Applications of Multivariate Techniques

Running Head: MULTIVARIATE TECHNIQUES APPLICATIONS
WAYS IN WHICH MULTIVARIATE TECHNIQUES WILL CONTRIBUTE TO
BIG-D INCORPORATED

MULTIVARIATE TECHNIQUES APPLICATIONS
Statistical tools and various techniques are used across multiple corporations to resolve
their daily challenges (Hudaverdi, 2012). Statistical analysis is applied across most companies
now for the purpose of serving a various purpose and resolve client related queries. In the current
report scope, the various purposes of multivariate analysis are analyzed for Big D Incorporated.
Along with the analysis of various multivariate techniques, the chosen multivariate technique in
comparison to the other methods is analyzed. The importance of the technique is told to the
Board of Directors, which can contribute to the overall process of decision making.
Big D sporting goods company when faced with an investment dilemma, while the
expansion of the business operations conducted varied statistical analysis. In order to carry out
the analysis of ways the multivariate techniques are used to serve the clients in the best possible
way, one needs to have a proper understanding of the topic. Multivariate analysis makes use of
statistical principles used to identify the relationship where variables are two or more (Haware,
Tho & Bauer-Brandl, 2009). These techniques are used to create a real situation where every
product, situation or any decision is affected by multiple factors. They are used to study data sets
and identify whether the variables are dependent on each other. This is a very effective test of
significance which entails a more accurate picture after analyzing multiple variables instead of
any one variable. This type of technique is commonly applied for research and development,
consumer research, quality control, market research, process control, quality assurance, and
optimization. Additionally, multivariate techniques are also effective in identifying potential
problems. The new client at Big D Incorporated which is sporting company for whom; they are
required to identify the best multivariate technique.
Big D Incorporated can utilize more than 20 multivariate techniques, however, the best
possible method varies between different companies and also owing to the category of data
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MULTIVARIATE TECHNIQUES APPLICATIONS
researched. Some of the different types of multivariate techniques that can identify various
dependent and interdependent responses are - multivariate analysis of variance (MANOVA),
multidimensional scaling, cluster analysis, multiple regression analysis, and factor analysis.
Multivariate analysis of variance identifies how one or more variable can influence a
group by calculating statistical averages. For example, in pharmaceutical companies, this type of
analysis is used to determine whether a drug will work or not, whether the severity of side
effects, average life expectancy and perceived pain of an experimental group on whom the dug is
administered would differ from that of the established product (Jackson, Riley & White, 2011).
Big D Incorporated can use this analysis to identify the type of customers who buys outdoor
sports goods most based on their current demographics of customers.
Cluster analysis helps in grouping similar variables in corresponding categories, thereby
generating clusters and segments. It cannot identify a dependent or independent variables; rather
they provide a structure within data in the absence of any interpretation or explanation. Taken,
for example, insurance companies use this type of data analysis to identify the reason behind the
rise in insurance claims, if a particular region experiences a high number of claims. Similarly,
big D Incorporated can utilize this type of analysis to develop market segment that can help their
client in positioning their products in a better way and also put them in a competitive position
over their competitors. Besides, this technique can also help them in exploring new markets such
as Lincoln Park neighborhood in Chicago and develop products that are useful and relevant to
specific clusters.
Multidimensional scaling is used to analyze the similarities or differences between two
sets of objects through a visual representation of distance. In multidimensional scaling plotting
on a graph, objects which have more similarities are depicted with a shorter distance and in
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