Excel comes with a statistical analysis toolkit that can be found under the data tab.

Once Data Analysis has been turned on, you will find the data analysis group there.

Data Analysis Toolpak is an Excel add-in with many statistical tools.

The video below demonstrates one of the tools in the Data Analysis Toolpak, common statistical tools - linear regression.

If the model fits the data well it can be used to predict values of y that can be expected based on values of x.

While the variable being explain is called the dependent or response variable.

Models built using one explanatory variable are called simple linear regression models.

However most models will try multiple variables to better explain the response variable.

Models using more than one explanatory variable are called multiple regression models.

To create a regression equation using Excel, follow these steps: Insert a scatterplot graph into a blank space or sheet in an Excel file with your data.

You can find the scatterplot graph on the Insert ribbon in Excel 2007 and later.

Select the x-axis (horizontal) and y-axis data and click OK.

You can use the degrees of freedom to get F-critical values in a statistical table, and then compare the F-critical values to the F statistic to determine a confidence level for your model.

Right-click on any of the dots and select Add Trendline from the menu.

Select Trendline Options on the left, if necessary, then select the Display Equation on Chart and Display R-Squared Value on Chart boxes.

It tells you how well the best-fitting line actually fits the data.

Going beyond the ends of observed values is risky when using a regression equation.

Theres no guarantee that the regression line will continue to be linear because it extends before and after the data points.

Watch out for the following three things when correlating customer analytics data and using regression analysis: Range restriction: Two variables might have a low correlation because youre only measuring in a narrow range.

This can happen, for example, if you are looking at a narrow range of customers say, the ones with the highest incomes or most transactions.

For example, high school grades are correlated with college grades.

It may seem like better studying in high school leads to better grades in college.

However, its often the case that a third variable, Socio Economic Status (SES) is a better explanation of both high school and college grades.

Students in families with higher SES tend to have higher grades in high school and college than students from families with low SES.

In customer analytics an improving economy or a growing company may be the reason for increases in sales, and not your marketing campaign or feature changes.

Nonlinearity: The relationship between variables needs to be linear that is, follow a line somewhat.

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