Day 1 – Basic understanding of Random numbers & Probability distributions, Hypothesis testing, Central Limit Theorem all using examples in Excel, Monte Carlo Simulation, Variance Reduction Techniques.
Day 2 – Day 2 – Modeling Statistical Distributions – Normal, Lognormal, Poisson, Exponential, Weibull, Gamma & Beta Distribution, Fitting distributions to data: Parameter estimation – MLE, MME, Moment Matching & Quantile Matching, Examining goodness of fit – Chie Sq, KS, AD, QQ Plot.
Day 3 – Regression using Ordinary Least Square & Maximum likelihood estimation methods, types of Regression (Normal Regression, Dummy Variable Regression, Logistic Regression).
Day 4 – Checking assumptions of Gauss Markov Theorem – Problems of Multi-Collinearity, Hateroskedasticity, Auto Correlation & Omitted Variable Bias.
Day 5 – Basic Understanding of components of Time Series (Trend, Seasonality, Cyclicality & White Noise), Checking Co variance Stationarity, Modelling Trend using Regression, Modelling Seasonality using dummy variable Regression.
Day 6 – Unit Root testing & Dikki Fuller test, Auto Correlation Functions (ACF) & Partial Auto Correlation Functions, Auto Regression and Moving Average Models, Checking for White Noise using Ljung Box Test, Checking fitness of Model using AIC, BIC & RMSE.