Statistics

R-squared Calculator: Coefficient of Determination

Compute statistical measures, distribution probabilities, and dataset metrics for R-squared: Coefficient of Determination with clear step-by-step solutions.

Reviewed by Sanjay Samanta & Academic Review Board
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Last updated: August 2026
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Verified Mathematical Solution

Data Inputs

Enter your data sets to calculate the R-squared value. Ensure both datasets are comma-separated and of equal length.

Comma-separated numerical values.

Comma-separated predicted numerical values.

Result

Explained Variance

R-squared Value:

The R-squared value, also known as the coefficient of determination, ranges from 0 to 1. It represents the proportion of the variance in the dependent variable that is predictable from the independent variable(s).

  • R-squared = 1: Perfect fit. The model explains all the variance in the dependent variable.
  • R-squared = 0: No fit. The model explains none of the variance.
  • 0 < R-squared < 1: Indicates the extent to which the variance in the dependent variable is predictable from the independent variable(s).
Formula: $$R^2 = 1 - \frac{SS_{res}}{SS_{tot}} = 1 - \frac{\sum_{i}(y_i - \hat{y}_i)^2}{\sum_{i}(y_i - \bar{y})^2}$$
Direct Answer & Overview
Verified Educational Guide

How to Calculate R-squared: Coefficient of Determination

Compute statistical measures, distribution probabilities, and dataset metrics for R-squared: Coefficient of Determination with clear step-by-step solutions.

Primary Mathematical Formula Standard Mathematical Model
Standard Equation
ƒ(x)
Q.E.D.
y=f(x1,x2,…,xn)y = f(x_1, x_2, \dots, x_n)
Evaluated with exact mathematical formulation • Rigorously verified
Exact Formula
Input Parameters
Required
1
Dependent Variable (y) Data (e.g., 2,4,5,7,8): Value for Dependent Variable (y) Data (e.g., 2,4,5,7,8)
Expected Outputs
Calculated
Computed R-squared Calculator: Coefficient of Determination result with step-by-step mathematical breakdown
Exact numerical value and verified formula evaluation
Worked Numerical Example
Instant Verification
Given the sample observation dataset X = [12, 15, 18, 22, 28], compute the mean (X̄), sample variance (s²), and sample standard deviation (s).
→ Sum all 5 observations: ΣX = 12 + 15 + 18 + 22 + 28 = 95. Divide by n = 5: X̄ = 95 / 5 = 19.0.; Subtract the mean from each item and square: (12-19)² = 49; (15-19)² = 16; (18-19)² = 1; (22-19)² = 9; (28-19)² = 81. Sum of squares = 156.
Mean X̄ = 19.0, Sample Variance s² = 39.0, Standard Deviation s = 6.245

What Is the R-squared Calculator: Coefficient of Determination?

Compute statistical measures, distribution probabilities, and dataset metrics for R-squared: Coefficient of Determination with clear step-by-step solutions.

Understanding R-squared

R-squared, or the coefficient of determination, is a statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model. In simpler terms, it shows how well the data points fit a regression line.

It's a crucial metric in evaluating the performance of regression models. A higher R-squared value generally indicates a better fit, suggesting that the model is effective at predicting the dependent variable. However, R-squared doesn\'t tell the whole story and should be considered alongside other metrics and domain knowledge.

For further reading, you can explore resources like:

How to Use the R-squared Calculator: Coefficient of Determination

Using this calculator is straightforward. Enter your known values into the fields below, and the solver will compute the result immediately:

• Dependent Variable (y) Data (e.g., 2,4,5,7,8)

Example input: 0.

Formula Reference
\(y = f(x_1, x_2, \dots, x_n)\)

Sample Problem: Computing Descriptive Statistics for Sample Dataset

Worked Example
Problem Statement

Given the sample observation dataset X = [12, 15, 18, 22, 28], compute the mean (X̄), sample variance (s²), and sample standard deviation (s).

1

Calculate the Sample Mean (X̄)

Sum all 5 observations: ΣX = 12 + 15 + 18 + 22 + 28 = 95. Divide by n = 5: X̄ = 95 / 5 = 19.0.

\bar{X} = \frac{12 + 15 + 18 + 22 + 28}{5} = \frac{95}{5} = 19.0
2

Compute Deviations and Squared Deviations

Subtract the mean from each item and square: (12-19)² = 49; (15-19)² = 16; (18-19)² = 1; (22-19)² = 9; (28-19)² = 81. Sum of squares = 156.

