Mathematics · Statistics
Matthews Correlation from Grouped Terms Calculator
Calculate matthews correlation coefficient from confusion-matrix determinant numerator and positive geometric denominator.
Inputs and results stay in this browser. Change one value at a time to explore the relationship.
Calculation steps
- Use c=a/b with confusion-matrix determinant numerator=1800 and positive geometric denominator=2400.
- Matthews correlation coefficient=0.75.
Understand Matthews Correlation from Grouped Terms
One idea, three depths
Choose how deeply to explain Matthews Correlation from Grouped Terms
Matthews Correlation from Grouped Terms: Calculate matthews correlation coefficient from confusion-matrix determinant numerator and positive geometric denominator.
Age 5Explain it to a 5-year-oldStart with a picture
Imagine using Matthews Correlation from Grouped Terms to answer this question: calculate matthews correlation coefficient from confusion-matrix determinant numerator and positive geometric denominator? Enter confusion-matrix determinant numerator and positive geometric denominator; the calculator shows Matthews correlation coefficient. For example: confusion-matrix determinant numerator=1800 and positive geometric denominator=2400 produce Matthews correlation coefficient=0.75. The answer tells you Matthews correlation coefficient.
Age 15Explain it to a 15-year-oldConnect it to the formula
The Matthews correlation coefficient is a confusion-matrix determinant divided by the geometric mean of four marginal products. This page evaluates the relationship directly. The rule is c=a/b. Its input values are confusion-matrix determinant numerator, positive geometric denominator, and the main result is Matthews correlation coefficient. For example: confusion-matrix determinant numerator=1800 and positive geometric denominator=2400 produce Matthews correlation coefficient=0.75.
CollegeExplain it at college levelState the model precisely
This calculator evaluates the stated matthews correlation from grouped terms relation over the valid real-number domain stated below. The implemented relation is c=a/b, evaluated from confusion-matrix determinant numerator, positive geometric denominator to produce Matthews correlation coefficient. The Matthews correlation coefficient is a confusion-matrix determinant divided by the geometric mean of four marginal products. This page evaluates the relationship directly. This calculator expects the numerator and denominator already grouped from the same confusion matrix.
Inputs and valid domain
- confusion-matrix determinant numerator must be a finite real number.
- positive geometric denominator must be a finite real number.
Important boundary: This calculator expects the numerator and denominator already grouped from the same confusion matrix.
The formula
c=a/b
How the calculator works through it
It substitutes confusion-matrix determinant numerator, positive geometric denominator into the formula and exposes every numerical step above. The main output is Matthews correlation coefficient.
Read the result correctly
The Matthews correlation coefficient is the direct answer to “calculate matthews correlation coefficient from confusion-matrix determinant numerator and positive geometric denominator.” Read it with the units shown beside the inputs; a sign, angle, percentage or rate changes what the number means.
A worked check
confusion-matrix determinant numerator=1800 and positive geometric denominator=2400 produce Matthews correlation coefficient=0.75.
Where this model stops being reliable
This calculator expects the numerator and denominator already grouped from the same confusion matrix.
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 Matthews Correlation from Grouped Terms works. They never block the calculator, and “optional” means useful context rather than a hidden requirement.
Hard requirements
- Reading formulas and substituting values
Matthews Correlation from Grouped Terms uses c=a/b. 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 Matthews Correlation from Grouped Terms 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 Matthews Correlation from Grouped Terms formula, but it helps you judge how stable a reported result may be.
Review this foundation about 6 min
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
- Read confusion-matrix determinant numerator, positive geometric denominator.
- Evaluate the principal relationship: c=a/b.
- Return Matthews correlation coefficient and check the domain conditions described above.
