## Introduction

When analyzing data, it’s important to consider how spread out the data is from the average. This is where mean deviation comes in. Mean deviation measures the average distance between each value and the mean. In this article, we will show you how to calculate mean deviation in Excel.

## What is Mean Deviation?

Mean deviation, also known as average deviation, is a measure of variability in a set of data. It measures how spread out the data is from the mean by taking the average of the absolute differences between each data point and the mean.

## Why Use Excel to Calculate Mean Deviation?

Excel is a powerful tool for data analysis and can make calculations like mean deviation much easier. It allows you to quickly enter your data and perform calculations without doing all the math by hand.

### Step 1: Enter Your Data

The first step to calculating mean deviation in Excel is to enter your data into a spreadsheet. In this example, we will use a set of test scores.

Student | Test Score |
---|---|

1 | 85 |

2 | 90 |

3 | 72 |

4 | 78 |

5 | 92 |

6 | 88 |

7 | 80 |

### Step 2: Calculate the Mean

The next step is to calculate the mean, or average, of the data. To do this, use the AVERAGE function in Excel.

```
=AVERAGE(B2:B8)
```

This formula calculates the average of the test scores in cells B2 through B8.

### Step 3: Calculate the Absolute Deviations

To calculate the absolute deviations, subtract the mean from each data point and take the absolute value. Use the ABS function in Excel to take the absolute value.

```
=ABS(B2-$C$2)
```

This formula calculates the absolute deviation for the first test score in cell B2.

### Step 4: Calculate the Mean Deviation

Finally, calculate the mean deviation by taking the average of the absolute deviations.

```
=AVERAGE(D2:D8)
```

This formula calculates the mean deviation for the test scores.

## Conclusion

Calculating the mean deviation in Excel is a simple process that can help you better understand your data. By calculating the mean deviation, you can see how spread out your data is from the average. This can be useful in a variety of fields, from finance to scientific research.