International Journal of Business Analytics and Intelligence

Kamakshaiah Musunuru ¹

Affiliations
1 Business Analytics, Dhruva College of Management, Hyderabad, IN

Abstract

R is not only a statistical suite but also efficient data mining software for data manipulation, calculation and graphical display. In fact, R being a language also has an effective data handling and storage facility. Besides having a suite of operators for calculations on arrays, in particular matrices. The R is developed from a simple and effective programming language (called “S”) which includes conditionals, loops; user defined recursive functions and input and output facilities. Methods: In this paper the data mining capabilities of R has been explained with the help of a study on secondary data sources, obtained from certain authenticated sources. The study is all about to understand stress with respect to certain other factors like heavy drinking, perceived health and life satisfaction. As it mentioned the data so used is secondary in nature, which is in its crude from having no sense to the user. But by a systematic execution of certain data mining tools, like correlation and MANOVA, certain important relationships along with ties were realized. Conclusions: The realizations were that all variables are strictly correlated with Karl Pearson correlation coefficient ranging from 0.73 to 0.99. In significant test all variables do not belie with alternative hypothesis, which means the association/ relationship is not zero. In MANOVA, the null hypothesis is rejected as the p-value is less than 0.05. Apart from this, most interestingly the variables are behaving like cohorts whereby resulting cohort effect.

Keywords

R Language, Rstudio, Secondary Data, Data Mining, Correlation, Manova

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Kamakshaiah Musunuru ¹, S. S. Prasada Rao ²

Affiliations
1 GITAM School of International Business, GITAM University, Visakhapatnam, Andhra Pradesh, IN
2 GITAM Institute of Management, GITAM University, Visakhapatnam, Andhra Pradesh, IN

Abstract

The goal of this research is to study the performance of foreign exchange trade in both India and China. India and China raised rapidly in recent times and there is abundant of speculation that these countries might reach to the level of few other developed nations as far as international trade is concerned. Whereas there isn’t any doubt that these countries emerging as economic powers in the Asia-Pacific region, a lot of effort is required at international platform with respect to trade and commerce. One of such areas of competition is international currency trade. The aim of this study is to understand trends of currency trade in order to predict how likely these countries are going to emerge as best in the region. The study used certain secondary datasets from very reliable and authenticated sources. As far as statistical techniques are concerned, random walk forecasting methods were employed to test the study hypothesis. The study gathered certain evidence that though there are similarities in present and past performance, it is not likely to be the same in the future. However, the study concludes that random forests forecasting as a methodology is highly useful in studying trends in the data.

Keywords

Asia-Pacific Region, Currency Trade, International Trade, Random Walk Forecasting, Time Series Analysis.

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