A conceptual understanding of Linear Regression Analysis is a prerequisite for this article. Thus, we recommend you to visit this link if you need more clarity. Let’s begin! Evaluation Metrics of the Classical Linear Regression Model: Before understanding the metrics to assess model performance, let us understand why do we even need them? A performance … Read more
A distribution is a statistical function that describes all the possible values and likelihoods that a random variable can take within a given range. (Ref: Statistical Models in Engineering, by Gerald Hahn and Samuel Shapiro, John Wiley and Sons, 1967) Binomial distribution It gives the probability of exactly x successes in n independent trials. Here, the probability … Read more
In this blog, we have tried to explain multicollinearity, autocorrelation, and heteroscedasticity clearly. Why are there error terms in a model? You must have noticed the presence of an error term (ϵ, greek letter epsilon) in every statistical model. Below are the reasons for it: Usually, it is not possible to consider all the independent … Read more
Linear Regression Analysis is the mathematical measure of the underlying relationship between two or more variables. In this blog, we will try to decode Linear Regression. Linear Regression When we study a variable (dependent) in terms of another variable (independent) through a linear relationship between them, it is called Bivariate Linear Regression Analysis. Multiple Linear … Read more