The outcome of the multivariate investigation allows identification of the multidimensional design space within which the process is not impacting the process performance and product quality attributes. The multivariate data is modeled into a mathematical equation that can predict the best optimal settings for the process and the effect of the excursions of the process parameters on the process performance and the product quality. The chemometrics, in turn, employs multivariate mathematical and statistical tools in combination with computational techniques to investigate the effect of multiple factors on the optimality of the process and product attributes. OFAT studies may or may not give the optimal settings for the process or the product attributes.
ICS describes chemometrics as the chemical discipline that uses mathematical and statistical models toĪ) d esign or select optimal measurement procedures and experiments, andī) to provide maximum chemical information by analyzing chemical data. Later in 1974, Svante Wold and Bruce Kowalski founded the International Chemometrics Society (ICS). Coined by a Swedish Scientist, Svante Wold, in 1972. The ways to perform analysis on this data depends on the goals to be achieved.Some of the techniques are regression analysis,path analysis,factor analysis and multivariate analysis of variance (MANOVA).“ Chemometrics” is a combination of two words “ chemo” and “ metrics” which signifies the application of computational tools to Chemical Sciences. It is similar to bivariate but contains more than one dependent variable. Example of this type of data is suppose an advertiser wants to compare the popularity of four advertisements on a website, then their click rates could be measured for both men and women and relationships between variables can then be examined. When the data involves three or more variables, it is categorized under multivariate. These variables are often plotted on X and Y axis on the graph for better understanding of data and one of these variables is independent while the other is dependent. Thus bivariate data analysis involves comparisons, relationships, causes and explanations. Here, the relationship is visible from the table that temperature and sales are directly proportional to each other and thus related because as the temperature increases, the sales also increase. Suppose the temperature and ice cream sales are the two variables of a bivariate data(figure 2). The analysis of this type of data deals with causes and relationships and the analysis is done to find out the relationship among the two variables.Example of bivariate data can be temperature and ice cream sales in summer season. This type of data involves two different variables. The description of patterns found in this type of data can be made by drawing conclusions using central tendency measures (mean, median and mode), dispersion or spread of data (range, minimum, maximum, quartiles, variance and standard deviation) and by using frequency distribution tables, histograms, pie charts, frequency polygon and bar charts. Suppose that the heights of seven students of a class is recorded(figure 1),there is only one variable that is height and it is not dealing with any cause or relationship. The example of a univariate data can be height. It does not deal with causes or relationships and the main purpose of the analysis is to describe the data and find patterns that exist within it. The analysis of univariate data is thus the simplest form of analysis since the information deals with only one quantity that changes. This type of data consists of only one variable.