Mathematics · Statistics

Correlation Explained-Variance Percentage Calculator

Calculate explained variance percentage from percentage scale and correlation coefficient.

Runs locally
Your numbers

Inputs and results stay in this browser. Change one value at a time to explore the relationship.

Your inputCalculatedPassed forward in chains
explained variance percentage51.84

Calculation steps

  1. Use c=ab² with percentage scale=100 and correlation coefficient=0.72.
  2. explained variance percentage=51.839999999999996.

Understand Correlation Explained-Variance Percentage

One idea, three depths

Choose how deeply to explain Correlation Explained-Variance Percentage

Correlation Explained-Variance Percentage: Calculate explained variance percentage from percentage scale and correlation coefficient.

Age 5Explain it to a 5-year-oldStart with a picture

Imagine using Correlation Explained-Variance Percentage to answer this question: calculate explained variance percentage from percentage scale and correlation coefficient? Enter percentage scale and correlation coefficient; the calculator shows explained variance percentage. For example: percentage scale=100 and correlation coefficient=0.72 produce explained variance percentage=51.839999999999996. The answer tells you explained variance percentage.

Age 15Explain it to a 15-year-oldConnect it to the formula

For simple linear regression with an intercept, correlation squared times 100 gives the explained-variance percentage. This page evaluates the relationship directly. The rule is c=ab². Its input values are percentage scale, correlation coefficient, and the main result is explained variance percentage. For example: percentage scale=100 and correlation coefficient=0.72 produce explained variance percentage=51.839999999999996.

CollegeExplain it at college levelState the model precisely

This calculator evaluates the stated correlation explained-variance percentage relation over the valid real-number domain stated below. The implemented relation is c=ab², evaluated from percentage scale, correlation coefficient to produce explained variance percentage. For simple linear regression with an intercept, correlation squared times 100 gives the explained-variance percentage. This page evaluates the relationship directly. The sign of correlation disappears on squaring, and the identity does not extend unchanged to every model.

Inputs and valid domain

  • percentage scale must be a finite real number.
  • correlation coefficient must be a finite real number.

Important boundary: The sign of correlation disappears on squaring, and the identity does not extend unchanged to every model.

The formula

c=ab²

How the calculator works through it

It substitutes percentage scale, correlation coefficient into the formula and exposes every numerical step above. The main output is explained variance percentage.

Read the result correctly

The explained variance percentage is the direct answer to “calculate explained variance percentage from percentage scale and correlation coefficient.” Read it with the units shown beside the inputs; a sign, angle, percentage or rate changes what the number means.

A worked check

percentage scale=100 and correlation coefficient=0.72 produce explained variance percentage=51.839999999999996.

Where this model stops being reliable

The sign of correlation disappears on squaring, and the identity does not extend unchanged to every model.

Learn it by changing one value

Begin with the worked example, then change one value while keeping the others fixed. Compare the new result and calculation steps to identify which part of the formula changed.

Dictionary terms behind this calculator

Before studying the codeWhat you should know firstUse the calculator immediately, or check the foundations before reading the implementation.

These foundations help you understand why Correlation Explained-Variance Percentage works. They never block the calculator, and “optional” means useful context rather than a hidden requirement.

Hard requirements

  • Reading formulas and substituting values

    Correlation Explained-Variance Percentage uses c=ab². You need to recognise what each side represents before substituting the stated inputs or rearranging the relationship.

    Review this foundation about 4 min

Strong support

  • Averages and representative values

    Representative values help you judge what the Correlation Explained-Variance Percentage inputs summarise and what the result can legitimately describe.

    Review this foundation about 5 min

Optional enrichment

  • Spread and measurement variation

    Variation is not always part of the Correlation Explained-Variance Percentage formula, but it helps you judge how stable a reported result may be.

    Review this foundation about 6 min
Learn the missing foundationsI already know these — show the code

Mathematics → algorithm → program

Implement this calculation in code

These are direct reference implementations of the calculator's principal relationship and first output. They run locally and include a small known-answer check where the language supports it.

Algorithm

  1. Read percentage scale, correlation coefficient.
  2. Evaluate the principal relationship: c=ab².
  3. Return explained variance percentage and check the domain conditions described above.
Python
            from math import *

def correlation_explained_variance_calculator(a, b) -> float:
    return (a * (b * b))

assert abs(correlation_explained_variance_calculator(100, 0.72) - 51.839999999999996) < 1e-6 * max(1.0, abs(51.839999999999996))
          
Current calculator valuesUpdates when you change an input above.
              
            
C
            #include <assert.h>
#include <math.h>

double correlation_explained_variance_calculator(double a, double b) {
    return (a * (b * b));
}

int main(void) {
    const double expected = 51.839999999999996;
    const double actual = correlation_explained_variance_calculator(100, 0.72);
    assert(fabs(actual - expected) < 1e-6 * fmax(1.0, fabs(expected)));
}
          
Current calculator valuesUpdates when you change an input above.
              
            
C++
            #include <cassert>
#include <cmath>
#include <numbers>

double correlation_explained_variance_calculator(double a, double b) {
    return (a * (b * b));
}

int main() {
    constexpr double expected = 51.839999999999996;
    const double actual = correlation_explained_variance_calculator(100, 0.72);
    assert(std::fabs(actual - expected) < 1e-6 * std::fmax(1.0, std::fabs(expected)));
}
          
Current calculator valuesUpdates when you change an input above.
              
