1. What is Coefficient of Dispersion?

The Coefficient of Dispersion (COD) is a statistical measure used to quantify the relative variability or dispersion of data points in a dataset. For glass strength analysis, it helps assess the consistency and reliability of glass products.

2. How Does the Calculator Work?

The calculator uses the COD formula:

Where:

• \( Max \) — Maximum value in the dataset
• \( Min \) — Minimum value in the dataset
• \( Average \) — Average value of the dataset

Explanation: The COD provides a normalized measure of dispersion that is independent of the measurement units, making it useful for comparing variability across different datasets.

3. Importance of COD Calculation

Details: In glass manufacturing, a lower COD indicates more consistent strength properties, which is crucial for quality control and product reliability assessment.

4. Using the Calculator

Tips: Enter the maximum, minimum, and average values from your glass strength measurements. All values must be positive numbers, and the maximum must be greater than or equal to the minimum.

5. Frequently Asked Questions (FAQ)

Q1: What does a high COD value indicate?
A: A high COD value suggests greater variability in glass strength, which may indicate inconsistent manufacturing processes or material quality.

Q2: What is considered a good COD value for glass?
A: Generally, a COD value below 0.1 (10%) is considered excellent, while values above 0.2 (20%) may indicate significant variability that requires investigation.

Q3: Can COD be negative?
A: No, COD cannot be negative as it represents the range of values (max-min) divided by the average, both of which are positive in glass strength measurements.

Q4: How does COD differ from standard deviation?
A: COD is a relative measure (range divided by average), while standard deviation is an absolute measure of dispersion. COD is often easier to interpret for non-statisticians.

Q5: Should I use COD for all quality control measurements?
A: COD is most useful when you need a simple, intuitive measure of relative variability. For more sophisticated analysis, consider using additional statistical measures like standard deviation or variance.