Ⅰ. Introduction
The goal of Multivariate statistical analysis(MSA) is dimension reduction of data. Especially, visualization of data is very important. In various methods of MSA, Multidimensional scaling(MDS) is introduced and discussed asa graphical method to complement conventional descriptive and confirmatory methods in validation and analysis of QOLdata. The use of MDS is illustrated i
tools for multidimensional dataanalysis (online analytical processing), and data mining.
Online analytical processing (OLAP), or multidimensional dataanalysis, allows businesses to view a single data in multi-dimensions. Some of the dimensions would be sales regions, cost, and time. Actual example of using online analytical processing would be combining dataabout products and regions. Asa r
CO2 emission. We excluded the innate and natural factors here because those factors are not easy to identify the difference between indicators, additionally when there is homogeneity between countries, those factors cannot be the critical variables to evaluate our model for IQ. The following is the definition of our predictors and the expectation of relationship between the predictors and IQ.
data which is schema-less and unstructured type of dataalready exists for several years. But lack of mgt capability on huge and unstructured data, it was thrown out or used asa sample. However as tech grows, there’s a possibility of analyze Big data.
Big data can be used to create added value on business.
Therefore, the importance of analytics is growing faster than at any time in histo
for opinions of professionals in favor and against the establishment of this system, which had ultimately shaped the hypothesis of our work. Third, in order to verify or reject the hypothesis, we have looked into previous studies and at the same time applied the results made from survey, interviews, and direct observations of the programs in the English Villages, as instruments of data collection