Abstract
In this chapter, we will become familiar with descriptive statistics that is comprised of concepts, terms, measures, and tools that help to describe, show, and summarize data in a meaningful way. When analyzing data, it is possible to use both descriptive and inferential statistics in order to analyze the results and draw some conclusions. We will discuss basic concepts, terms, and procedures, such as mean, median, variance, correlation, etc., to explore, describe, and summarize a given set of data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
We will use the following notation: X is a random variable, \(\mathbf {x}\) is a column vector, \(\mathbf {x}^T\) (the transpose of \(\mathbf {x}\)) is a row vector, \(\mathbf {X}\) is a matrix, and \(x_i\) is the i-th element of a dataset.
- 3.
References
A. B. Downey, “Probability and Statistics for Programmers”, O’Reilly Media, 2011, ISBN-10: 1449307116.
Probability Distributions: Discrete vs. Continuous, http://stattrek.com/probability-distributions/discrete-continuous.aspx.
Acknowledgements
This chapter was co-written by Petia Radeva and Laura Igual.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2017 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Igual, L., Seguí, S. (2017). Descriptive Statistics. In: Introduction to Data Science. Undergraduate Topics in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-50017-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-50017-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50016-4
Online ISBN: 978-3-319-50017-1
eBook Packages: Computer ScienceComputer Science (R0)