Generally, regression analysis is done for prediction purposes, such that knowing the X parameters you can assume Y parameter which is significantly close to real value.īasically there are two main types of regression : This equation simply illustrates that the value of Y is dependent on the value of X and some other facts like intercept, slope, and regression residual. Here X and Y are the two variables that we are observing.
X = independent variable(the variable that you are using to predict Y ) Y = dependent variable(the variable that you are trying to predict ) This straight line is represented by a simple formula which is also called regression equation: To determine the same relationship there is another method often used called regression which beliefs in building a straight line which best represents the relation between two variables. We examine correlation to identify the type of relationship our variables have in between and their strength which is represented by a numerical value between -1 to 1. On a simple data set, the relationship between two observations (variables) is called correlation. You must have heard about the concept of correlation. If not, WHAT ARE PYTHON PACKAGES FOR DATA SCIENCE? and IMPORTING AND EXPORTING DATA IN PYTHON WITH PANDA are a few articles on a short introduction to these libraries.īefore diving into simple and multiple linear regression let me give you some theoretical concept on simply “Regression”. Since you are into regression algorithms now you must have used these libraries for data analysis tasks before. In this tutorial, we will be working with pandas, numpy, sklearn libraries and some visualization libraries like matplotlib and seaborn.
It is common to make the additional stipulation that the ordinary least squares (OLS) method should be used: the accuracy of each predicted value is measured by its squared residual (vertical distance between the point of the data set and the fitted line), and the goal is to make the sum of these squared deviations as small as possible. The adjective simple refers to the fact that the outcome variable is related to a single predictor. That is, it concerns two-dimensional sample points with one independent variable and one dependent variable (conventionally, the x and y coordinates in a Cartesian coordinate system) and finds a linear function (a non-vertical straight line) that, as accurately as possible, predicts the dependent variable values as a function of the independent variable. In statistics, simple linear regression is a linear regression model with a single explanatory variable. Here the dependent variable (GDP growth) is presumed to be in a linear relationship with the changes in the unemployment rate. Okun's law in macroeconomics is an example of the simple linear regression.
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Linear regression model with a single explanatory variable Part of a series on
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