Regression analysis is a way to find trends in data. For example, you might guess that there’s a connection between how much you eat and how much you weigh; regression analysis can help you quantify that.
Regression analysis will provide you with an equation for a graph so that you can make predictions about your data. For example, if you’ve been putting on weight over the last few years, it can predict how much you’ll weigh in ten years time if you continue to put on weight at the same rate. It will also give you a slew of statistics (including a p-value and a correlation coefficient) to tell you how accurate your model is. Most elementary stats courses cover very basic techniques, like making scatter plots and performing linear regression. However, you may come across more advanced techniques like multiple regression.
Simple linear regression is used to find out the best relationship between a single input variable (predictor, independent variable, input feature, input parameter) & output variable (predicted, dependent variable, output feature, output parameter) provided that both variables are continuous in nature. This relationship represents how an input variable is related to the output variable and how it is represented by a straight line.Let us have a look at scatter plots. Scatter diagrams or plots provides a graphical representation of the relationship of two continuous variables.
Multiple linear regression refers to a statistical technique that is used to predict the outcome of a variable based on the value of two or more variables. It is sometimes known simply as multiple regression, and it is an extension of linear regression. The variable that we want to predict is known as the dependent variable, while the variables we use to predict the value of the dependent variable are known as independent or explanatory variables.
