We use **regression analysis** when we have data from two variables from two different sources and want to build a relationship between them. **Regression analysis** provides us with a linear model that allows us to predict possible outcomes. There will be some differences between the predicted and actual values for obvious reasons. As a result, we** calculate the standard error** using the regression model, which is the average error between predicted and actual values. In this tutorial, we will show you how to calculate the standard error of regression analysis in **Excel**.

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## 4 Simple Steps to Calculate Standard Error of Regression in Excel

Assume you have a data set with an **independent variable ( X)** and a

**dependent variable (**. As you can see, they have no significant relationship. But we want to build one. As a result, we’ll use

*Y*)**to create a linear relationship between the two. We’ll calculate the standard error between the two variables using the regression analysis. We’ll go over some of the regression model’s parameters in the second half of the article to help you interpret it.**

*Regression Analysis*### Step 1: Apply Data Analysis Command to Create a Regression Model

- Firstly, go to the
tab and click on the*Data*command.*Data Analysis*

- From the
list box, select the*Data Analysis*option.*Regression* - Then, click
.*OK*

### Step 2: Insert Input and Output Range in Regression Box

- For the
, select the range*Input Y Range*with the header.*C4:C13* - Click on the
check box.*Labels*

- Select the range
for the*B4:B13*.*Input X Range*

- To get the result in the preferred location, select any cell (
) for the*B16*.*Output Range* - Finally, click
.*OK*

**Read More:** **How to Calculate Standard Error of Proportion in Excel (with Easy Steps)**

### Step 3: Find Out Standard Error

- From the regression analysis, you can obtain the value of the standard error (
).*3156471*

**Read More:** **How to Find Residual Standard Error in Excel (2 Easy Methods)**

### Step 4: Plot Regression Model Chart

- Firstly, click on the
tab.*Insert* - From the
group, select the*Charts*chart.*Scatter*

**Right-click**over one of the points.- From the options, select the
**Add trendline**option.

- Therefore, your
chart will be plotted as the image shown below.*regression analysis*

- To display the
equation, click on the*regression analysis**Display equation on Chart**Format Trendline.*

- As a result, the equation (
) of the regression analysis will appear in the chart.*y = 1.0844x + 107.21*

** Notes: **

You can calculate the difference between the predicted values and the actual values from the equation of regression analysis.

**Steps:**

- Type the formula to represent the regression analysis equation.

`=1.0844*B5 + 107.21`

- Therefore, you will get the first predicted value (
), which differs from the actual value (**129.9824**).*133*

- Use the
to auto-fill column*AutoFill Tool*.*D*

- To calculate the error, type the following formula to subtract.

`=C5-D5`

- Finally, auto-fill column
to find the error values.*E*

**Read More:** **How to Calculate Standard Error of Regression Slope in Excel**

## The Interpretation of Regression Analysis in Excel

**1. Standard Error**

We can see from the regression analysis equation that there is always a difference or error between the predicted and actual values. As a result, we must calculate the average deviation of the differences.

A **standard error **represents the average error between the predicted value and the actual value. We discovered ** 8.3156471** as the

**in our example regression model. It indicates that there is a difference between the predicted and actual values, which could be greater than the**

*standard error***standard error**(

**) or less than the**

*15.7464***standard error**(

**). However, our**

*4.0048***average error**will be

**, which is the**

*8.3156471***.**

*standard error*As a result, the model’s goal is to reduce the standard error. The **lower **the standard error, the more **accurate **the model.

**2. Coefficients**

The regression coefficient evaluates the responses of unknown values. In the regression equation (** y = 1.0844x + 107.21**),

**is the**

*1.0844***,**

*coefficient***is the predictor independent variable,**

*x***is the constant, and**

*107.21***is the response value for the**

*y***.**

*x*- A
predicts that the higher the coefficient, the higher the response variable. It indicates a*positive coefficient***proportional**relationship. - A
predicts that the higher the coefficient, the lower the response values. It indicates a*negative coefficient***disproportional**relationship.

**3. P-Values**

In regression analysis, ** p-values** and coefficients cooperate to inform you whether correlations in your model are statistically relevant and what those relationships are like. The

**null hypothesis**that the independent variable has no link with the dependent variable is tested using the

**p-value**for each independent variable. There is no link between changes in the independent variable and variations in the dependent variable if there is no correlation.

- Your sample data gives enough support to
**falsify**the null hypothesis for the full population if the**p-value**for a variable is**less**than your significance threshold. Your evidence supports the notion of a**non-zero correlation**. At the population level, changes in the independent variable are linked to changes in the dependent variable. - A
**p-value****larger**than the significance level, on either side, suggests that your sample has**insufficient proof**to establish that a**non-zero correlation**exists.

Because their **p-values** (** 5.787E-06**,

**) are**

*1.3E-06***less**than the

**significant value**(

**), the**

*5.787E-06***and**

*Independent Variable (X)***are**

*Intercept***statistically significant**, as seen in the regression output example.

**4. R-Squared Values**

For linear regression models, **R-squared** is a **completeness measurement**. This ratio shows the **percentage of variance** in the dependent variable that the independent factors account for when taken together. On a handy ** 0–100** percent scale,

**R-squared**quantifies the

**strength**of the connection between your model and the dependent variable.

The **R ^{2}**value is a measure of how well the regression model fits your data. The

**higher**the

**number**, the

**better**feasible the model.

## Conclusion

I hope this article has given you a tutorial about how to calculate the standard error of regression in **Excel**. All of these procedures should be learned and applied to your dataset. Take a look at the practice workbook and put these skills to the test. We’re motivated to keep making tutorials like this because of your valuable support.

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