Standard deviation is a measure of how spread out the numbers in a data set are. A low standard deviation means the data points are close to the mean, and a high standard deviation means the data points are spread out.

To calculate standard deviation, first find the mean of the data. Then, subtract the mean from each data point, square the result, and average the squared differences. Finally, take the square root of that average.

It helps measure the variability or dispersion in a data set, making it useful for understanding how consistent or inconsistent the data is.

The formula for standard deviation is:
σ = √[ Σ (xi – μ)² / N ], where μ is the mean of the data and N is the number of data points.

No, the standard deviation is always a positive value or zero because it represents the average distance of data points from the mean.

The standard deviation is the square root of the variance. Variance measures the spread of the data, and standard deviation gives a more interpretable measure since it is in the same units as the data.

A low standard deviation means that the data points are close to the mean, while a high standard deviation indicates that the data points are more spread out.

Standard deviation is most useful for quantitative data. It is not meaningful for categorical or non-numeric data.

It is used to measure the spread of data, and to assess how well a model or hypothesis fits the data.

If all data points are the same, the standard deviation will be zero, as there is no variation in the data.