# Linear Regression Calculator You can use this Linear Regression Calculator to find out the equation of the regression line along with the linear correlation coefficient. It also produces the scatter plot with the line of best fit. Enter all known values of X and Y into the form below and click the "Calculate" button to calculate the linear regression equation.

Linear Regression Calculator

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## What is Linear Regression Calculator

A linear regression calculator is a tool that helps analyze the relationship between two variables by fitting a linear equation to the data. It calculates the slope, intercept, and other statistical measures associated with the linear regression model.

The linear regression model assumes a linear relationship between the independent variable (X) and the dependent variable (Y). It fits the best-fitting line through the data points, minimizing the sum of the squared differences between the observed Y values and the predicted Y values on the line.

Here's how you can use a linear regression calculator:

1. Input your data: You need a set of paired data points, where you have observations for both the independent variable (X) and the dependent variable (Y).

2. Perform the calculation: The linear regression calculator will compute the slope (β₁), intercept (β₀), and other statistical measures such as the coefficient of determination (R²), standard error, and p-values.

3. Interpret the results: The slope represents the rate of change in the dependent variable for every unit change in the independent variable. The intercept represents the value of the dependent variable when the independent variable is zero. The coefficient of determination (R²) indicates the proportion of variability in the dependent variable explained by the linear regression model.

4. Visualize the regression line: The calculator may also generate a scatter plot of the data points along with the regression line to visualize the relationship between the variables.

Please note that linear regression assumes a linear relationship between the variables and certain assumptions about the data, including independence of observations, linearity, constant variance, and normality of residuals. These assumptions should be checked before interpreting the results.

If you provide me with the specific data points or any other details, I can assist you further by performing a linear regression analysis.

## Reference

### The Linear Regression Calculator uses the following formulas:

The equation of a simple linear regression line (the line of best fit) is y = mx + b,

Slope m: m = (n*∑xi yi - (∑xi)*(∑yi)) / (n*∑xi2 - (∑xi)2)

Intercept b: b = (∑yi - m*(∑xi)) / n

Mean x: x̄ = ∑xi / n

Mean y: ȳ = ∑yi / n

Sample correlation coefficient r: r = (n*∑xiyi - (∑xi)(∑yi)) / Sqrt([n*∑xi2 - (∑xi)2][n*∑yi2 - (∑yi)2])

-1 < r < +1

Where:

n  is the total number of samples,

xi (x1, x2, ... ,xn) are the x values,

yi (y1, y2, ... ,yn) are the y values,

∑xi  is the sum of x values,

∑yi  is the sum of y values,

∑xi yi  is the sum of products of x and y values,

∑xi2  is the sum of squares of x values,

∑yi2  is the sum of squares of y values.

## Linear Regression Calculator Example

Sure! Here's an example of how you can calculate linear regression using a simple dataset:

Let's say we have the following dataset:

X = [1, 2, 3, 4, 5] # Independent variable Y = [2, 4, 5, 4, 6] # Dependent variable

To calculate the linear regression, we need to find the equation of a line that best fits the data. The equation for a straight line is given by:

Y = m * X + c

where "m" is the slope of the line and "c" is the y-intercept.

We can use the least squares method to estimate the values of "m" and "c" that minimize the sum of squared differences between the actual Y values and the predicted Y values from the line.

In this example, we can calculate the linear regression with the following steps:

Step 1: Calculate the mean of X and Y.

• Mean(X) = (1 + 2 + 3 + 4 + 5) / 5 = 3
• Mean(Y) = (2 + 4 + 5 + 4 + 6) / 5 = 4.2

Step 2: Calculate the deviations from the mean for X and Y.

• Deviations(X) = [1 - 3, 2 - 3, 3 - 3, 4 - 3, 5 - 3] = [-2, -1, 0, 1, 2]
• Deviations(Y) = [2 - 4.2, 4 - 4.2, 5 - 4.2, 4 - 4.2, 6 - 4.2] = [-2.2, -0.2, 0.8, -0.2, 1.8]

Step 3: Calculate the product of deviations for X and Y.

• Product(XY) = [-2 * -2.2, -1 * -0.2, 0 * 0.8, 1 * -0.2, 2 * 1.8] = [4.4, 0.2, 0, -0.2, 3.6]

Step 4: Calculate the squared deviations for X.

• Squared(X) = [(-2)^2, (-1)^2, 0^2, 1^2, 2^2] = [4, 1, 0, 1, 4]

Step 5: Calculate the slope (m) of the line.

• m = Σ(Product(XY)) / Σ(Squared(X)) = (4.4 + 0.2 + 0 - 0.2 + 3.6) / (4 + 1 + 0 + 1 + 4) = 8 / 10 = 0.8

Step 6: Calculate the y-intercept (c) of the line.

• c = Mean(Y) - (m * Mean(X)) = 4.2 - (0.8 * 3) = 4.2 - 2.4 = 1.8

Therefore, the linear regression equation for this dataset is: Y = 0.8 * X + 1.8.

Using this equation, you can make predictions for Y values based on given X values.

You may also be interested in our Quadratic Regression Calculator or Gini Coefficient Calculator