Statistics for Data Science 1 - W8_L6: Discrete random variables - cumulative distribution function

Welcome to Week 8 Lecture 6 of the course "Statistics for Data Science - I" by Prof. Andrew Thangaraj. Full Course: https://study.iitm.ac.in/ds/course_pages/BSMA1002.html Video Overview This lecture introduces the Cumulative Distribution Function (CDF), a key concept in probability and statistics. We begin by revisiting the Probability Mass Function (PMF) and understanding how it defines probabilities for discrete random variables. The discussion then moves to CDFs, explaining how they represent the probability that a random variable takes on a value less than or equal to a given number. The lecture includes detailed examples, such as the number of credit cards possessed by people, and shows how to interpret and graph step-function CDFs. By the end, you’ll understand how PMFs and CDFs work together to describe probability distributions. About IIT Madras' online Bachelor of Science programme IIT Madras offers four-year BS programmes that aim to provide quality education to all, irrespective of age, educational background, or location. The BS programme has multiple levels, which provide flexibility to students to exit at any of these levels. Depending on the courses completed and credits earned, the learner can receive a Foundation Certificate from IITM CODE (Centre for Outreach and Digital Education), Diploma(s) from IIT Madras, or BSc/BS Degrees from IIT Madras. For more details, Visit: https://www.iitm.ac.in/academics/study-at-iitm/non-campus-bs-programmes #CumulativeDistributionFunction #CDF #ProbabilityMassFunction #PMF #Probability #Statistics #RandomVariable #StepFunction #DataAnalysis #Lecture #Tutorial #Distribution #ProbabilityDistribution #DiscreteRandomVariable #StatisticalLearning #DataScience #QuantitativeMethods