Basic Statistics and Data Analysis

Statistics Lecture Notes, Online MCQs

Category: Measure of Central Tendency

Basic Statistics / Measure of Central Tendency

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Median Measure of Central Tendency

Median (a measure of central tendency) is the middle-most value in the data set when all of the values (observations) in a data set are arranged either in ascending or descending order of their magnitude. Median is also considered as a measure of central tendency which divides the data set in two halves, where the first half contains 50% observations below the median value and 50% above the median value. If in a data set there are an odd number of observations (data points), the median value is the single-most middle value after sorting the data set.

Example: Consider the following data set 5, 9, 8, 4, 3, 1, 0, 8, 5, 3, 5, 6, 3.
To find the median of the given data set, the first sort it (either in ascending or descending order), that is
0, 1, 3, 3, 3, 4, 5, 5, 5, 6, 8, 8, 9. The middle-most value of the above data after sorting is 5, which is the median of the given data set.

When the number of observations in a data set is even then the median value is the average of two middle-most values in the sorted data.

Example: Consider the following data set, 5, 9, 8, 4, 3, 1, 0, 8, 5, 3, 5, 6, 3, 2.
To find the median first sort it and then locate the middle-most two values, that is,
0, 1, 2, 3, 3, 3, 4, 5, 5, 5, 6, 8, 8, 9. The middle-most two values are 4 and 5. So the median will be the average of these two values, i.e. 4.5 in this case.

The median is less affected by extreme values in the data set, so the median is the preferred measure of central tendency when the data set is skewed or not symmetrical.

For large data set it is relatively very difficult to locate median value in sorted data. It will be helpful to use median value using formula. The formula for odd number of observations is
$\begin{aligned}
Median &=\frac{n+1}{2}th\\
Median &=\frac{n+1}{2}\\
&=\frac{13+1}{2}\\
&=\frac{14}{2}=7th
\end{aligned}$

The 7th value in sorted data is the median of the given data.

The median formula for even number of observation is
$\begin{aligned}
Median&=\frac{1}{2}(\frac{n}{2}th + (\frac{n}{2}+1)th)\\
&=\frac{1}{2}(\frac{14}{2}th + (\frac{14}{2}+1)th)\\
&=\frac{1}{2}(7th + 8th )\\
&=\frac{1}{2}(4 + 5)= 4.5
\end{aligned}$

Note that median measure of central tendency, cannot be found for categorical data

Basic Statistics / Measure of Central Tendency

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Mode Measure of Central Tendency

The mode is the most frequent observation in the data set i.e. the value (number) that appears the most in data set. It is possible that there may be more than one mode or it may also be possible that there is no mode in a data set. Usually mode is used for categorical data (data belongs to nominal or ordinal scale) but it is not necessary. Mode can also be used for ordinal and ratio scale, but there should be some repeated value in the data set or data set can be classified in groups. If any of the data point don’t have same values (no repetition in data values) , then the mode of that data set will not exit or may not be meaningful. A data set having more than one mode is called multimode or multimodal.

Example 1: Consider the following data set showing the weight of child at age of 10 years: 33, 30, 23, 23, 32, 21, 23, 30, 30, 22, 25, 33, 23, 23, 25. We can found the mode by tabulating the given data in form of frequency distribution table, whose first column is the weight of child and second column is the number of times the weight appear in the data i.e frequency of the each weight in first column.

Weight of 10 year childFrequency
221
235
252
303
321
332
Total15

From above frequency distribution table we can easily found the most frequently occurring observation (data point), which will be the mode of data set. Therefore the mode of the given data set is 23, meaning that majority of the 10 year child have weight of 23kg. Note that for finding mode it is not necessary do make frequency distribution table, but it helps in finding the mode quickly and frequency table can also be used in further calculations such as percentage and cumulative percentage of each weight group.

