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The **standard error of the estimate** is a way to measure the accuracy of the predictions made by a regression model.

Often denoted Ïƒ_{est}, it is calculated as:

**Ïƒ _{est} = âˆšÎ£(y â€“ Å·)^{2}/n**

where:

**y:**The observed value**Å·:**The predicted value**n:**The total number of observations

The standard error of the estimate gives us an idea of how well a regression model fits a dataset. In particular:

- The smaller the value, the better the fit.
- The larger the value, the worse the fit.

For a regression model that has a small standard error of the estimate, the data points will be closely packed around the estimated regression line:

Conversely, for a regression model that has a large standard error of the estimate, the data points will be more loosely scattered around the regression line:

The following example shows how to calculate and interpret the standard error of the estimate for a regression model in Excel.

**Example: Standard Error of the Estimate in Excel**

Use the following steps to calculate the standard error of the estimate for a regression model in Excel.

**Step 1: Enter the Data**

First, enter the values for the dataset:

**Step 2: Perform Linear Regression**

Next, click theÂ **Data** tab along the top ribbon. Then click theÂ **Data Analysis** option within theÂ **Analyze** group.

If you donâ€™t see this option, you need to first load the Analysis ToolPak.

In the new window that appears, clickÂ **Regression** and then clickÂ **OK**.

In the new window that appears, fill in the following information:

Once you clickÂ **OK**, the regression output will appear:

We can use the coefficients from the regression table to construct the estimated regression equation:

**Å· = 13.367 + 1.693(x)**

And we can see that the standard error of the estimate for this regression model turns out to be **6.006**. In simple terms, this tells us that the average data point fallsÂ **6.006** units from the regression line.

We can use the estimated regression equation and the standard error of the estimate to construct a 95% confidence interval for the predicted value of a certain data point.

For example, suppose x is equal to 10. Using the estimated regression equation, we would predict that y would be equal to:

Å· = 13.367 + 1.693*(10) = 30.297

And we can obtain the 95% confidence interval for this estimate by using the following formula:

- 95% C.I. = [Å· â€“ 1.96*Ïƒ
_{est}, Å· + 1.96*Ïƒ_{est}]

For our example, the 95% confidence interval would be calculated as:

- 95% C.I. = [Å· â€“ 1.96*Ïƒ
_{est}, Å· + 1.96*Ïƒ_{est}] - 95% C.I. = [30.297 â€“ 1.96*6.006, 30.297 + 1.96*6.006]
- 95% C.I. = [18.525, 42.069]

**Additional Resources**

How to Perform Simple Linear Regression in Excel

How to Perform Multiple Linear Regression in Excel

How to Create a Residual Plot in Excel