The focus of this tutorial will be on a simple linear regression. If the dependent variable is modeled as a non-linear function because the data relationships do not follow a straight line, use nonlinear regression instead. If you use two or more explanatory variables to predict the dependent variable, you deal with multiple linear regression. Simple linear regression models the relationship between a dependent variable and one independent variables using a linear function. The goal of a model is to get the smallest possible sum of squares and draw a line that comes closest to the data. It will give you an answer to this and many more questions: Which factors matter and which can be ignored How closely are these factors related to each other And how certain can you be about the predictions. But how do you know which ones are really important Run regression analysis in Excel.
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