Explore our extensive collection of courses designed to help you master various subjects and skills. Whether you're a beginner or an advanced learner, there's something here for everyone.
Join us for our free workshops, webinars, and other events to learn more about our programs and get started on your journey to becoming a developer.
For all the self-taught geeks out there, here is our content library with most of the learning materials we have produced throughout the years.
It makes sense to start learning by reading and watching videos about fundamentals and how things work.
Data Science and Machine Learning - 16 wks
Full-Stack Software Developer - 16w
Search from all Lessons
Curated list of small interactive and incremental exercises you can take to get better at any coding skill.
Curated section of projects to build while learning with simple instructions, videos, solutions, and more.
Guides on different topics related to the technologies that we teach in our courses
Linear regression is a type of model used to predict the value of a dependent variable (or target variable) based on the value of one or more independent variables (or predictor variables). Linear regression assumes that there is a direct linear relationship between the independent variables and the dependent variable. If the relationship between the target variable is with a single predictor variable, the regression is said to be simple. If it is with several predictors, it is called multiple regression.
This model is based on five assumptions, which are as follows:
Simple linear regression allows us to study the relationships between two continuous numerical variables. In this type of regression we try to fit a straight line to the data that best describes the relationship between the two variables. That is, we look for a line that minimizes the vertical distance between itself and all the data points, so that the best linear relationship occurs when all the points form the line and there is no dispersion.
The equation that defines this relationship (straight line) is:
The goal of simple linear regression is, therefore, to find the best values of and that minimize the error . Once we have found these values, we can use them to predict the values of given any .
In linear regression, each dependent value has a single corresponding independent variable.
Multiple linear regression is an extension of simple linear regression used when there is more than one independent variable. It is used to model the relationship between two or more characteristics and a response by fitting a linear equation (more extended than the above) to the observed data.
The basic form of a multiple linear regression equation with
n variables is:
Multiple linear regression allows the analyst to determine which particular variables have a significant impact on the dependent variable and by what magnitude. Like simple linear regression, this regression makes assumptions of linearity, normality, homoscedasticity, and absence of multicollinearity, and the results may be unreliable if these assumptions are violated.