When there are more than one independent variables in the model, then the linear model is termed as the multiple linear regression model The linear model
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Linear Regression Once we've acquired data with multiple variables, one very important question is how the variables are related For example, we could ask
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SIMPLE LINEAR REGRESSION x is coefficient Often the “1” subscript in β1 is replaced by the name of the explanatory variable or some abbreviation of it
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When there are more than one independent variables in the model, then the linear model is termed as the multiple linear regression model The linear model
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This discrepancy is usually referred to as the residual Note that the linear regression equation is a mathematical model describing the relationship between X and
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The Simple Linear Regression Model The simplest deterministic mathematical relationship between two variables x and y is a linear relationship: y = β0 + β1 x
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Linear Regression model: ❑ Mean of Y is a straight line function of X, plus an error term or residual ❑ Goal is to find the best fit line that minimizes the sum of
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Multiple regression models thus describe how a single response variable Y depends linearly on a number of predictor variables Examples: • The selling price of a
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The simple linear Regression Model • Correlation coefficient is non-parametric and just indicates that two variables are associated with one another, but it does
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