How to Choose the Right Distribution? Binomial vs Poisson vs Negative Binomial vs Hypergeometric скачать в хорошем качестве

How to Choose the Right Distribution? Binomial vs Poisson vs Negative Binomial vs Hypergeometric

How to Choose the Right Distribution? Binomial vs Poisson vs Negative Binomial vs Hypergeometric Linkedin   / pratap-padhi   Website https://smearseducation.com/ Join my FREE Skool Community to get all updates and support https://www.skool.com/sme-education-9... Watch my previous recordinds on CS2 Time Series 👉    • Master Time Series Forecasting:Guide to AR...   CS2 Risk Modelling and Survival Analysis 👉    • What is a Stochastic Process? Easy explana...   CS1 Previous recorded videos watch 👉    • What are discrete random variables? |Class...   CM1 Previous recorded videos watch 👉    • How to calculate simple interest | Fundame...   TIMESTAMPS 00:00 Course intro and who this class is for 01:17 Why discrete distributions matter in CS1 02:04 What is a binomial experiment 04:08 Binomial PMF explained step by step 07:01 Cricket match example for binomial 11:05 Binomial vs Poisson. Key connection 13:10 Mean and variance comparison 18:19 Rare events and Poisson intuition 20:14 Vaccine reaction example 24:03 Pure Poisson use cases 29:03 Binomial vs Hypergeometric 31:50 Estimating probability vs given probability 35:13 Balls in a box example 37:18 With replacement vs without replacement 39:02 Hypergeometric formula intuition 45:09 Exact vs approximate solutions 47:44 Binomial vs Negative Binomial 48:05 Geometric distribution explained 55:23 Negative binomial intuition 01:00:20 Discrete uniform distribution 01:01:50 Class wrap-up VIDEO DESCRIPTION This class covers all major discrete probability distributions required for CS1 and foundational statistics.You learn binomial, Poisson, hypergeometric, geometric, negative binomial, and discrete uniform distributions in one connected flow. The focus is not memorising formulas. The focus is choosing the right distribution and understanding why. You will clearly see • When binomial works and when it fails • Why Poisson appears when events are rare • Why hypergeometric replaces binomial without replacement • How geometric and negative binomial reverse the binomial question • How all distributions connect through assumptions Real-life intuition is used throughout. This class is ideal for • CS1 Actuarial Science students • Statistics students • Data science beginners • Anyone struggling to select distributions correctly After this class, you will know • What the random variable is in each case • What is fixed and what is random • Which assumptions matter • How exam questions are framed • How applied problems differ from theory Many students know formulas but struggle with one key question. Which distribution should I apply and why. The session starts by setting the right foundation. What a random variable means in real problems. What is fixed and what is random. Why assumptions matter in statistics, especially in actuarial exams. You then move step by step through all major discrete distributions. Binomial distribution is explained from first principles. Repeated trials. Constant probability. Independence. Success and failure. From binomial, the class naturally moves to Poisson distribution. You learn why Poisson appears when events are rare. Why n becomes very large and p becomes very small. How lambda connects binomial and Poisson through np. Real-life examples like vaccine reactions, printing errors, and customer arrivals make this intuitive. Next, the class explains why binomial sometimes fails. Sampling without replacement changes everything. This leads to hypergeometric distribution. You then see how exam questions hide these assumptions and how to detect them correctly. The class then moves to geometric and negative binomial distributions. Here, the perspective changes. Instead of fixing trials and counting successes, you fix the number of successes and count how many trials are needed. Examples like exams, repeated attempts, wickets in cricket, and searching for a specific item make the logic clear. Both types of geometric interpretation are explained so you avoid common confusion. Finally, the session briefly covers discrete uniform distribution and how it differs from other discrete models. By the end of this class, you will clearly understand • How all discrete distributions are connected • How one distribution transforms into another • How exam questions test assumptions, not formulas • How to confidently choose the correct model This class is designed for • CS1 Actuarial Science students • Statistics students • Data science beginners • Anyone struggling with probability distributions #CS1 #ActuarialScience #probabilitydistribution #binomialdistribution #PoissonDistribution #HypergeometricDistribution #NegativeBinomial #GeometricDistribution #StatisticsBasics #DataScienceFoundations