\sum (X_i - \bar{X})^2 = 49 + 16 + 1 + 9 + 81 = 156
3

Apply Bessel’s Correction for Sample Variance (n - 1)

Divide the sum of squared deviations by n - 1 = 4: s² = 156 / 4 = 39.0.

s^2 = \frac{156}{5 - 1} = 39.0
4

Calculate Sample Standard Deviation (s)

Take the square root of variance: s = √39.0 ≈ 6.245.

s = \sqrt{39.0} \approx 6.245
Final Result Mean X̄ = 19.0, Sample Variance s² = 39.0, Standard Deviation s = 6.245

How to Calculate R-squared: Coefficient of Determination Step-by-Step

Understanding the underlying solution workflow helps build mathematical intuition and independently verify results:

1
Identify and note down the given values for: Dependent Variable (y) Data (e.g., 2,4,5,7,8).
2
Set up the primary formula: \(y = f(x_1, x_2, \dots, x_n)\). Substitute the identified values into their respective positions.
3
Complete the statistical calculations (e.g., sum, mean, or computing variance and probability) from the dataset.
4
Round the final calculated answer to the required decimal accuracy or significant figures.

Real-World Applications of R-squared Calculator: Coefficient of Determination

Practical scenarios where r-squared calculator: coefficient of determination calculations are applied across engineering, business, and everyday problem solving:

Clinical Trial Hypothesis Testing

Medical researchers evaluate sample distributions, standard error, and statistical significance to prove treatment efficacy before regulatory approvals.

Six Sigma Industrial Quality Control

Manufacturing engineers monitor process standard deviation and capability indices (Cpk) to keep manufacturing defect rates below 3.4 parts per million.

Financial Portfolio Volatility & Value-at-Risk

Risk officers compute asset return variances and Z-scores to estimate potential daily capital losses under extreme market movements.

Common Pitfalls & Mistakes to Avoid

Key calculation errors to avoid when computing r-squared calculator: coefficient of determination:

Confusing Sample (N - 1) and Population (N) Standard Deviation

Use Sample Standard Deviation (Bessel’s correction with N - 1 degrees of freedom) when analyzing a sample dataset representing a broader population.

Relying Exclusively on the Mean for Heavily Skewed Data

When data contains significant outliers (e.g. household income, real estate prices), report the Median and Interquartile Range (IQR) alongside the Mean.

Misinterpreting P-Values in Hypothesis Testing

A p-value is the probability of observing results as extreme as the sample data assuming the null hypothesis is true; it is NOT the probability that the null hypothesis is true.

Key Terminology Glossary

Essential terms and definitions related to r-squared calculator: coefficient of determination:

Dependent Variable (y) Data (e.g., 2,4,5,7,8) A mathematical symbol (such as x, y, or t) representing an unknown or changeable quantity.
Standard Deviation A measure of dispersion showing how much individual data observations deviate from the sample average.
Bessel’s Correction (N - 1) The degrees-of-freedom divisor adjustment applied to remove bias when estimating population variance from samples.
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About the R-squared Calculator: Coefficient of Determination

The R-squared Calculator: Coefficient of Determination is maintained by Basic Math Tools, an educational platform committed to providing accurate STEM and financial computing tools. Every tool processes calculations transparently in your browser for privacy, instant responsiveness, and mathematical accuracy.

If you have suggestions or questions regarding mathematical formulas, please review our Editorial Policy or contact our math team.

Fact-Checked & Verified • Computational Accuracy Standards
Updated August 2026 • Editorial Policy
Authored By
Sanjay Samanta

Lead Developer & Founder of Basic Math Tools. Specializes in browser-native computational algorithms and applied mathematics.

Reviewed & Verified By
Academic Review Board

Mathematics & curriculum specialists. Audited against standard algebraic and arithmetic principles.

Found an error or have an improvement suggestion? Report a calculation issue

Frequently Asked Questions

When should I use sample vs. population standard deviation?
Use population standard deviation (σ, dividing by N) when your dataset contains every single member of the group you are studying (e.g., all 30 students in a specific classroom). Use sample standard deviation (s, dividing by n - 1, Bessel's correction) when your data represents a sample drawn from a larger unmeasured population; dividing by n - 1 eliminates statistical bias.
How do extreme outliers affect the mean compared to the median?
The arithmetic mean is sensitive to outliers because every numerical value contributes directly to the sum: a single extreme score will pull the mean sharply toward it. The median is resistant (robust) because it depends solely on rank order: changing an extreme value does not alter the middle position. For skewed distributions (like income), median is the preferred measure of central tendency.
What does a Z-score tell you about a data point?
A Z-score (z = (x - μ) / σ) measures how many standard deviations an individual observation lies above or below the mean. A Z-score of 0 is exactly at the mean; +2.0 indicates a score in the top ~2.5% of a normal distribution; and -1.5 indicates a score 1.5 standard deviations below average.
What is the Empirical Rule (68-95-99.7 rule) in normal distributions?
For any symmetric, bell-shaped normal distribution: approximately 68% of all data points fall within 1 standard deviation of the mean (μ ± 1σ); 95% fall within 2 standard deviations (μ ± 2σ); and 99.7% fall within 3 standard deviations (μ ± 3σ). Observations beyond ±3σ are typically flagged as statistical anomalies.
What is the difference between standard deviation and standard error?
Standard deviation (s) quantifies the variability or spread of individual data points within a dataset. Standard error of the mean (SE = s / √n) quantifies the precision of the sample mean estimate—measuring how much sample means would fluctuate across repeated samplings from the population.

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