What really matters here is how accurately the linear regression equation describes the relationship between the two variables. However, that doesn’t preclude any of us from trying to describe something simply. Many things are better described using exponential or logarithmic equations rather than linear equations. Not everything in the world is going to have a linear relationship between them. Now you can use the equation to predict new values whenever you need to. Go to the 'options' tab and be sure to check the boxes to display the equation on the chart. Select the linear trend line for the type. Next, simply right-click on any data point and select “add trend line” to bring up the regression equation dialogue box. After you have input your data into a table format, you can use the chart tool to make a scatter plot of the points. If you have a spreadsheet program such as Microsoft Excel, then creating a simple linear regression equation is a relatively easy task. When the errors are reduced to their smallest level possible, the line of ‘best fit' is created. The smaller the errors are, the more accurate the equation is and the better it is at predicting unknown values. The distance between any point (observed or measured value) and the line (predicted value) is called the error.
As you can see, the line does not actually pass through all of the points. The image at right shows a set of data points and a “best fit” line that is the result of a regression analysis. A scatter plot and regression equation of age versus cat ownership
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