As a domain expert in the field of mathematics, particularly in statistics, I can provide a comprehensive explanation regarding the concept of the sum of squares and its relation to the impossibility of having a negative value. In statistics, the sum of squares (SS) is a measure that is often used in the context of variance and standard deviation calculations. It is a fundamental concept that underlies many statistical analyses, including the analysis of variance (ANOVA) and regression analysis. To understand why the sum of squares cannot be less than zero, let's first define what it is. The sum of squares is calculated by taking each number in a dataset, subtracting the mean of the dataset (which centers the data around zero), squaring the result (which ensures it is non-negative), and then summing all these squared differences together. The formula for the sum of squares is as follows: \[ SS = \sum_{i=1}^{n} (x_i - \bar{x})^2 \] Where: - \( SS \) is the sum of squares, - \( n \) is the number of observations in the dataset, - \( x_i \) is the ith observation, - \( \bar{x} \) is the mean of all observations. Now, let's break down the components of this formula to understand why the sum of squares is always non-negative: 1. Subtracting the Mean: When you subtract the mean from each data point, you are essentially centering your data around zero. This step does not affect the sign of the result; it merely shifts all data points by the same amount. 2. Squaring the Differences: This is the critical step that ensures the sum of squares is non-negative. Squaring a number, regardless of whether it is positive or negative, always results in a positive value. For example, squaring a positive number \( a \) gives \( a^2 \), and squaring a negative number \( -a \) gives \( (-a)^2 = a^2 \). Therefore, the squared difference \( (x_i - \bar{x})^2 \) is always non-negative. 3. Summing the Squared Differences: Since each squared difference is non-negative, adding them together can only result in a non-negative sum. It's impossible for the sum to be negative because you cannot add a series of non-negative numbers and end up with a negative result. The reference content you provided is correct in stating that squared deviations are always greater than or equal to zero. This is a mathematical fact that stems from the properties of squaring numbers. When you square a number, you are essentially multiplying it by itself, which cannot yield a negative result. Furthermore, the standard deviation, which is the square root of the average of the squared differences from the mean, is a measure of the amount of variation or dispersion in a set of values. A standard deviation of zero indicates that there is no variability in the dataset; all values are identical. In conclusion, the sum of squares is a mathematical construct that is inherently non-negative. It is a fundamental part of statistical analysis and serves as a basis for understanding the dispersion and variability within a dataset. read more >>

SS cannot be less than zero because it is computed by adding squared deviations. Squared deviations are always greater than or equal to zero. ... A standard deviation of zero indicates there is no variability. In this case, all of the scores in the sample have exactly the same value.read more >>