Composite Index Calculation

Composite Index Formula:

\[ \text{Composite Index} = \sum (\text{Weight}_i \times \text{Index}_i) \]

Weight 1 (%):

Index 1 (%):

Weight 2 (%):

Index 2 (%):

Weight 3 (%):

Index 3 (%):

Composite Index:

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1. What Is Composite Index?

The Composite Index is a weighted average of multiple sub-indices that provides an overall measure of performance across different dimensions. It combines various indicators into a single comprehensive score.

2. How Does The Calculator Work?

The calculator uses the composite index formula:

\[ \text{Composite Index} = \sum (\text{Weight}_i \times \text{Index}_i) \]

Where:

\( \text{Weight}_i \) — Weight of each component as percentage
\( \text{Index}_i \) — Value of each index component as percentage
\( \sum \) — Summation of all weighted components

Explanation: Each component's weight represents its relative importance, and the index value represents its performance level. The composite index is the sum of all weighted components.

3. Importance Of Composite Index

Details: Composite indices are widely used in business, economics, and research to provide holistic assessments of complex systems, enabling comparative analysis and performance tracking over time.

4. Using The Calculator

Tips: Enter weights as percentages (0-100%) representing the relative importance of each component. Enter index values as percentages (0-100%) representing performance levels. Ensure total weights equal 100% for accurate results.

5. Frequently Asked Questions (FAQ)

Q1: What is the purpose of using weights?
A: Weights reflect the relative importance of each component in the overall index, allowing more critical factors to have greater influence on the final result.

Q2: How many components can be included?
A: While this calculator handles three components, composite indices can include any number of components as long as their weights sum to 100%.

Q3: What are common applications?
A: Used in business performance metrics, economic indicators, quality indices, sustainability scores, and multi-criteria decision analysis.

Q4: How to interpret the result?
A: Higher values indicate better overall performance. The result should be compared against benchmarks or historical values for meaningful interpretation.

Q5: What are limitations?
A: Results depend on accurate weight assignment and component selection. May oversimplify complex relationships between different factors.