Watch This Once and Master GLM | The Most Important CS1 Topic for Exams and Actuarial Work скачать в хорошем качестве

Watch This Once and Master GLM | The Most Important CS1 Topic for Exams and Actuarial Work

Watch This Once and Master GLM | The Most Important CS1 Topic for Exams and Actuarial Work 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 👉    • CS1 Discrete Random Variables and Probabil...   CM1 Previous recorded videos watch 👉    • CM1 Simple Interest and Discount Explained...   Timestamps 00:00 Introduction: Forecasting at non-tabular points 01:10 When Linear Regression works and when it fails 03:20 Understanding linear relationship and prediction 06:00 Sample data vs future prediction concept 09:00 Correlation and identifying linear relationship 12:30 Why Linear Regression fails for nonlinear data 15:00 Exponential growth vs linear growth example 18:00 Introduction to Generalized Linear Models (GLM) 20:30 Linear predictor vs expected value 22:40 Link function concept explained 25:00 Poisson, Binomial, and Exponential link functions 27:00 Actuarial example: predicting unexplained absences 33:00 Linear predictor to expected value transformation 35:30 GLM example: predicting exam pass probability 42:00 Why predictions can fail if important variables missing 45:52 Linear Regression assumption: errors follow Normal distribution 47:00 Understanding true data in regression: Y as random variable 49:10 Why prediction involves new random variable 51:00 Independent vs dependent variables explained 52:00 GLM example: exponential distribution for insurance claims 54:00 Linear predictor and link function in exponential model 56:00 Maximum Likelihood Estimation concept in GLM 59:00 Log likelihood and parameter estimation 01:02:00 Why software is required to solve GLM 01:05:00 Predicting claim amount using GLM 01:13:00 Exponential family of distributions explained 01:14:00 Mean and variance derivations in exponential family 01:15:00 Poisson distribution in exponential family form 01:18:00 Normal distribution in exponential family form 01:22:00 Exponential distribution derivation 01:27:00 Gamma distribution derivation 01:30:00 Why GLM is foundation of actuarial modeling Description GLM is one of the most powerful and essential topics in CS1 and actuarial science. This class builds the concept step by step from first principles and connects linear regression, exponential family distributions, link functions, and Maximum Likelihood Estimation into one clear framework used in real actuarial work. This lecture covers the exact conceptual transition from Linear Regression to Generalized Linear Models and explains why normal distribution is insufficient for many actuarial problems such as claim counts, claim severity, waiting times, and frequency modeling. Main topics covered in this class: • True assumption in Linear Regression. Normal distribution applies to errors, not directly to Y • Why Linear Regression fails when data follows exponential, Poisson, or gamma distributions • Concept of exponential family of distributions and why GLM is built on this family • Understanding random variables Y₁, Y₂, …, Yₙ in regression modeling • Why X values are reference and Y values form the true data • Concept of predicting future observations as random variables • Linear predictor η = α + βX and its role in modeling • Expected value E(Y) and its connection with the linear predictor • Link function and its purpose in GLM • Exponential distribution modeling using GLM • Insurance example: predicting claim amount using age • Why Least Squares fails and why Maximum Likelihood Estimation is required in GLM • Writing likelihood function and log likelihood function step by step • Why solving GLM requires computational tools and software • Role of exponential family form in simplifying likelihood estimation • Understanding how GLM is used to predict future outcomes in actuarial science This class builds deep conceptual clarity required for CS1 exams and real actuarial modeling work such as pricing, reserving, frequency modeling, severity modeling, and predictive analytics. #ActuarialScience#GeneralizedLinearModel #GLM#LinearRegression#CS1 #DataScience#Statistics#MachineLearning#ExponentialFamily #PoissonRegression#GammaDistribution#InsuranceModeling#PredictiveModeling #MaximumLikelihood#RegressionAnalysis#ActuarialStudents#DataScienceEducation