Statistical Thinking and Model Building
Statistical Thinking and Model Building
In the evolution of any scientific discipline there is a period in which attempts are made to develop mathematical theories in order to account for and explain the observations generated by the phenomena with which the discipline is concerned. During this period, the qualitative and verbal theories are replaced, or supplemented, by quantitative and mathematical theories which express, in the form of some type of equation or equations, a postulated mechanism or model that can generate a theoretical set of observations.
The theoretical and experimental observations can then be compared in order to see if the model is a reasonable one, i.e., if it is capable of accounting for the experimental observation obtained under the conditions stipulated by the data analysis. Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains.
When analyzing the data , we can select one of two approaches to the study of the phenomenon concerned. These two approaches, which are termed deterministic and stochastic (or probabilistic), reflect the casual nature of the postulated mechanism (or model) which we express in mathematical form. It is not of great interest to ask if the phenomenon with which we are concerned is deterministic or stochastic. We are concerned with the analysis of a variable or stochastic model, the investigation of its properties, and its ability to account for experimental observations. The analysis of data or model building involve huge computational complexities. To reduce the burden of computational complexities statistical packages are developed.
The term s data analysis refers to the mathematical abstraction, pattern, or representation of the empirical process. Statistical analysis is for the purpose of estimating the probability of outcomes within a forecast to predict what conditions might be like under different situations. The random variables are usually constrained by historical data, such as past data. The word “stochastic” derives from the Greed to aim, to guess and means “random” or “chance.” The antonym is “sure,” “deterministic,” or “certain.” In the last five decades the statistical packages have been developed very rapidly and has found application in a large number of fields.
In particular, a class of tools and techniques with computational simplicities termed as statistical packages has been investigated rather extensively. It is of great importance in many branches of science , engineering, commerce , Management and in other fields. SPSS is one of the most commonly used statistical package for data analysis. SPSS refers “Statistical product and service solutions ”. Earlier it is known as Statistical package for social Sciences. SPSS is a comprehensive and flexible statistical analysis and data management system. SPSS can take data from almost any type of file and use them to generate tabulated reports, charts, and plots of distributions and trends, descriptive statistics, and conduct complex statistical analyses.
Some application of statistical analysis in engineering are : Image Processing and Image Retrievals, Statistical quality control, statistical signal processing, Bio-Informatics, AI and Robotics, Industrial Engineering , CAD/CAM , etc. The applications of statistical data analysis in sciences range from Statistical genetics to Engineering Physics. The applications of Statistical analysis in sychology and Behavioral Sciences have generated a worthy applied area of psychometrics. With the evaluation of modern computational facilities, the present day creation and innovation is surrounding through statistical packages and its applications. Statistical analysis is a way of quantifying the dynamic relationships of sequences of random events. Statistical packages play an important role in elucidating many areas of the natural and engineering sciences. They can be used to analyze the variability inherent in any process, to deal with uncertainties affecting managerial decisions and with the complexities of psychological and social interactions, and to provide new perspectives, methodology, models, and intuition to aid in other statistical studies.