Principles of Statistics for Data Analysis
Statistical principles provide the foundation for analysing data in a structured and meaningful way. They enable analysts to move beyond raw figures, supporting deeper understanding of patterns, relationships, and variability within datasets used in real-world contexts.
Technique Overview
Principles of Statistics for Data Analysis Definition
Statistical principles refer to the core methods used to collect, summarise, analyse, and interpret data. These include descriptive statistics, probability, data distributions, and inferential techniques, which enable analysts to identify patterns, quantify uncertainty, and draw valid conclusions from data (Freedman, Pisani and Purves, 2007; Moore, McCabe and Craig, 2017).
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Business Evidence
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Further Reading
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Principles of Statistics for Data Analysis references (4 of up to 20) *
- Davenport, T.H. and Harris, J.G. (2017) Competing on Analytics: The New Science of Winning. Updated edn. Boston, MA: Harvard Business Review Press.
- De Veaux, R.D., Velleman, P.F. and Bock, D.E. (2018) Stats: Data and Models. 5th edn. Harlow: Pearson.
- Diez, D.M., Barr, C.D. and Çetinkaya-Rundel, M. (2019) OpenIntro Statistics. 4th edn
- Freedman, D., Pisani, R. and Purves, R. (2007) Statistics. 4th edn. New York: W.W. Norton & Company.
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