Example 2: Consider we have information of person about his/her gender. Consider the M stands for male and F stands for Female. The sequence of person’s gender noted is as follows: F, F, M, F, F, M, M, M, M, F, M, F, M, F, M, M, M, F, F, M. The frequency distribution table of gender is

Weight of 10 year childFrequency
Male11
Female9
Total25

The mode of gender data is male, showing that most frequent or majority of the people have male gender in this data set.

Mode can be found by simply sorting the data in ascending or descending order. Mode can also be found by counting the frequent value without sorting the data especially when data contains small number of observations, though it may be difficult in remembering the number of times which observation occurs. Note that mode is not affected by the extreme values (outliers or influential observations).

The mode is also a measure of central tendency, but the mode may not reflect the center of the data very well. For example the mean of data set in example 1, is 26.4kg while mode is of 23kg.

One should use mode measure of central tendency if he/ she expects that data points will repeat or have some classification in it. For example in productionprocess a product produced can be classified as defective or non-defective product. Similarly student grades can classified as A, B, C, D etc. For such kind of data one should use mode as a measure of central tendency instead of mean or median.

Example 3: Consider the following data. 3, 4, 7, 11, 15, 20, 23, 22, 26, 33, 25, 13. There is no mode of this data as each of the value occurs once. Grouping this data in some useful and meaningful form we can get mode of the data for example, the grouped frequency table is

GroupValuesFrequency
0 to 93, 4, 73
10 to 1911, 13, 153
20 to 2920, 22, 23, 25, 265
30 to 39331
Total12

From this table, we cannot find the most appearing value, but we can say that “20 to 29” is the group in which most of the observations occur. We can say that this group contains the mode which can be found by using mode formula for grouped data.

Mode from Bar Graph

Bar Graph

Introduction to Multivariate Statistics / Measure of Central Tendency / Measure of Dispersion / Multivariate Statistics

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Descriptive Statistics Multivariate Data set

Much of the information contained in the data can be assessed by calculating certain summary numbers, known as descriptive statistics such as Arithmetic mean (a measure of location), an average of the squares of the distances of all of the numbers from the mean (variation/spread i.e. measure of spread or variation), etc. Here we will discuss descriptive statistics multivariate data set.

We shall rely most heavily on descriptive statistics which is a measure of location, variation, and linear association. For descriptive statistics multivariate data set, let us start with a measure of location, a measure of spread, sample covariance, and sample correlation coefficient.

Measure of Location

The arithmetic Average of $n$ measurements $(x_{11}, x_{21}, x_{31},x_{41})$ on the first variable (defined in Multivariate Analysis: An Introduction) is

Sample Mean = $\bar{x}=\frac{1}{n} \sum _{j=1}^{n}x_{j1} \mbox{ where } j =1, 2,3,\cdots , n $

The sample mean for $n$ measurements on each of the p variables (there will be p sample means)

$\bar{x}_{k} =\frac{1}{n} \sum _{j=1}^{n}x_{jk} \mbox{ where }  k  = 1, 2, \cdots , p$

Measure of Spread

Measure of spread (variance) for $n$ measurements on the first variable can be found as
$s_{1}^{2} =\frac{1}{n} \sum _{j=1}^{n}(x_{j1} -\bar{x}_{1} )^{2} $ where $\bar{x}_{1} $ is sample mean of the $x_{j}$’s for p variables.

Measure of spread (variance) for $n$ measurements on all variable can be found as

$s_{k}^{2} =\frac{1}{n} \sum _{j=1}^{n}(x_{jk} -\bar{x}_{k} )^{2}  \mbox{ where } k=1,2,\dots ,p \mbox{ and } j=1,2,\cdots ,p$

The Square Root of the sample variance is sample standard deviation i.e

$S_{l}^{2} =S_{kk} =\frac{1}{n} \sum _{j=1}^{n}(x_{jk} -\bar{x}_{k} )^{2}  \mbox{ where }  k=1,2,\cdots ,p$

