecture 74 - Examples Lecture 75 - Testing Equality of Proportions Lecture 76 - Chi-Square Test for Goodness Fit I Lecture 77 - Chi-Square Test for Goodness Fit II Lecture 78 - Testing for Independence in rxc Contingency Table I Lecture 79 - Testing for Independence in rxc Contingency Table II