Math July 13, 2026 · 8 Min Read

Scatter Plot Calculator – Guide & Formulas

Plot data points and calculate linear regression, correlation coefficient, and line of best fit. Get R² value and predict y from x values.

Try the free calculator

Put these formulas into practice with our instant, step-by-step Scatter Plot Calculator.

Open Calculator ›

Use our free **scatter plot calculator** to **plot data points** and **calculate linear regression** with the **correlation coefficient** and **line of best fit**. This **linear regression calculator** helps you find the **R² value** and **predict y from x** with step-by-step results. Whether you need a **scatter plot with regression line**, **least squares regression calculator**, or **correlation coefficient calculator**, this tool provides accurate results. 100% free — no signup required!

Key Takeaway

Use the free Scatter Plot Calculator to plot data points and calculate linear regression, correlation coefficient, and line of best fit. get r² value and predict y from x values. Get instant results with step-by-step explanations.

How to Use the Scatter Plot Calculator

  1. Enter an **x-value** and **y-value** for your first data point in the **scatter plot calculator**.
  2. Click **"Add Point"** to add each data pair to the plot.
  3. Repeat to add **multiple data points** for your dataset.
  4. Click **"Calculate"** to compute the **linear regression** and **correlation**.
  5. Review the **regression equation** (y = mx + b) with slope and intercept.
  6. Check the **correlation coefficient (r)** and **R² value**.
  7. Use the **predicted y values** from the **line of best fit**.
  8. Analyze the **scatter plot trend** and **data analysis** results.

The Formula

y = mx + b | Slope: m = (n—xy - —x—y) / (n—x² - (—x)²) | Intercept: b = (—y - m—x) / n

Variable Definitions

  • m: Slope of the regression line
  • b: Y-intercept of the regression line
  • r: Pearson correlation coefficient (-1 to 1)
  • R²: Coefficient of determination (proportion of variance explained)
  • n: Number of data points
  • -: Summation symbol

Example: Linear Regression for 5 Data Points

Find the line of best fit for points: (1,2), (2,4), (3,5), (4,4), (5,5).

  1. Step 1: Calculate sums: —x=15, —y=20, —xy=70, —x²=55, n=5.
  2. Step 2: Slope m = (5×70 - 15×20) / (5×55 - 15²) = (350-300)/(275-225) = 50/50 = 1.
  3. Step 3: Intercept b = (20 - 1×15) / 5 = 5/5 = 1.
  4. Step 4: Regression equation: y = 1x + 1.
  5. Step 5: For x=6, predicted y = 6 + 1 = 7.

Frequently Asked Questions

What is a scatter plot?

A scatter plot displays individual data points on a graph with x and y coordinates. It helps visualize the relationship between two variables.

What is linear regression?

Linear regression finds the best-fitting straight line through a set of data points. It minimizes the sum of squared vertical distances between the points and the line.

What does the correlation coefficient mean?

The correlation coefficient (r) measures the strength and direction of the linear relationship. Values range from -1 (perfect negative) to +1 (perfect positive), with 0 indicating no linear relationship.

What is R² value?

R² (R-squared) is the coefficient of determination. It represents the proportion of variance in y that is explained by the regression model. Values range from 0 to 1.

How many data points do I need?

You need at least 2 points for a line, but 5 or more points give more meaningful regression statistics and correlation analysis.

Can I predict values with this calculator?

Yes, once you have the regression equation, you can substitute any x value to predict the corresponding y value using the line of best fit.

What is the least squares method?

The least squares method finds the regression line by minimizing the sum of the squared vertical distances (residuals) between each data point and the line.

What does a negative correlation mean?

A negative correlation means that as one variable increases, the other tends to decrease. The regression line slopes downward from left to right.

How do I interpret the slope?

The slope (m) indicates how much y changes for each unit increase in x. A slope of 2 means y increases by 2 for every 1-unit increase in x.

Can I use this for non-linear data?

This calculator performs linear regression. For curved relationships, you would need polynomial or exponential regression, which requires different methods.