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ABSTRACT: WidgeCorps’s management team had a lack in understand of some of the key multivariate statistical techniques used by many companies to measure how variables react with one another. This paper will discuss how three of these techniques are commonly used and provides a recommendation for the company to use as they move forward with research and development of new products. This paper also compares and contrasts the different multivariate techniques.
KEYWORDS: multivariate techniques, Chi-Square Test, multidimensional scaling

There are many different multivariate techniques commonly used in businesses across the world. This paper will compare three commonly used techniques including factor analysis, multi-dimensional scaling, and cluster analysis. Additionally, I will provide my recommendation for WidgeCorp to follow as we move forward and dive into the cold beverage market.
To begin, it is important to have a clear understanding about why and how a company will use multivariate techniques as part research. The term multivariate technique is somewhat of a blanket-term which includes many different techniques used by statisticians and researchers in many different fields, (Dayton, 2012). Multivariate techniques allow for companies to perform research on more than one variable to determine if there is a relationship between them. For many companies, the multivariate techniques are used to effective measure quality and safety, (Yang, 2010). WidgeCrop will be able to use each of the techniques as we move forward with our new business ventures into the cold beverage market.
Factor Analysis:
Factor analysis is one of the many techniques that can be used in different types of research projects. Factor analysis is most often used to compare variables which have a correlation to other confounding variables, (Dayton, 2012). Factor analysis will prove helpful after we have developed our products and are testing the new beverages in different markets. As an



References: Berenson, M., Krehbiel, T., & Levine, D., (2010). Business Statistics: A First Course. Prentice Hall. Upper Saddle River, NJ. Borgatti, S. (1997). Multidimensional Scaling. Retrieved 09-09-2012 from http://www.analytictech.com/borgatti/mds.htm. Cinca., C.,& Molinero, C., (2001). Bank failure, a Multidimensional Scaling approach. Eurpoean Journal of Finance. 7(2)18. Dayton, D., (2012). Multivariate statistics. Retrieved 09-23-2012 from https://campus.ctuonline.edu Downes, L., (2012). Customer intelligence, privacy, and the “Creepy factor”. Harvard Business Review. Retrieved 09-09-2012 from http://blogs.hbr.org/cs/2012/08/customer_intelligence_privacy.html. Du, R., & Kamakura, (2012). Qualitative trend spotting. Journal of Marketing Research. 49(4)22. Keough, M., & Quinn, G., (2001). Design and Analysis for biologists. Retrieved from http://bio.classes.ucsc.edu/bio286/MIcksBookPDFs/QK18.PDF. Yang, Z., & Yingwei, Z., (2010). Process monitoring, fault diagnosis and quality prediction methods based on multivariate statistical techniques. IETE Technical Review. 27(5)14. Wilkes, R., (1977). Product positioning by multidimensional scaling. Journal of Advertising Research. 17(4)5.

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