Introduction: Ordinary Least Squares(OLS) is a commonly used technique for linear regression analysis. OLS makes certain assumptions about the data like linearity, no multicollinearity, no autocorrelation, homoscedasticity, normal distribution of errors. Violating these assumptions may reduce […]

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## Introduction To Linear Regression(Part-2)

In the previous part of the Introduction to Linear Regression, we discussed simple linear regression. Simple linear regression is a basic model with just two variables an independent variable x, and a dependent variable y based […]

Continue readingMore Tag## Regression Evaluation Metrics

Once we build our regression model, how can we measure the goodness of fit? We have various regression evaluation metrics to measure how well our model fits the data. In this article, we will see some […]

Continue readingMore Tag## Introduction to Linear Regression

Before we venture into linear regression, let’s first try to understand what regression analysis is? What is Regression? Regression is a statistical approach used for predicting real values like the age, weight, salary, for example. In […]

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