Hey there, are you ready to dive into another exciting journey? Today's adventure spot is Regression Analysis, a powerful tool for modeling relationships between variables. Our quest will be to explore the realm of Simple Linear Regression and implement it from scratch using Python!
Imagine being able to predict future outcomes based on specific parameters — exciting, isn't it? That's the magic of Regression Analysis! Let's gear up and embark on this journey with Simple Linear Regression, a storyline featuring two main characters: dependent and independent variables.
Regression, a superstar in the world of statistics, finance, investing, and Machine Learning, is our guide to predicting future outcomes. In the grand world of regression, there are two central provinces: Simple Linear Regression and Multiple Linear Regression. Our map is marked to travel through the province of Simple Linear Regression.
To get you excited, let's take an example. Suppose you own a restaurant and want to predict your sales for the next week. You pull out your past data, relating your advertisement hours with sales details. Fancy predicting sales using advertisement data? Buckle up, as that's where we are heading!
Within the kingdom of Simple Linear Regression, there's a strong belief that the two main characters (variables x and y) share a linear relationship. It's as though they're tied together with a magical linear thread. Here's a look at their relationship script:
