Utilizing Multivariate Techniques In Outdoor Sporting Goods Customer Scenario

How multivariate techniques can serve the organization best and how they can be applied to their new client, the outdoor sporting goods customer. The Board of Directors has asked you to research and explain 3 major ways in which multivariate statistics are utilized in this scenario. In this case, be sure to justify your decision.

Research using the library and the Internet to find at least 1 example of how a real company has used each of the following multivariate techniques: factor analysis, multidimensional scaling, and cluster analysis. This can be considered a benchmark if you can justify how it could benefit Big D Incorporated.

Write a summary to upper management explaining the following:

•How can each multivariate technique be utilized in Big D Incorporated, and what purpose would each serve?

•Which technique is your preferred method, and how is your chosen multivariate technique different from the other two techniques?

•What will the Board of Directors learn from your selected technique and more importantly, how will it contribute to the overall decision-making process? Ensure that your explanation is clear and concise in its explanation. 

Major ways in which multivariate statistics are utilized in this scenario

Multivariate statistics are the techniques that uses two or more variables or factors to demonstrate various insights from a data for making important decisions and inferences (Jesus, Beatriz, Beatriz, & Justiniano, 2011). The insights are made about the sample data (Jesus, Beatriz, Beatriz, & Justiniano, 2011). The insights are used for making inferences or conclusions about the features or characteristics of the population (Roman, Ravilya, & Ekaterina, 2010). Multivariate statistics can also be defined as the principles that involves observation and analysis of two or more variables of the sample (Roman, Ravilya, & Ekaterina, 2010).

The major use of multivariate statistics is for making comparisons, investigating patterns and relationships between the variables. Multivariate statistics are also used to make predictions of certain variables (Roman, Ravilya, & Ekaterina, 2010). There are several multivariate statistics analysis. Some of the known multivariate statistics analysis include; multiple regression analysis, logistic regression analysis, discriminant analysis, multivariate analysis of variance, factor analysis, cluster analysis, multidimensional scaling, correspondence analysis, conjoint analysis, canonical correlation analysis and  structural equation modelling (Roman, Ravilya, & Ekaterina, 2010).

A multiple linear regression analysis is a multivariate statistic technique that is used to investigate the relationship between a single independent variable and two or more independent variables (Roman, Ravilya, & Ekaterina, 2010). A multiple regression analysis is done to establish a linear model that can be used to predict the dependent variable using the independent variables. Multivariate linear regression analysis is mainly applied in forecasting (Jesus, Beatriz, Beatriz, & Justiniano, 2011).

A logistic regression analysis is another form of multivariate statistics analysis that is used for predicting the dependent variable using the independent variables (Jayasinghe, et al., 2011). The major difference between a logistics regression analysis and conventional multiple regression analysis is that a logistic regression analysis is applied when the value being predicted or forecasted is probabilistic while a multiple regression analysis in cases where the variables being predicted are deterministic (Jayasinghe, et al., 2011).

Discriminant analysis is a form of multivariate statistics that is used to classify observations or variables into groups that are homogeneous (Jesus, Beatriz, Beatriz, & Justiniano, 2011). A discriminant analysis is an important analyses for making comparisons of the variables (Jesus, Beatriz, Beatriz, & Justiniano, 2011). An example of discriminant analysis is the use of cross tabulation (Jesus, Beatriz, Beatriz, & Justiniano, 2011).

A multivariate analysis of variance (MANOVA) is a form of multivariate statistical analysis that is used to investigate the relationship between more than two categorical variables (Jesus, Beatriz, Beatriz, & Justiniano, 2011). The major aim of conducting a multivariate analysis of variance (MUNOVA) is to establish whether there is any significant difference in the average values of the categorical variables (Reddy, et al., 2015).

Factor analysis is a form of multivariate statistical analysis that is done to the variable to establish the variables that are significant (Bustaamante, Paredes, Moyaral, & Moral, 2009). Therefore, given a wide range of independent variables, a factor analysis is important in determining the independent variables that have significant effect or contribution to the dependent variable (Bustaamante, Paredes, Moyaral, & Moral, 2009). Thus, a factor analysis helps a researcher to reduce the independent variables to those that are sizable and can easily be studied, only those variables that have significant influence on the dependent variable (Bustaamante, Paredes, Moyaral, & Moral, 2009).

Examples of real companies that used factor analysis, multidimensional scaling, and cluster analysis

Cluster analysis is a form of multivariate statistical analysis that is conducted to identify factors with similar features and classify them into separate homogenous groups for proper inferencing and decision making (Bustaamante, Paredes, Moyaral, & Moral, 2009).

Multidimensional scaling is a form of multivariate statistical analysis that is done to change or transform consumer judgments of similar into distances represented in multidimensional space (Bustaamante, Paredes, Moyaral, & Moral, 2009). Therefore, a multidimensional scaling is a form of decomposition approach. For instance, our scenario of outdoor sporting goods, we could ask the customers to rate them based on different factors (Bustaamante, Paredes, Moyaral, & Moral, 2009). The ratings can be decomposed and represented in different metrics that represent the current customer rating (Bustaamante, Paredes, Moyaral, & Moral, 2009). The current ratings can help the customers and the potential customers to choose the best rated outdoor sporting goods, thereby boosting their confidence in the products (Bustaamante, Paredes, Moyaral, & Moral, 2009).

A conjoint analysis is a form of multivariate statistical analysis that applies the evaluation of a factor under different categories of attributes (Bustaamante, Paredes, Moyaral, & Moral, 2009). For instance, we could evaluate the utility of the outdoor sporting goods under different levels of customer satisfaction (Roman, Ravilya, & Ekaterina, 2010).

A structural equation modelling is a form of multivariate statistical analysis that evaluates the relationship between a set of variables or factors simultaneously (Bustaamante, Paredes, Moyaral, & Moral, 2009). Therefore, the unique feature of a structural modelling technique is that it may contain more than two dependent variables unlike the conventional regression analysis with only one dependent variable (Beverly, Zapata, & Kreinovich, 2014).

Having discussed the major multivariate statistical analysis methods or techniques, it is important to point the three most appropriate for D mining (Lapko, 2010). The three most appropriate multivariate techniques are; the multiple regression analysis, the multivariate analysis of variance technique and the conjoint analysis technique (Roman, Ravilya, & Ekaterina, 2010).

A multiple regression is a technique that is used for prediction or forecasting (Li, et al., 2012). A multiple regression model will be helpful in predicting the volume of sales of the outdoor sporting goods (Mateu, Lorenzo, & Porcu, 2010). Using the volume of sales as the dependent variable, there are some other variables that were determined as having effects on the volume of sales (Reddy, et al., 2015). The multiple regression analysis or modelling will help in coming up with a model to predict the volume of sales at any time given the factors or determinants (Ritter, 2012).

Multivariate analysis of variance (MANOVA) is useful in analysis the categorical factors or attributes of the outdoor sporting goods such as the customer rating of the goods, the level of customer preference for the difference goods and the durability (Roman, Ravilya, & Ekaterina, 2010). A conjoint analysis is useful in grouping the various factors of the outdoor sporting goods for proper comparison (Roman, Ravilya, & Ekaterina, 2010).

The multivariate analysis techniques are used in a wide range of areas and for a wide range of functions. An example of the use of the multivariate analysis techniques is the ranking of companies by the S&P companies (Suhr & Diane). The ranking is done using the conjoint analysis. Similarly, banks use the multiple regression analysis to predict the future cash flows (Viktor, 2015).

Most useful multivariate techniques for Big D Incorporated

A multiple linear regression analysis is a multivariate statistic technique that is used to investigate the relationship between a single independent variable and two or more independent variables (Lapko, 2010). The upper management can use the multiple linear regression analysis technique to predict the future cash flows (Kosheleva & Kreinovich, 2013). Similarly, the multiple regression analysis can be used to predict the level of future sales of the outdoor sporting activities (Jesus, Beatriz, Beatriz, & Justiniano, 2011).

A logistic regression analysis is another form of multivariate statistics analysis that is used for predicting the dependent variable using the independent variables (Jameel, et al., 2009). The major difference between a logistics regression analysis and conventional multiple regression analysis is that a logistic regression analysis is applied when the value being predicted or forecasted is probabilistic while a multiple regression analysis in cases where the variables being predicted are deterministic. The upper management can use this technique to predict several other factors of the outdoor sporting activities such as the level of customer preference of each of the outdoor sporting good (Jayasinghe, et al., 2011).

Discriminant analysis is a form of multivariate statistics that is used to classify observations or variables into groups that are homogeneous (Jayasinghe, et al., 2011). The upper management can use this technique to classify their risks into various categories depending on the potential sources of each risk (Jameel, et al., 2009).

A multivariate analysis of variance (MANOVA) is a form of multivariate statistical analysis that is used to investigate the relationship between more than two categorical variables (Everitt, Landau, Abine, Leese, & Stahl, 2011). The major aim of conducting a multivariate analysis of variance (MUNOVA) is to establish whether there is any significant difference in the average values of the categorical variables (Izmalkov, 2015). The upper management can use multivariate analysis of variance to investigate whether there is any significant difference in the different categories of risks and potential risks that they are likely to face (Bustaamante, Paredes, Moyaral, & Moral, 2009).

My most preferred technique is the multiple regression analysis technique. I like the multiple regression modelling because it uses the current data to predict future outcomes. The prediction of the future outcomes using the current information is a sign of going- concern by the company (Beverly, Zapata, & Kreinovich, 2014).

The management will learn a number of things from my choice and decision process. The management will learn that I prefer a data-driven decision making. Similarly, the management will learn that I employ comparative analysis in making decisions where there are a lot of choices to make. For example, in this scenario, I have explained the different multivariate analysis techniques and picked just one.

References

Beverly, R., Zapata, F., & Kreinovich, V. (2014). Granularity explains empirical factor-of-three relation between probabilities of pulmonary embolism in different patient categories. 06.

Bustaamante, M. A., Paredes, C., Moyaral, A. M., & Moral, R. (2009). Study of the composting process of winery and distillery wastes using multivariate techniques. 07.

Everitt, B. S., Landau, Abine, Leese, M., & Stahl, D. (2011). [Wiley Series in Probability and Statistics] Cluster Analysis (Everitt/Cluster Analysis) || Optimization Clustering Techniques. 32.

Izmalkov, A. A. (2015). Cluster analysis methodology through the monitoring of the development of the regional agro-industrial complex. 09.

Jameel, A. B., Tasneem, G. K., Abdul, Q. S., Mohammad, B. A., Hassan, I. A., Ghulam, A. K., & Sumaira, K. (2009). Optimization of cloud point extraction and solid phase extraction methods for speciation of arsenic in natural water using multivariate technique. 07.

Jayasinghe, Malith, Tari, Zahir, Zeephongeskul, & Panlop. (2011). 2010 IEEE International Conference on Cluster Computing - Performance Analysis of Multi-level Time Sharing Task Assignment Policies on Cluster-Based Systems. 10.

Jesus, A., Beatriz, B., Beatriz, M.-I., & Justiniano, C.-G. (2011). Plastic identification and comparison by multivariate techniques with laser-induced breakdown spectroscopy. 07.

Kosheleva, O., & Kreinovich, V. (2013). For describing uncertainty, ellipsoids are better than generic polyhedra and probably better than boxes: a remark. 04.

Lapko, A. V. (2010). The analysis of nonparametric mixture properties with a probablity density of a multidimensional random variable. 04.

Li, Wenjuan, Zhang, Oifei, Wu, Jiyi, . . . Haili. (2012). 2012 IEEE International Conference on Cluster Computing Workshops - Trust-Based and QoS Demand Clustering Analysis Customizable Cloud Workflow Scheduling. 09.

Mateu, J., Lorenzo, G., & Porcu, E. (2010). Features detection in spatial point processes via multivariate techniques. 21.

Reddy, P. J., Pulhani, V. B., Kolekar, S. P., Sigh, R. V., Rajvir, J. K., & Pradeepkumar, K. S. (2015). Comparison of rapid liquid scintillation multivariate technique with conventional techniques for regular surveillance of water samples. Journal of Radioanalytical and Nuclear Chemistry, 5.

Ritter, N. (2012). A comparison of distribution-free and non-distribution free methods in factor analysis. aper presented at Southwestern Educational Research Association (SERA) Conference 2012, 12.

Roman, M. B., Ravilya, Z. S., & Ekaterina, I. L. (2010). Gasoline classification using near infrared (NIR) spectroscopy data: Comparison of multivariate techniques. 09.

Suhr, & Diane. (n.d.). Principal Component analysis vs. explanatory factor analysis.

Viktor, S. (2015). Genetic mutations probability reduction by means of heavy water consumption. 03.