The article will show you some basic methods on how to do **multiple regression analysis in Excel**. This is a very important topic in the field of statistics. It helps us to predict a dependent variable concerning one or multiple dependent variables.

In the dataset, we have some information about some cars: their **names**, **prices**, **maximum speeds **in **miles per hour**, the **peak power **their engine can produce, and the maximum **range **of distance they can travel without refilling their tank.

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## What Is Multiple Regression?

**Multiple regression** is a statistical process by which we can analyze the relationship between a **dependent variable **and several **independent variables**. The purpose of **regression **is to predict the nature of **dependent variables **with respect to corresponding **independent variables**.

## 2 Steps to Do Multiple Regression Analysis in Excel

**Step- 1: Enable the Data Analysis Tab**

The **Data Tab **does not contain the **Data Analysis** ribbon by default. To activate this, go through the procedure below.

- First, go to
**File**>>**Options**

- Then select
**Add-ins**>>**Excel Add-ins**>>**Go**

- Check
**Analysis ToolPak**in the**Add-ins available:**section and click**OK**.

After that, the **Data Analysis Ribbon **will appear in the **Data Tab**.

**Step- 2: Creating the Multiple Regression Analysis in Excel**

Here I’ll show you how to analyze **multiple regression**.

- From the
**Data**tab >> select**Data Analysis** - A
**dialog box**will show up the select**Regression**and click**OK**.

A **Regression **dialog box will appear.

- We will predict the car
**price**according to their**maximum speed**,**peak power**and**range**. - Select the
**range**of**dependent variables**(**Input Y Range**). In my case, it’s**C4:C14**. - After that, select the
**range**of**independent variables**(**Input X Range**). In my case, it’s**D4:F14**. - Check
**Labels**and select**New Worksheet Ply:**in the**Output Options**. If you want your**regression analysis**in the current sheet, put a cell reference where you want to start the**analysis**in the**Output Range**

You may choose **Residuals **if you want to do further **analysis**.

- After that, you will see the
**regression analysis**in a**new sheet**. Format the**analysis**according to your convenience.

Thus you can do **multiple regression analysis **in Excel.

**Similar Readings**

**How to Do Simple Linear Regression in Excel (4 Simple Methods)****How to Interpret Regression Results in Excel (Detailed Analysis)**

## A Brief Discussion about Multiple Regression Analysis in Excel

The **regression analysis **leaves several values of certain parameters. Let’s see what they mean.

**Regression Statistics**

In the **Regression Statistics **portion, we see values of some parameters.

**Multiple R:**This refers to the**Correlation Coefficient**that determines how strong the linear relationship among the variables is. The range of values for this**coefficient**is (-1, 1). The strength of the relationship is proportionate to the absolute value of**Multiple R**.**R Square:**It is another**Coefficient**to determine how well the**regression line**will fit. It also shows how many points fall on the regression line. In this example, the value of**R**is^{2}**86**, which is good. It implies that**86%**of the data will fit the**multiple regression line**.**Adjusted R Square:**This is the**adjusted R squared**value for the**independent variables**in the model. It is suitable for**multiple regression analysis**and so for our data. Here, the value of**Adjusted R Square**is**79**.**Standard Error:**This determines how perfect your**regression**equation will be. As we are doing a random**regression analysis**, the value of**Standard Error**here is pretty high.**Observations:**The number of observations in the dataset is**10**.

### Analysis of Variance **(**ANOVA**)**

In the **ANOVA **analysis section, we also see some other **parameters**.

**df:**The ‘**degrees of freedom**’ is defined by**df**. The value of**df**here is**3**because we have**3**types of**independent variables**.**SS: SS**refers to the sum of squares. If the**Residual Sum**of the**Square**is much smaller than the**Total Sum**of**Square**, your data will fit in the**regression line**more conveniently. Here, the**Residual SS**is much smaller than**Total SS**, so we can surmise that our data may fit in the**regression line**in a better way**MS: MS**is the mean square. The value of**Regression**and**Residual**MS is**78**and**5372210.11**respectively.**F**and**Significance F:**These values determine the reliability of the**regression analysis**. If the**Significance F**is less than**05**, the**multiple**regression analysis is suitable to use. Otherwise, you may need to change your**independent variable**. In our dataset, the value of**Significance F**is**0.01**which is good for analysis.

**Regression Analysis Output**

Here, I will discuss the output of **Regression Analysis**.

**Coefficients and Others**

In this section, we get the value of **coefficients **for the **independent variables- Max. Speed**, **Peak Power **and **Range**. We can also find the following information for each **coefficient**: its **Standard Error**, **t Stat**, **P-value **and other parameters.

2. **Residual Output**

The **Residual Values **help us to understand how much the **predicted price **deviates from its actual value and the **standard **value of **residuals** that would be acceptable.

The way the prediction by **regression analysis **works is given below.

Say, we want to predict the **price** of the first car according to its **independent variables**. The **independent variables **are the **Max. Speed**, **Peak Power **and **Range **whose values are **110 miles per hour**, **600 horsepower **and **130 miles**, respectively. The corresponding **regression coefficients **are **245.43**, **38.19 **and **94.38**. The **y intercept value **is **-50885.73**. So the predicted price will be **245.43*110+38.19*600+94.38*130-50885.73≈11295**.

According to the dataset of this article, if you want to predict a car’s **price **which has a **maximum speed **of **x mph**, **peak power **of **y hp **and **range **of **z miles**, the predicted price will be **245.43*x+38.19*y+94.38*z**.

**Read More:** **How to Interpret Multiple Regression Results in Excel**

## Using Graph to Understand Multiple Linear Regression in Excel

If you want to visualize the **regression line **of your data, let’s go through the procedure below.

**Steps:**

- First, from the
**Data**tab >> Go to**Data Analysis** - A
**Data Analysis**dialog box will appear then select**Regression**. - Finally, click
**OK**.

Another **dialog box **of **Regression** will appear.

- Select
**Residual**and**Line Fit Plots**. - Click
**OK**.

After that, you will see the graph of** the regression line fits **according to **Max. Speed**, **Peak Power **and **Range** in a **new sheet** along with analysis.

Below here, it represents the **line fit **according to **Max. Speed**.

And the following image shows the **line fit **according to **Peak Power**.

The below picture represents the **line fit **according to **Range**.

Please download the workbook and see the **plots **for a better understanding.

**Read More:** **How to Do Linear Regression in Excel (4 Simple Ways)**

## Practice Section

Here, I’m giving you the dataset of this article so that you can analyze **multiple linear regression** on your own.

## Conclusion

Suffice to say, this article will help you understand how to do **multiple regression analysis **in Excel and it’s a brief description of the parameters. If you have any ideas or feedback regarding this article, please share them in the comment box. This will help me enrich my upcoming articles.