AVEDEV
The AVEDEV function calculates the average of the absolute deviations of data points from their mean. It helps analyze the variability or dispersion of a dataset. This function provides insights into the data's consistency.
Example explanation
Cell B7 uses the AVEDEV function to calculate the average deviation of the scores from the mean, showing the variability of the scores.
Syntax 🔗
=AVEDEV(number1, [number2,...])
number1 | The first number or array of numbers whose average deviation is to be calculated. |
number2 | ... (Optional), Additional numbers or arrays of numbers for which the average deviation is to be calculated. |
About AVEDEV 🔗
The AVEDEV function calculates the average of the absolute deviations of data points from their mean. It helps you understand the variability and consistency of your dataset. Use AVEDEV to assess how much data points differ from the dataset's central value, which can reveal insights about the data's reliability and any potential outliers or significant variations.
Examples 🔗
To calculate the average deviation of monthly sales figures like 250, 400, 300, 600, and 350 from their mean, use the AVEDEV formula: =AVEDEV(250, 400, 300, 600, 350). This formula returns the average absolute deviation, which helps you understand the variability of your sales data.
Notes 🔗
Use the AVEDEV function to find the average of the absolute deviations of your data points from their mean. This function treats the input data as a sample from a larger population. Make sure the arguments you provide accurately represent the dataset for which you want to calculate the average deviation.
Questions 🔗
The result of the AVEDEV function signifies the average absolute deviation of the data points from their mean. It provides valuable insights into the variability, dispersion, and consistency of the dataset, allowing users to assess the degree to which data points deviate from the central value.
How does the AVEDEV function handle negative deviations?The AVEDEV function considers the absolute deviations of data points from the mean, effectively disregarding the negative or positive nature of the deviation. By computing the average of absolute deviations, it offers a comprehensive assessment of the overall variability in the dataset.