            
Linux x86-64 assembly

x86-64 NASM · System V ABI · Linux · SSE2 with libm where required

            ; double correlation_explained_variance_calculator(double a, double b)
; Linux x86-64 NASM · System V ABI · first eight doubles in xmm0–xmm7
global correlation_explained_variance_calculator
section .text

correlation_explained_variance_calculator:
    push rbp
    mov rbp, rsp
    sub rsp, 32
    movsd [rbp-8], xmm0
    movsd [rbp-16], xmm1
    movsd xmm0, [rbp-16]
    mulsd xmm0, [rbp-16]
    movsd [rbp-32], xmm0
    movsd xmm0, [rbp-8]
    mulsd xmm0, [rbp-32]
    movsd [rbp-24], xmm0
    movsd xmm0, [rbp-24]
    leave
    ret
          
Current calculator valuesUpdates when you change an input above.
              
            
MATLAB
            function result = correlation_explained_variance_calculator(a, b)
    result = (a * (b * b));
end
          
Current calculator valuesUpdates when you change an input above.
              
            
Wolfram Language
            ClearAll[mwCalculate];
mwCalculate[a_, b_] := (a * (b * b));
          
Current calculator valuesUpdates when you change an input above.
              
            

Continue in mathematical software

The downloaded file includes your current inputs and first calculated result. It is created locally.

Floating-point answers can differ slightly by language, compiler and processor. Compare within a suitable tolerance rather than assuming every decimal representation will be identical.

Supporting sourcesAcademic referencesPrimary standards, textbooks and complete citations

Standards, reading and academic references

Use the calculator as the worked interaction, then consult the primary standards and academic textbooks listed below. MW SysArc links to the original sources; the explanation on this page is original and does not reproduce them.

Introductory Statistics 2e

Read the free OpenStax statistics textbook
Cite this book
APA 7
Illowsky, B., & Dean, S. (2023). Introductory statistics 2e. OpenStax. https://openstax.org/books/introductory-statistics-2e/pages/1-introduction
MLA 9
Illowsky, Barbara, and Susan Dean. Introductory Statistics 2e. OpenStax, 2023, https://openstax.org/books/introductory-statistics-2e/pages/1-introduction.
Chicago author-date
Illowsky, Barbara, and Susan Dean. 2023. Introductory Statistics 2e. Houston, TX: OpenStax. https://openstax.org/books/introductory-statistics-2e/pages/1-introduction.

OpenStax entries are free to read online. Follow the licence shown on each linked source before redistributing or adapting its content.

Reuse the page responsiblyCite this pageAPA, MLA, Chicago, Harvard, BibTeX and RIS

These formats cite this calculator page itself. They are separate from the academic references above, which support the mathematical method and terminology.

APA 7

MW SysArc. (2026, July 21). Correlation Explained-Variance Percentage Calculator. MW SysArc Tools. https://math.mwsysarc.com/statistics/correlation-explained-variance-calculator

MLA 9

MW SysArc. “Correlation Explained-Variance Percentage Calculator.” MW SysArc Tools, 21 July 2026, https://math.mwsysarc.com/statistics/correlation-explained-variance-calculator. Accessed 31 Aug. 2026.

Chicago 17

MW SysArc. “Correlation Explained-Variance Percentage Calculator.” MW SysArc Tools. Published July 21, 2026. Accessed August 31, 2026. https://math.mwsysarc.com/statistics/correlation-explained-variance-calculator.

Harvard

MW SysArc (2026) ‘Correlation Explained-Variance Percentage Calculator’, MW SysArc Tools. Published 21 July 2026. Available at: https://math.mwsysarc.com/statistics/correlation-explained-variance-calculator (Accessed: 31 August 2026).

BibTeX and RIS records

BibTeX

@misc{mwsysarc_correlation_explained_variance_calculator_2026,
  author = {{MW SysArc}},
  title = {Correlation Explained-Variance Percentage Calculator},
  howpublished = {MW SysArc Tools},
  year = {2026},
  url = {https://math.mwsysarc.com/statistics/correlation-explained-variance-calculator},
  note = {Published July 21, 2026; accessed August 31, 2026}
}

RIS

TY  - ELEC
AU  - MW SysArc
TI  - Correlation Explained-Variance Percentage Calculator
T2  - MW SysArc Tools
PY  - 2026
DA  - 2026-07-21
Y2  - 2026-08-31
UR  - https://math.mwsysarc.com/statistics/correlation-explained-variance-calculator
N1  - Published July 21, 2026
ER  -

Clear answers

Frequently asked questions

What does the Correlation Explained-Variance Percentage do?

Calculate explained variance percentage from percentage scale and correlation coefficient.

How does the Correlation Explained-Variance Percentage work?

The calculator applies c=ab². For simple linear regression with an intercept, correlation squared times 100 gives the explained-variance percentage. This page evaluates the relationship directly.

What can I learn from the Correlation Explained-Variance Percentage?

It connects the mathematical rule to your chosen numbers and shows each calculation step. Change one input at a time to see how the result responds.

Does MW SysArc receive or store what I enter?

No. The calculation runs locally in your browser. MW SysArc does not receive or store your calculation inputs.

How should I use the result?

Use the steps to understand the method, then verify important school or professional work using the notation and rounding rules required in your setting.

Last reviewed . Calculations tested .

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