Sum Of Squares - Formula, Steps, Error, Examples - Cuemath

Sum of Squares

The sum of squares means the sum of the squares of the given numbers. In statistics, it is the sum of the squares of the variation of a dataset. For this, we need to find the mean of the data and find the variation of each data point from the mean, square them and add them. In algebra, the sum of the square of two numbers is determined using the (a + b)2 identity. We can also find the sum of squares of the first n natural numbers using a formula. The formula can be derived using the principle of mathematical induction. We do these basic arithmetic operations which are required in statistics and algebra. There are different techniques to find the sum of squares of given numbers.

In this article, we will discuss the different sum of squares formulas. To calculate the sum of two or more squares in an expression, the sum of squares formula is used. Also, the sum of squares formula is used to describe how well the data being modeled is represented by a model. Let us learn these along with a few solved examples in the upcoming sections for a better understanding.

1.	What is the Sum of Squares?
2.	Sum of Squares Formula
3.	Steps to Find Sum of Squares
4.	Sum of Squares in Statistics
5.	Sum of Squares Error
6.	FAQs on Sum of Squares

What is the Sum of Squares?

The sum of squares in statistics is a tool that is used to evaluate the dispersion of a dataset. To evaluate this, we take the sum of the square of the variation of each data point. In algebra, we find the sum of squares of two numbers using the algebraic identity of (a + b)2. Also, in mathematics, we find the sum of squares of n natural numbers using a specific formula which is derived using the principle of mathematical induction. Let us now discuss the formulas of finding the sum of squares in different areas of mathematics.

Sum of Squares Formula

The sum of squares formula in statistics is used to describe how well the data being modeled is represented by a model. It shows the dispersion of the dataset. To calculate the sum of two or more squares in an expression, the sum of squares formula is used. Thus, a few sums of squares formulas are,

• In statistics : Sum of squares of n data points= ∑ni=0 (xi - x̄)2
• In algebra : Sum of squares = a2 + b2 = (a + b)2 - 2ab
• Sum of squares of n natural numbers formula: 12 + 22 + 32 + ... + n2 = [n(n+1)(2n+1)] / 6

Where,

• ∑ = represents sum
• xi = each value in the set
• x̄ = mean of the values
• xi – x̄ = deviation from the mean value
• (xi – x̄)2 = square of the deviation
• a, b = arbitrary numbers
• n = number of terms in the series

Let a and b be the two numbers. Assuming the squares of a and b are a2 and b2. The sum of the squares of a and b is a2 + b2. We could obtain a formula using the known algebraic identity (a+b)2 = a2 + b2 + 2ab. Subtracting 2ab from both the sides we can conclude that a2 + b2 = (a + b)2 - 2ab. Let a, b, c be the 3 numbers for which we are supposed to find the sum of squares. The sum of their squares is a2 + b2 + c2. Using the known algebraic identity (a+b+c)2 = a2 + b2 + c2 + 2ab + 2bc +2ca, we can evaluate that a2 + b2 + c2 = (a+b+c)2 - 2ab - 2bc - 2ca.

Steps to Find Sum of Squares

The total sum of squares can be calculated in statistics using the following steps:

• Step 1: In the dataset, count the number of data points.
• Step 2: Calculate the mean of the data.
• Step 3: Subtract each data point from the mean.
• Step 4: Determine the square of the difference determined in step 3.
• Step 5: Add the squares determined in step 4.

Sum of Squares in Statistics

The steps discussed above help us in finding the sum of squares in statistics. The value tells us the amount of dispersion in a dataset. It measures the variation of the data points from the mean and helps in studying the data in a better way. If the value of the sum of squares is large, then it implies that there is a high variation of the data points from the mean value. On the other hand, if the value is small, then it implies that there is a low variation of the data from its mean.

Sum of Squares Error

In statistics, the sum of squares error (SSE) is the difference between the observed value and the predicted value. It is also called the sum of squares residual (SSR) as it is the sum of the squares of the residual, that is, the deviation of predicted values from the actual values. The formula for the sum of squares error is given by,

SSE = ∑ni=0 (yi - f(xi))2, where yi is the ith value of the variable to be predicted, f(xi) is the predicted value, and xi is the ith value of the explanatory variable.

We can also evaluate the sum of squares error (SSE) by subtracting the sum of squares regression (SSR) from the sum of squares total (SST), that is, SSE = SST - SSR

Important Notes on Sum of Squares

• The sum of squares in statistics is a tool that is used to evaluate the dispersion of a dataset.
• SSE = SST - SSR
• Sum of squares of n data points= ∑ni=0 (xi - x̄)2

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