Much of the information contained in the multivariate data sets can be assessed by calculating certain summary numbers, known as multivariate 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 and multivariate data sets.
Multivariate data sets are used in various fields, such as:
- Social Sciences: Analyzing factors influencing social phenomena like voting behavior, educational attainment, or health outcomes.
- Business: Understanding customer demographics and purchase patterns, market research, risk assessment, and financial modeling.
- Natural Sciences: Studying relationships between environmental variables, analyzing climate data, or exploring genetic factors influencing diseases.
Multivariate Data Sets: Descriptive Analysis
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 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 for multivariate data sets 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 variables 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 the 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 $V_2$ for multivariate data sets 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 product of the deviation from their respective means) therefore
$s_{ik} =\frac{1}{n} \sum _{j=1}^{n}(x_{ji} -\bar{x}_{i} )(x_{jk} -\bar{x}_{k} )$; $i=1,2,\cdots, p$ and $k=1,2,\cdots, p$.
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
For Multivariate Data Sets, the sample correlation coefficient for the ith and kth variables 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.
Other Multivariate Analysis
Multiple Regression: It is used to model the relationship between a dependent variable (DV) and multiple independent variables (IV).
Principal Component Analysis (PCA): It reduces the dimensionality of data by identifying a smaller set of uncorrelated variables that capture most of the data’s variance.
Cluster Analysis: It groups the data points into clusters based on their similarities, helping identify subgroups within the data.
Discriminant Analysis: It classifies data points into predefined groups based on their characteristics.
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