Regression is a statistical measure that attempts to determine the strength of the relationship between one dependent variable (usually denoted by Y) and a series of other changing variables (known as independent variables, or X). It attempts to fit a model to observed data in order to quantify the relationship between the X variables and the Y variable. The fitted model may then be used either to merely describe the relationship between the two groups of variables, or to predict new values. I hope that's not complete gibberish to you.
Open this link to look at an example: https://docs.google.com/spreadsheets/d/1JFx1Oa8K8XF6W2F6Qze9wSvLRVy1-ISU49uZuE0J9h0/pubhtml
You may have to scroll up and down by using the arrows on the keyboard in the example. I had to publish the example in Google Docs because I couldn't figure out how to upload the excel file, so if you want to try to run the regression just copy and paste it into excel.
In order to be able to run the regression in excel you'll need to download the Analysis ToolPak by clicking file, options, Add-Ins and then select Analysis ToolPak and click go. Check mark the Analysis ToolPak and click ok. This will create a new tab at the top called Data. In the Data tab at the far right select "Data Analysis" and find "Regression" in the drop down menu and click ok.
For the Input Y Range select the price label and all of the prices.
For the Input X Range select the square feet label through the land label and the 40 cells below.
Check mark the Labels options. This will make it easier to read the regression results.
Make sure New Worksheet Ply: is selected before clicking ok.
Look at that beauty above! That's the regression output.
R Squared = .619, which means that this model explains 61.9% of the price. It's the measure of fit.
The coefficient for the intercept is the alpha and the coefficients for the x variables are their betas, so the equation we would use to appraise a home is as follows:
Price = 48,348 + 33*square feet + 4,941*water front -5,585*school zone + 6,263*land
I could go on and on about all the numbers in the regression, but I think I'll stop there. If you managed to stay with me all the way through, thank you.
