**Outliers** in the **Regression Analysis** are a common issue. In most cases, **Outliers** are the result of experimental errors. Having this kind of **Outliers** can steer the direction of the analysis and create misconceptions about the outcome. If you are baffled to** find out the Outliers** from your** Regression Analysis** results, this article may come in handy for you. Here we discuss how you can find **Outliers** in **Regression Analysis** in Excel effectively.

## Download Practice Workbook

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## 3 Easy Ways to Find Outliers in Regression Analysis in Excel

We are going to use 3 separate ways in order to distinguish the** Outliers** from the **Regression Analysis** results in Excel. The methods will involve using various types of functions and charts. For better understanding, prior knowledge of the **Regression Analysis** and basic charts is recommended.

### 1. Applying Combined Formula

Using **the IF function** we will get the **Outliers** from our dataset. We will also use the **SQRT**, **ABS,** and **SUM** functions. We can conduct the **Regression Analysis** and extract the **Outliers **from the output.

**Steps**

- In the beginning, we need to conduct a
**Regression Analysis**of our dataset. - In order to do this, go to
**Data**>**Data analysis**.

- Then in the new dialog box named
**Data Analysis**, select the option**Regression**. - Then click
**OK.**

- In the
**Input Y Range**box, select the range of cells**C4:C15**. - Then in the
**Input X Range**box, select the range of cells**B4:B15.** - Next tick on the
**Labels**box. - Then in the
**Output options**group, select the**Output Range**and then select cell**G5**in the range box. - After then, check the
**Residuals**,**Standardize Residuals**, and**Line Fit Plots**boxes. - Click
**OK**after this.

- Then you will notice that the
**Regression Analysis**results are now presented in the Excel sheet starting from the**G5**cells.

Now we will need the **Squared Sum **value of the residuals and then we will use that value to calculate the **Standard Deviation **of the residuals.

- Select the cell
**K26,**and enter the following formula:

`=I26^2`

- Entering this formula will calculate the square of the residual at cell
**K26.**

- Drag the
**Fill Handle**to cell**K36.** - Doing this will fill the range of cells
**K26:K36**with the squared value of the**Residuals**.

- Then select the cell
**K37**and enter the following formula:

`=SUM(K26:K36)`

- Doing this will calculate the sum of the range of cells
**K26:K36**, in other words, we got the**Sum**of the**Squared Residuals(***SSE*).

- Next, to calculate the
**Standard Deviation(S)**, we select the cell**K38**and enter the following formula:

`=2*SQRT(K37/(H10-2))`

- This value is the value of the Standard Deviation of the residuals of the given dataset.
- Now we select the cell
**L26**and enter the following formula:

`=IF(ABS(I26)>$K$38,"Outlier","Not Outlier")`

- This formula will check whether the absolute value of the residuals is greater than the
**Sum**of the**Squared Residuals(**calculated in the earlier step. If the residual is greater than the*SSE*)**SSE**value, then the cell will show “**Outlier**”. Otherwise, it will show “**Not Outlier**”. - Drag the
**Fill Handle**to the cell**L36,**and doing this will indicate whether the cell value is an**Outlier**or not in the range of cells**L26:L36.** - We can observe that the first value of the range of cell
**L26:L36**is showing**Outlier**And the rest of them are**Not Outliers**.

- Next, select the cell
**D5**and enter the following formula:

`=C5+$K$38`

** **

- Then drag the
**Fill Handle**to cell**D15,**which will fill the range of cell**D5:D15**with the upper limit of the casualties**Outlier**value. Any value above the values will be considered an**Outlier**.

** **

- Similar way, select cell
**E5**and enter the following formula:

`=C5-$K$38`

- Then drag the
**Fill Handle**to cell**E5.**Doing this will fill the range of cell**E5:E15**with the lower limit of the**Outlier**value of**Casualties.**

- Then we created the graph with the values we obtained so far. Which constitutes the lower and the upper limit of the
**Outlier**values.

We can see clearly from the graph that the first value is on the verge of the upper limit. So this value is the** Outlier **here.

