find linear regression equation calculator

#Find linear regression equation calculator software

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.

#Find linear regression equation calculator software

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Points very close to the line of best fit. Strong positve and negative correlations have data.The line of best that falls down quickly from left to the right is.The line of best that rises quickly from left to right is called a.Line of best fit (trend line) - A line on a scatter plot which can be drawn near the points to more clearly show Where the summations are again taken over the entire data set Given any set of n data points in the form (`x_i`, `y_i`),Īccording to this method of minimizing the sum of square errors, the line of best fit is obtained when In this particular equation, the constant m determines the slope or gradient of that line, and the constant term 'b' determines the point at which the line crosses the y-axis, The origin of the name 'e linear'e comes from the fact that the set of solutions of such an equation forms a straight line in the plane. Simple linear regression is a way to describe a relationship between two variables through an equation of a straight line,Ĭalled line of best fit, that most closely models this relationship.Ī common form of a linear equation in the two variables x and y is