1. Courses
  2. MTH262

Statistics and Probability Theory

  • Teacher
    Maryam Murtaza (Math)
  • Category
    Mathematics & Statistics
  • Course Code
    MTH262
  • Session
    Fall 2020

Course Material/Videos

Available Resources
15515 Lecture 1 - Part 1:Introduction to Statistics Video Notes
15516 Lecture 1 - Part 2: Introduction to Statistics Video Notes
15517 Lecture 1 - Part 3: Introduction to Statistics Video Notes
15518 Lecture 2 - Part 1: Presentation of Data Video Notes
15519 Lecture 2 - Part 2: Presentation of Data Video Notes
15520 Lecture 3 - Part 1: Presentation of Data Video Notes
15521 Lecture 3 - Part 2: Presentation of Data Video Notes
15522 Lecture 3 - Part 3:Presentation of Data Video Notes
15523 Lecture 4 - Part 1: Measure of central tendency Video Notes
15524 Lecture 4 - Part 2: Measure of central tendency Video Notes
15911 Lecture 5 - Part 1: Measure of central Tendency(Properties of Arithmetic) Video Notes
15912 Lecture 5 - Part 2: Measure of Central Tendency(Properties of Arithmetic Mean) Video Notes
15913 Lecture 5 - Part 3: Measure of Central Tendency(Properties of Arithmetic Mean) Video Notes
16588 Lecture 6 - Part 1: Quintiles Video Notes
16589 Lecture 6 - Part2: Quintiles Video Notes
17006 Lecture 7 - Part 1: Measure of Dispersion Video Notes
17007 Lecture 7 - Part 2: Measure of Dispersion Video Notes
17008 Lecture 8 - Part 1:Measure of Dispersion Video Notes
17009 Lecture 8 - Part 2: Measure of Dispersion Video Notes
18485 Lecture 9 - Part 1: Counting Sample Points Video Notes
18486 Lecture 9 - Part 2:Counting Sample Points Video Notes
18487 Lecture 9 - Part 3: Counting Sample Points Video Notes
18488 Lecture 10 - Part 1: Probability Video Notes
18489 Lecture 10 - Part 2:Probability Video Notes
18490 Lecture 10 - Part 3: Probability Video Notes
18491 Lecture 11 - Part 1: Joint and Marginal Probability Video Notes
18492 Lecture 11 - Part 2: Conditional Probability Video Notes
18493 Lecture 11 - Part 3: Independent and Dependent Events Video Notes
18494 Lecture 12 - Part 1: Total Law Of Probability Video Notes
18495 Lecture 12 - Part 2: Total Law of Probability Video Notes
18496 Lecture 12 - Part 3: Total Law of Probability Video Notes
19064 Lecture 13 - Part 1: Bayes Theorem Video Notes
19065 Lecture 13 - Part 2: Bayes Theorem Video Notes
19066 Lecture 13 - Part 3: Bayes Theorem Video Notes
19067 Lecture 14 - Part 1: Reliability of system Video Notes
19068 Lecture 14 - Part 2: Reliability of system Video Notes
19069 Lecture 14 - Part 3: Reliability of system Video Notes
19070 Lecture 15 - Part 1: Random Variable Video Notes
19071 Lecture 15 - Part 2: Random Variable Video Notes
19072 Lecture 15 - Part 3: Random Variable Video Notes
19073 Lecture 16 - Part 1:Expected value and variance of discrete random variable Video Notes
19074 Lecture 16 - Part 2: Continuous Random Variable Video Notes
19075 Lecture 16 - Part 3:Continuous Random Variable (Continue) Video Notes
19076 Lecture 17 - Part 1: Cumulative distribution function of continuous random variable Video Notes
19077 Lecture 17 - Part 2: Expected value and variance of continuous random variable Video Notes
19976 Lecture 18 - Part 1: Binomial Distribution Video Notes
19977 Lecture 18 - Part 2: Binomial Distribution Video Notes
19978 Lecture 18 - Part 3: Binomial Distribution Video Notes
19979 Lecture 19 - Part 1: Multinomial Distribution Video Notes
19980 Lecture 19 - Part 2: Multinomial Distribution Video Notes
19981 Lecture 20 - Part 1: Hypergeometric Distribution Video Notes
19982 Lecture 20 - Part 2: Hypergeometric Distribution Video Notes
19983 Lecture 20 - Part 3: Hypergeometric Distribution Video Notes
20635 Lecture 21 - Part 1: Multivariate Hypergeometric Distribution Video Notes
20636 Lecture 21 - Part 2: Multivariate Hypergeometric Distribution Video Notes
21867 Lecture 22 - Part 1: Negative Binomial Distribution Video Notes
21868 Lecture 22 - Part 2: Negative Binomial Distribution Video Notes
21869 Lecture 22 - Part 3: Negative Binomial Distribution Video Notes
21870 Lecture 23 - Part 1: Poisson Distribution Video Notes
21871 Lecture 23 - Part 2:Poisson Distribution Video Notes
21872 Lecture 23 - Part 3: Poisson Distribution Video Notes
21873 Lecture 24 - Part 1: Poisson Distribution Video Notes
21874 Lecture 24 - Part2: Poisson Distribution Video Notes
21875 Lecture 25 - Part 1: Uniform Distriution Video Notes
21876 Lecture 25 - Part 2: Uniform Distribution Video Notes
21877 Lecture 26 - Part 1: Normal Distribution Video Notes
21878 Lecture 26 - Part 2: Normal Distribution Video Notes
21879 Lecture 27 - Part 1: Normal Distribution Video Notes
21880 Lecture 27 - Part 2: Normal Distribution Video Notes
21881 Lecture 27 - Part 3: Normal Distribution Video Notes
21882 Lecture 28 - Part 1: Normal Distribution Video Notes
21883 Lecture 28 - Part 2:Normal Distribution Video Notes

COMSATS CourseWare, abbreviated as CCW offers open materials developed by the faculty of CUI, the country’s foremost institution of education and research. The CCW is an expression of mission of COMSATS University Islamabad which states to "extend frontiers of knowledge”, “instigating inquiry”, “benefit students, scholars and communities across the country, the Muslim Ummah and the World, at large”, “share knowledge among non-degree seeking students” and “ extend, apply and exchange knowledge between the institute and society”.

COMSATS University Islamabad, Wah Campus
Wah Cantt, Rawalpindi, Punjab

© Copyright 2025, CUI Wah Campus.