Linear Regression is generally classified into two types: Simple Linear Regression Multiple Linear Regression Simple Linear Regression In Simple Linear Regression, we try to find the relationship between (input) and . This can be expressed in the form of a straight line. a single independent variable a corresponding dependent variable (output) The same equation of a line can be re-written as: represents the output or dependent variable. Y are two unknown constants that represent the intercept and coefficient (slope) repectively. β0 and β1 (Epsilon) is the error term. ε The following is a sample graph of a Simple Linear Regression Model : Some applications of Simple Linear Regression include : Predicting crop yields based on amount of rainfall : Yield is dependent variable while amount of rainfall is independent variable. Marks scored by student based on number of hours studied (ideally) : Here marks scored is dependent and number of hours studied is independent. Predicting the Salary of a person based on years of experience : Thus Experience become the independent variable while Salary becomes the dependent variable. Multiple Linear Regression In Multiple Linear Regression, we try to find relationship between and corresponding dependent variable (output). The independent variables can be continuous or categorical . 2 or more independent variables (inputs) The equation that describes how the predicted values of y is related to is called as p independent variables Multiple Linear Regression equation: Below is the graph for Multiple Linear Regression Model, applied on the data set: iris Multiple Linear Regression Analysis can help us in following ways : It helps us and . The multiple linear regression analysis can be used to get . predict trends future values point estimates It can be used to or impacts of changes. That is, multiple linear regression analysis can help to understand . forecast effects how much will the dependent variable change when we change the independent variables It can be used to . identify the strength of the effect that the independent variables have on a dependent variable

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Linear Regression and its Mathematical implementation

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