# How to calculate r square

R-squared, also known as the coefficient of determination, is a statistical measure that illustrates how well a regression model explains the variation in the dependent variable. It ranges from 0 to 1, where 1 indicates that the model perfectly accounts for all the variability, and 0 means none of it is explained by the model. Calculating R-square can be essential in determining the effectiveness of a regression model in predicting future values and understanding how much variance can be explained by the independent variables.

In this article, we will explore various methods to calculate R-square step by step.

**Method 1: Manual Calculation**

Given a dataset with ‘n’ samples and their respective values for X (independent variable) and Y (dependent variable), follow these steps to calculate R-square:

1. Compute the mean of Y: Add all Y values and divide by ‘n’. This will give you the average dependent variable value (Ȳ).

2. Calculate the total sum of squares (TSS): For each observation, find the difference between its actual Y value and Ȳ. Square this value, then sum up all squared differences.

TSS = Σ( Yi – Ȳ )^2

3. Perform linear regression on your dataset to obtain predicted Y values (Ŷi) using the equation Ŷi = a + b * Xi, where ‘a’ is the intercept and ‘b’ is the slope.

4. Calculate the residual sum of squares (RSS): For each observation, find the difference between its actual Y value (Yi) and its predicted Y value (Ŷi). Square this value, then sum up all squared differences.

RSS = Σ( Yi – Ŷi )^2

5. Compute R-square using TSS and RSS:

R² = 1 – (RSS / TSS)

**Method 2: Using Statistical Software**

Many statistical software packages, such as R, Python, and Excel, offer built-in functions to calculate R-square. These functions often employ advanced techniques for linear regression and are therefore more accurate:

1. R: Use the ‘lm()’ function to create a linear regression model, followed by the ‘summary()’ function to obtain R-squared values.

model <- lm(Y ~ X)

summary(model)$r.squared

2. Python: Employ the ‘linear_model’ library in sklearn to create a linear regression model. Fit the data and use the ‘score()’ function to retrieve R-squared values.

from sklearn.linear_model import LinearRegression

model = LinearRegression()

model.fit(X, Y)

R_square = model.score(X, Y)

3. Excel: Utilize the LINEST() or RSQ() functions to calculate R-squared values in Excel.

**Conclusion**

Calculating R-square is essential for gauging the effectiveness of your regression models. Whether you opt for manual calculation or using statistical software, understanding how to compute and interpret R-square values will empower you to make better predictions and optimize your models.