Python
from math import *
def matthews_correlation_grouped_calculator(a, b) -> float:
return (a / b)
assert abs(matthews_correlation_grouped_calculator(1800, 2400) - 0.75) < 1e-6 * max(1.0, abs(0.75))
C
#include <assert.h>
#include <math.h>
double matthews_correlation_grouped_calculator(double a, double b) {
return (a / b);
}
int main(void) {
const double expected = 0.75;
const double actual = matthews_correlation_grouped_calculator(1800, 2400);
assert(fabs(actual - expected) < 1e-6 * fmax(1.0, fabs(expected)));
}
C++
#include <cassert>
#include <cmath>
#include <numbers>
double matthews_correlation_grouped_calculator(double a, double b) {
return (a / b);
}
int main() {
constexpr double expected = 0.75;
const double actual = matthews_correlation_grouped_calculator(1800, 2400);
assert(std::fabs(actual - expected) < 1e-6 * std::fmax(1.0, std::fabs(expected)));
}
Linux x86-64 assembly
x86-64 NASM · System V ABI · Linux · SSE2 with libm where required
; double matthews_correlation_grouped_calculator(double a, double b)
; Linux x86-64 NASM · System V ABI · first eight doubles in xmm0–xmm7
global matthews_correlation_grouped_calculator
section .text
matthews_correlation_grouped_calculator:
push rbp
mov rbp, rsp
sub rsp, 32
movsd [rbp-8], xmm0
movsd [rbp-16], xmm1
movsd xmm0, [rbp-8]
divsd xmm0, [rbp-16]
movsd [rbp-24], xmm0
movsd xmm0, [rbp-24]
leave
ret
MATLAB
function result = matthews_correlation_grouped_calculator(a, b)
result = (a / b);
end
Wolfram Language
ClearAll[mwCalculate];
mwCalculate[a_, b_] := (a / b);
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 textbookCite 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). Matthews Correlation from Grouped Terms Calculator. MW SysArc Tools. https://math.mwsysarc.com/statistics/matthews-correlation-grouped-calculator
MLA 9
MW SysArc. “Matthews Correlation from Grouped Terms Calculator.” MW SysArc Tools, 21 July 2026, https://math.mwsysarc.com/statistics/matthews-correlation-grouped-calculator. Accessed 31 Aug. 2026.
Chicago 17
MW SysArc. “Matthews Correlation from Grouped Terms Calculator.” MW SysArc Tools. Published July 21, 2026. Accessed August 31, 2026. https://math.mwsysarc.com/statistics/matthews-correlation-grouped-calculator.
Harvard
MW SysArc (2026) ‘Matthews Correlation from Grouped Terms Calculator’, MW SysArc Tools. Published 21 July 2026. Available at: https://math.mwsysarc.com/statistics/matthews-correlation-grouped-calculator (Accessed: 31 August 2026).
BibTeX and RIS records
BibTeX
@misc{mwsysarc_matthews_correlation_grouped_calculator_2026,
author = {{MW SysArc}},
title = {Matthews Correlation from Grouped Terms Calculator},
howpublished = {MW SysArc Tools},
year = {2026},
url = {https://math.mwsysarc.com/statistics/matthews-correlation-grouped-calculator},
note = {Published July 21, 2026; accessed August 31, 2026}
}RIS
TY - ELEC
AU - MW SysArc
TI - Matthews Correlation from Grouped Terms Calculator
T2 - MW SysArc Tools
PY - 2026
DA - 2026-07-21
Y2 - 2026-08-31
UR - https://math.mwsysarc.com/statistics/matthews-correlation-grouped-calculator
N1 - Published July 21, 2026
ER -Clear answers
Frequently asked questions
What does the Matthews Correlation from Grouped Terms do?
Calculate matthews correlation coefficient from confusion-matrix determinant numerator and positive geometric denominator.
How does the Matthews Correlation from Grouped Terms work?
The calculator applies c=a/b. The Matthews correlation coefficient is a confusion-matrix determinant divided by the geometric mean of four marginal products. This page evaluates the relationship directly.
What can I learn from the Matthews Correlation from Grouped Terms?
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 .