Measure of Dispersion

The standard deviation is a widely used concept in statistics and it tells how much variation (measure of spread or dispersion) is in the data set. It can be defined as the positive square root of the mean (average) of the squared deviations of the values from their mean.
To calculate the standard deviation one has to follow these steps:

Calculation of Standard Deviation

1. First, find the mean of the data.
2. Take the difference of each data point from the mean of the given data set (which is computed in step 1). Note that, the sum of these differences must be equal to zero or near to zero due to rounding of numbers.
3. Now compute the square of the differences obtained in Step 2, it would be greater than zero, and it will be a positive quantity.
4. Now add up all the squared quantities obtained in step 3. We call it the sum of squares of differences.
5. Divide this sum of squares of differences (obtained in step 4) by the total number of observations (available in data) if we have to calculate population standard deviation ($\sigma$). If you want t to compute sample standard deviation ($S$) then divide the sum of squares of differences (obtained in step 4) by the total number of observations minus one ($n-1$) i.e. the degree of freedom. Note that $n$ is the number of observations available in the data set.
6. Find the square root (also known as under root) of the quantity obtained in step 5. The resultant quantity in this way is known as the standard deviation (SD) for the given data set.

The sample SD of a set of $n$ observation, $X_1, X_2, \cdots, X_n$ denoted by $S$ is

\begin{aligned}
\sigma &=\sqrt{\frac{\sum_{i=1}^n (X_i-\overline{X})^2}{n}}; Population\, SD\\
S&=\sqrt{ \frac{\sum_{i=1}^n (X_i-\overline{X})^2}{n-1}}; Sample\, SD
\end{aligned}

The standard deviation can be computed from variance too.

The real meaning of the standard deviation is that for a given data set 68% of the data values will lie within the range $\overline{X} \pm \sigma$ i.e. within one standard deviation from the mean or simply within one $\sigma$. Similarly, 95% of the data values will lie within the range $\overline{X} \pm 2 \sigma$ and 99% within $\overline{X} \pm 3 \sigma$.

Examples

A large value of SD indicates more spread in the data set which can be interpreted as the inconsistent behaviour of the data collected. It means that the data points tend to be away from the mean value. For the case of smaller standard deviation, data points tend to be close (very close) to the mean indicating the consistent behavior of the data set.

The standard deviation and variance are used to measure the risk of a particular investment in finance. The mean of 15% and standard deviation of 2% indicates that it is expected to earn a 15% return on investment and we have a 68% chance that the return will be between 13% and 17%. Similarly, there is a 95% chance that the return on the investment will yield an 11% to 19% return.

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Introduction of Sum Square Deviations

In statistics, the sum of squared deviations (also known as the sum of squares) is a measure of the total variability (Measure of spread or variation) within a data set. In other words, the sum of squares is a measure of deviation or variation from the mean (average) value of the given data set.

Computation of Sum of Squared Deviations

A sum of squares is calculated by first computing the differences between each data point (observation) and the mean of the data set, i.e. $x=X-\overline{X}$. The computed $x$ is known as the deviation score for the given data set. Squaring each of these deviation scores and then adding these squared deviation scores gave us the sum of squared deviation (SS), which is represented mathematically as

\[SS=\sum(x^2)=\sum(X-\overline{X})^2\]

Note that the small letter $x$ usually represents the deviation of each observation from the mean value, while the capital letter $X$ represents the variable of interest in statistics.

The Sum of Squared Deviations Example

Consider the following data set {5, 6, 7, 10, 12}. To compute the sum of squares of this data set, follow these steps

• Calculate the average of the given data by summing all the values in the data set and then divide this sum of numbers by the total number of observations in the data set. Mathematically, it is $\frac{\sum X_i}{n}=\frac{40}{5}=8$, where 40 is the sum of all numbers $5+6+7+10+12$ and there are 5 observations in number.
• Calculate the difference of each observation in the data set from the average computed in step 1, for the given data. The differences are
  $5 – 8 = –3$; $6 – 8 = –2$; $7 – 8 = –1$; $10 – 8 =2$ and $12 – 8 = 4$
  Note that the sum of these differences should be zero. $(–3 + –2 + –1 + 2 +4 = 0)$
• Now square each of the differences obtained in step 2. The square of these differences are
  9, 4, 1, 4 and 16
• Now add the squared number obtained in step 3. The sum of these squared quantities will be $9 + 4 + 1 + 4 + 16 = 34$, which is the sum of the square of the given data set.

Sums of Squares in Different Context

In statistics, the sum of squares occurs in different contexts such as

• Partitioning of Variance (Partition of Sums of Squares)
• The sum of Squared Deviations (Least Squares)
• The sum of Squared Differences (Mean Squared Error)
• The sum of Squared Error (Residual Sum of Squares)
• The sum of Squares due to Lack of Fit (Lack of Fit Sum of Squares)
• The sum of Squares for Model Predictions (Explained Sum of Squares)
• The sum of Squares for Observations (Total Sum of Squares)
• The sum of Squared Deviation (Squared Deviations)
• Modeling involving the Sum of Squares (Analysis of Variance)
• Multivariate Generalization of Sum of Square (Multivariate Analysis of Variance)

As previously discussed, the Sum of Squares is a measure of the Total Variability of a set of scores around a specific number.

Summary

• A higher sum of squares indicates that your data points are further away from the mean on average, signifying greater spread or variability in the data. Conversely, a lower sum of squares suggests the data points are clustered closer to the mean, indicating less variability.
• The sum of squares plays a crucial role in calculating other important statistics like variance and standard deviation. These concepts help us understand the distribution of data and make comparisons between different datasets.

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R Faqs

Measure of Central Tendency provides typical value about the data set, but it does not tell the actual story about the data i.e. mean, median, and mode are enough to get summary information, though we know about the center of the data. In other words, we can measure the center of the data by looking at averages (mean, median, and mode). These measures tell nothing about the spread of data. So for more information about data, we need some other measure, such as the Range measure of dispersion or spread.

Range Measure of Dispersion

The Spread of data can be measured by calculating the range of data; the range tells us how many numbers of data extend. The range is an absolute measure of dispersion that can be found by subtracting the highest value (called upper bound) in data from the smallest value (called lower bound). i.e.

Range = Upper Bound – Lowest Bound
OR
Range = Largest Value – Smallest Value

This absolute measure of dispersion has disadvantages as range only describes the width of the data set (i.e. only spread out) measured in the same unit as data, but it does not give the real picture of how data is distributed. If data has outliers, using range to describe the spread of that can be very misleading as the range is sensitive to outliers.

We need to be careful in using the range measure of dispersion as it does not give the full picture of what’s going between the highest and lowest values. It might give a misleading picture of the spread of the data because it is based only on the two extreme values. Therefore, Range is an unsatisfactory measure of dispersion.

However, the range measure of dispersion is widely used in statistical process control such as control charts of manufactured products, daily temperature, stock prices, etc., applications as it is very easy to calculate. It is an absolute measure of dispersion, its relative measure known as the coefficient of dispersion defines the relation

\[Coefficient\,\, of\,\, Dispersion = \frac{x_m-x_0}{x_m-x_0}\]

The coefficient of dispersion is pure dimensionless and is used for comparison purposes.

Data Frame in R Language

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