Sample Covariance

Consider n pairs of measurement on each of Variable 1 and Variable 2
\[\left[\begin{array}{c} {x_{11} } \\ {x_{12} } \end{array}\right],\left[\begin{array}{c} {x_{21} } \\ {x_{22} } \end{array}\right],\cdots ,\left[\begin{array}{c} {x_{n1} } \\ {x_{n2} } \end{array}\right]\]
That is $x_{j1}$ and $x_{j2}$ are observed on the jth experimental item $(j=1,2,\cdots ,n)$. So a measure of linear association between the measurements of  $V_1$ and $V_2$ is provided by the sample covariance
\[s_{12} =\frac{1}{n} \sum _{j=1}^{n}(x_{j1} -\bar{x}_{1} )(x_{j2} -\bar{x}_{2}  )\]
(the average of product of the deviation from their respective means) therefore

$s_{ik} =\frac{1}{n} \sum _{j=1}^{n}(x_{ji} -\bar{x}_{i} )(x_{jk} -\bar{x}_{k}  )$;  i=1,2,..,p and k=1,2,\… ,p.

It measures the association between the kth variable.

Variance is the most commonly used measure of dispersion (variation) in the data and it is directly proportional to the amount of variation or information available in the data.

Sample Correlation Coefficient

The sample correlation coefficient for the ith and kth variable is

\[r_{ik} =\frac{s_{ik} }{\sqrt{s_{ii} } \sqrt{s_{kk} } } =\frac{\sum _{j=1}^{n}(x_{ji} -\bar{x}_{j} )(x_{jk} -\bar{x}_{k} ) }{\sqrt{\sum _{j=1}^{n}(x_{ji} -\bar{x}_{i} )^{2}  } \sqrt{\sum _{j=1}^{n}(x_{jk} -\bar{x}_{k}  )^{2} } } \]
$\mbox{ where } i=1,2,..,p \mbox{ and}  k=1,2,\dots ,p$

Note that $r_{ik} =r_{ki} $ for all $i$ and $k$, and $r$ lies between -1 and +1. $r$ measures the strength of the linear association. If $r=0$ the lack of linear association between the components exists. The sign of $r$ indicates the direction of the association.

Basic Statistics / Measure of Central Tendency / Measure of Dispersion

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Measure of Dispersion or Variability

The measure of location or averages or central tendency is not sufficient to describe the characteristics of a distribution, because two or more distributions may have averages which are exactly alike, even though the distributions are dissimilar in other aspects, and on the other hand, measure of central tendency represents the typical value of the data set. To give a sensible description of data, a numerical quantity called the measure of dispersion/ variability or scatter that describe the spread of the values in a set of data have two types of measures of dispersion or variability:

  1. Absolute Measures
  2. Relative Measures

A measure of central tendency together with a measure of dispersion gives adequate description of data as compared to use of measure of location only, because the averages or measures of central tendency only describes the balancing point of the data set, it does not provide any information about the degree to which the data tend to spread or scatter about the average value. So Measure of dispersion is an indication of the characteristic of the central tendency measure. The smaller the variability of a given set, the more the values of the measure of averages will be representative of the data set.

  1. Absolute Measures
    Absolute measures defined in such a way that they have units such as meters, grams, etc. same as those of the original measurements. Absolute measures cannot be used to compare the variation/spread of two or more sets of data.
    Most Common absolute measures of variability are:  
    • Range
    • Semi-Interquartile Range or Quartile Deviation
    • Mean Deviation
    • Variance
    • Standard Deviation
  2. Relative Measures
    The relative measures have no units as these are ratios, coefficients, or percentages. Relative measures are independent of units of measurements and are useful for comparing data of different natures.  
    • Coefficient of Variation
    • Coefficient of Mean Deviation
    • Coefficient of Quartile Deviation
    • Coefficient of Standard Deviation

Different terms are used for the measure of dispersion or variability such as variability, spread, scatterness, the measure of uncertainty, deviation, etc.

References:
http://www2.le.ac.uk/offices/careers/ld/resources/numeracy/variability

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