How to find the SSX?

Once the number of measurements is established, the next step is to compute the mean. The mean is calculated by summing all the measurements and then dividing the total by the number of measurements (n). This provides an average value that represents the central tendency of the data.

Following the computation of the mean, the next phase involves subtracting each individual measurement from the mean. This step is crucial as it highlights the deviation of each data point from the average.

After subtracting each measurement from the mean, the differences obtained are squared. Squaring the differences ensures that all values are positive, eliminating any negative deviations that might complicate the calculation.

To calculate the sum of squares, the initial step involves determining the number of measurements. This quantity is represented by the letter "n," which signifies the sample size. Essentially, it denotes how many data points are being considered in the calculation.

The subsequent step entails summing all the squared differences. This process aggregates the deviations, providing a cumulative measure of the variance in the data. Once the sum of the squares is obtained, it is divided by (n-1) to calculate the sample variance. This adjustment is necessary because we are dealing with a sample rather than the entire population.