Theory, Practice and Techniques in Statistics

These days statistical methods are used everywhere. There is no area of work in which statistical methods is not applied. The significance of the statistical science is developing in almost all domains of knowledge, such as, astronomy, biology, meteorology, demography, economics and mathematics. Economic planning without statistics is bound to be baseless. Statistics assist in administration, and simplify the work of formulation of new policies. Financial organizations and investors apply statistical data to outlines the past experience. Statistics are also supportive to an auditor, when he applies sampling techniques to audit the accounts of his clients or customers. Statistics play an important role in business assisting entrepreneur to plan production permitting to the choice of the costumers, and product quality can also be checked more proficiently applying statistical methods. So, all the activities of the entrepreneur depend on information provided by the statistics. An entrepreneur can make right decision about the business location, products marketing, and financial resources etc.
This book “Theory, Practice and Techniques in Statistics” discusses covers a much wider range of topics than a typical introductory text on mathematical statistics, emphasizing the applications of statistics and probability to finance. It highlights the significance of statistical methods in everyday applications. The significance of statistics in business and industry links the gap between the tools of statistics and their use in today’s business world.

Suitable for students in statistics, computer science, data mining and machine learning this book will help in:

• Proper and efficient planning of a statistical inquiry in any field of study.
• Collecting an appropriate quantitative data.
• Presenting complex data in a suitable tabular, diagrammatic and graphic form for an easy and clear comprehension of the data.
• Understanding the nature and pattern of variability of a phenomenon through quantitative observations.
• Drawing valid inference, along with a measure of their reliability about the population parameters from the sample data.