**Read More: ****How to Calculate Outliers in Excel (5 Easy Ways)**

### 2. Applying Inter-Quartile Range

By calculating the first and the third **Quartiles **of a dataset and then estimating the **Inter-quartile Range** between them, we can separate the **Outlier** from the dataset. We will use the combination of **QUARTILE.EXC** and **IF **functions to achieve this.

**Steps**

- In the beginning, select the cell
**F5**and enter the following formula:

`=QUARTILE.EXC(C5:C11,1)`

- In the beginning, select the cell
**F5**and enter the following formula:

`=QUARTILE.EXC(C5:C11,3)`

- In the beginning, select the cell
**F7**and enter the following formula:

`=F6-F5`

- In the beginning, select the cell
**F8**and enter the following formula:

`=F6+1.5*F7`

- In the beginning, select the cell
**F9**and enter the following formula:

`=F5-(F7*1.5)`

- Then select the cell
**D5**and enter the following formula:

`=IF(C5<G9,"Outlier","Not Outlier")`

- Then drag the
**Fill Handle**to cell**D11**.

- Doing this will fill the range of cell
**D5:D11**with text indicating whether the value is an**Outlier**or not.

When we created a scatter plot of the graph, we can easily distinguish the **Outliers** from the rest of the dataset in Excel while conducting regression analysis.

**Read More: ****How to Show Outliers in Excel Graph (2 Effective Methods)**

### 3. Using Charts to Find Outliers

Using charts is the fastest way to separate** Outliers** in **Regression Analysis** results. Although they can’t be 100 percent accurate and can vary from person to person. It can be used as a part of the primary screening process.

**Steps**

- For this method, we need to create a
**Scatter chart**. - To do this, go to the
**Insert**tab and then click on the**Scatter**in the charts group.

- Then right-click on the mouse and click on the
**Select Data**.

- After this, there will be a new dialog box named
**Select Data Source.** - In that box click on the
**Add**button.

- Then in the
**Edit Series**dialog box, click on the**Series X**values range box and select the range of cells**B5:B11**. - Then click on the
**Series Y**value range box and select the range of cells**C5:C11.** - Click
**OK**after this.

- Then you will notice that the chart in the worksheet now has the data plotted.
- In order to plot a
**Trendline**, we need to click on the plus icon on the right side of the chart. - Then choose and tick the
**Trendline**box. - There will be a
**Trendline**after then.

- Double click on the
**Trendline**to select it and then right-click on it. - From the context menu, click on the
**Format Trendline.**

- On the side panel menu, tick the
**Display Equation on chart**and the**Display R Squared value**boxes.

- The equation and the r-squared value of the equations are now presented in the chart.

- From the chart, it is quite evident the third value in the database is the
**Outlier**, as it is quite a way from the**Trendline.**

**💬 ****Note**

The above method only works when the dataset is small. Where you can scan the value and the chart quickly. In the case of a large dataset, you better opt for the second and the first method.

And this is how we find out the** Outliers** in regression analysis in Excel using the scatter chart.

**Read More: ****How to Find Outliers with Standard Deviation in Excel (with Quick Steps)**

## Conclusion

To sum it up, the question “how to find **Outliers** in **Regression Analysis Excel**‘ is answered here in 3 separate steps with elaborate explanations.

For this problem, a workbook is available for download where you can practice these methods.

Feel free to ask any questions or feedback through the comment section. Any suggestion for the betterment of the **Exceldemy** community will be highly appreciable

This is very hard to follow for setting up with your own dataset. The column values are not consistent, so it is hard to know what you should be applying across different columns. The standard deviation calculation references G10 – but there is nowhere where G10 is referenced! The formula is also not the standard deviation formula I am familiar with, which has N on the bottom. Doing the maths to work out what G10 is – it is 9 – but there is NO VALUE 9 anywhere above – so it is very confusing. I’ve tried a number of different ways to work this out, and every time, either all my points are outliers or all are not. The graph it is very easy to see that there are outliers – but this regression method is very complicated to follow!

Hello KAREN W,

First of all, we would like to apologize for the trouble. As you pointed out, there were in fact some issues with the cell referencing in the Regression mathod, luckily they have been updated.

The Exceldemy team is grateful to you for sharing your thoughts and feedback. Hopefully, now you can obtain the desired result.