# Principles of Statistics and Reporting of Data

• Anju Agrawal
• Krishna Gopal
Chapter

## Abstract

Statistics is the study of the collection, organisation and interpretation of data. Statistics is considered to be a mathematical science, and it pertains to the collection of data, analysis, interpretation or explanation and presentation of data, while others consider it a branch of mathematics concerned with collecting and interpreting data. As it has its empirical roots and its focus on applications, statistics is usually considered to be a distinct mathematical science rather than a branch of mathematics. Statisticians improve the quality of data with the designing of experiments and survey sampling. Statistics also provides tools for prediction and forecasting using data and statistical models. In applying statistics to a scientific, industrial or societal problem, it is necessary to begin with a population or process to be studied. Populations can be diverse topics such as ‘all persons living in a country’ or ‘every atom composing a crystal’. A population can also be composed of observations of a process at various times, with the data from each observation serving as a different member of the overall group. Data collected about this kind of ‘population’ constitutes what is called a time series. Traditionally, statistics was concerned with drawing inferences using a semi-standardised methodology that was ‘required learning’ in most sciences. This has changed with use of statistics in non-inferential contexts. What was once considered a dry subject, taken in many fields as a degree requirement, is now viewed enthusiastically. Initially derided by some mathematical purists, it is now considered essential methodology in certain areas.

## Keywords

Statistical Process Control Geographic Information System Ordinal Measurement Diverse Topic Drawing Inference
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

## References

1. Breiman L (2001) Statistical modelling: the two cultures. Stat Sci 16(3):199–231. doi:10.1214/ss/1009213726 DOI:10.1214%2Fss%2F1009213726 MR1874152
2. Chance BL, Rossman AJ (2005) Preface. In: Investigating statistical concepts, applications, and methods. Duxbury Press, Pacific Grove. ISBN 978-0-495-05064-3. http://www.rossmanchance.com/iscam/preface.pdf
3. Dodge Y (2003) The Oxford dictionary of statistical terms. Oxford University Press, Oxford. ISBN 0-19-920613-9Google Scholar
4. Hays WL (1973) Statistics for the social sciences. Holt, Rinehart and Winston, p xii. ISBN 978-0-03-077945-9Google Scholar
5. Moore D (1992) Teaching statistics as a respectable subject. In: Gordon F, Gordon S (eds) Statistics for the twenty-first century. Washington, DC: The Mathematical Association of America, pp 14–25. ISBN 978-0-88385-078-7Google Scholar
6. Moses LE (1986) Think and explain with statistics. Addison-Wesley, Boston, pp 1–3. ISBN 978-0-201-15619-5Google Scholar
7. Thompson B (2006) Foundations of behavioral statistics. Guilford Press, New YorkGoogle Scholar