linear regression prediction equation calculator

#LINEAR REGRESSION PREDICTION EQUATION CALCULATOR SOFTWARE#

Polynomial Regression is a form of linear regression in which the relationship between the independent variable x and dependent variable y is modeled as an nth-degree polynomial. Where y’ is the estimated target output, y is the corresponding (correct) target output, and Var is Variance, the square of the standard deviation. The best possible score is 1.0, lower values are worse. We define:Įxplained_variance_score = 1 – Var In the above example, we determine the accuracy score using Explained Variance Score. Residual Error Plot for the Multiple Linear Regression Let us consider a dataset where we have a value of response y for every feature x: Hence, we try to find a linear function that predicts the response value(y) as accurately as possible as a function of the feature or independent variable(x). dependent and independent variables are linearly related. In linear regression, we assume that the two variables i.e.

It is one of the most basic machine learning models that a machine learning enthusiast gets to know about. Simple linear regression is an approach for predicting a response using a single feature.

Multiple linear regression: This involves predicting a dependent variable based on multiple independent variables.

Simple linear regression: This involves predicting a dependent variable based on a single independent variable.

There are two main types of linear regression: As shown below, figure 1 has homoscedasticity while Figure 2 has heteroscedasticity.Īs we reach the end of this article, we discuss some applications of linear regression below.

Homoscedasticity : Homoscedasticity describes a situation in which the error term (that is, the “noise” or random disturbance in the relationship between the independent variables and the dependent variable) is the same across all values of the independent variables.

Outliers can affect the results of the analysis. Outliers are data points that are far away from the rest of the data.

No outliers: We assume that there are no outliers in the data.

You can refer here for more insight into this topic. Autocorrelation occurs when the residual errors are not independent of each other.

Little or no autocorrelation : Another assumption is that there is little or no autocorrelation in the data.

Multicollinearity occurs when the features (or independent variables) are not independent of each other.

Little or no multi-collinearity : It is assumed that there is little or no multicollinearity in the data.

#LINEAR REGRESSION PREDICTION EQUATION CALCULATOR SOFTWARE#

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Linear regression prediction equation calculator