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Statistics for scientists = a concis...

Michelucci, Umberto.

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Statistics for scientists = a concise guide for data-driven research /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Statistics for scientists/ by Umberto Michelucci.
Reminder of title:	a concise guide for data-driven research /
Author:	Michelucci, Umberto.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xxiv, 167 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Introduction to Statistics -- Types of Data -- Data Collection Methods (Sampling Theory) -- Measures of Central Tendency -- Measures of Dispersion -- Measures of Positions -- Outliers -- Introduction to Distributions -- Skewness, Kurtosis and Modality -- Data Visualisation -- Confidence Intervals -- Hypothesis Testing -- Correlation and Linear Regression -- Statistical Project - Steps and Process -- Appendix A - Partioning of the Ordinary Least Square Variance -- Appendix B - Big-O and Little-o Notation.
Contained By:	Springer Nature eBook
Subject:	Mathematical analysis - Statistical methods. -
Online resource:	https://doi.org/10.1007/978-3-031-78147-6
ISBN:	9783031781476

Statistics for scientists = a concise guide for data-driven research /
Michelucci, Umberto.

Statistics for scientistsa concise guide for data-driven research /[electronic resource] :by Umberto Michelucci. - Cham :Springer Nature Switzerland :2025. - xxiv, 167 p. :ill. (some col.), digital ;24 cm.

Introduction to Statistics -- Types of Data -- Data Collection Methods (Sampling Theory) -- Measures of Central Tendency -- Measures of Dispersion -- Measures of Positions -- Outliers -- Introduction to Distributions -- Skewness, Kurtosis and Modality -- Data Visualisation -- Confidence Intervals -- Hypothesis Testing -- Correlation and Linear Regression -- Statistical Project - Steps and Process -- Appendix A - Partioning of the Ordinary Least Square Variance -- Appendix B - Big-O and Little-o Notation.

This book offers researchers and practitioners a concise and accessible guide to the essential concepts in statistics, emphasizing their proper application. It encourages readers to delve deeper into the fascinating field of statistics, a branch of mathematics that enhances our understanding of the world around us. Designed to provide enough material for a short introductory course, Statistics for Scientists caters to students at all levels. It emphasizes real-world applications, providing scientists with the tools they need to conduct more reliable and valid studies, ultimately contributing to the advancement of scientific knowledge. Learn to interpret statistical results accurately and draw meaningful conclusions from your data, significantly contributing to the advancement of scientific knowledge. Structured to deliver a clear overview of statistics and data analysis for scientific research, the book begins with fundamental concepts, including random variables, outcome spaces, and the distinction between descriptive and inferential statistics. It then explores data types, measures of central tendency, dispersion, and position. The discussion continues with an examination of outliers and various methods for identifying them. As the chapters progress, more complex topics such as distributions, hypothesis testing, and regression analysis are introduced in a step-by-step manner. This structure makes the book suitable for readers ranging from beginners to those seeking a quick refresher. The author has selected key concepts that anyone interested in using statistics should be familiar with. Some topics, such as hypothesis testing, are covered briefly; a more comprehensive treatment would require a stronger background in statistics and mathematics (such as calculus). With pedagogical elements that include text boxes with Definitions, Examples, and Warnings, this book introduces the necessary concepts of statistics for scientists described in a short and concise way, enriched with tips and rigorous explanations. This book is an invaluable resource for scientists seeking to improve their data analysis skills and contribute to the growing body of scientific knowledge through rigorous and reliable research.

ISBN: 9783031781476

Standard No.: 10.1007/978-3-031-78147-6doiSubjects--Topical Terms:

3592081
Mathematical analysis
--Statistical methods.

LC Class. No.: QA276.4

Dewey Class. No.: 519.50285

Statistics for scientists = a concise guide for data-driven research /
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505 0 $a Introduction to Statistics -- Types of Data -- Data Collection Methods (Sampling Theory) -- Measures of Central Tendency -- Measures of Dispersion -- Measures of Positions -- Outliers -- Introduction to Distributions -- Skewness, Kurtosis and Modality -- Data Visualisation -- Confidence Intervals -- Hypothesis Testing -- Correlation and Linear Regression -- Statistical Project - Steps and Process -- Appendix A - Partioning of the Ordinary Least Square Variance -- Appendix B - Big-O and Little-o Notation.
520 $a This book offers researchers and practitioners a concise and accessible guide to the essential concepts in statistics, emphasizing their proper application. It encourages readers to delve deeper into the fascinating field of statistics, a branch of mathematics that enhances our understanding of the world around us. Designed to provide enough material for a short introductory course, Statistics for Scientists caters to students at all levels. It emphasizes real-world applications, providing scientists with the tools they need to conduct more reliable and valid studies, ultimately contributing to the advancement of scientific knowledge. Learn to interpret statistical results accurately and draw meaningful conclusions from your data, significantly contributing to the advancement of scientific knowledge. Structured to deliver a clear overview of statistics and data analysis for scientific research, the book begins with fundamental concepts, including random variables, outcome spaces, and the distinction between descriptive and inferential statistics. It then explores data types, measures of central tendency, dispersion, and position. The discussion continues with an examination of outliers and various methods for identifying them. As the chapters progress, more complex topics such as distributions, hypothesis testing, and regression analysis are introduced in a step-by-step manner. This structure makes the book suitable for readers ranging from beginners to those seeking a quick refresher. The author has selected key concepts that anyone interested in using statistics should be familiar with. Some topics, such as hypothesis testing, are covered briefly; a more comprehensive treatment would require a stronger background in statistics and mathematics (such as calculus). With pedagogical elements that include text boxes with Definitions, Examples, and Warnings, this book introduces the necessary concepts of statistics for scientists described in a short and concise way, enriched with tips and rigorous explanations. This book is an invaluable resource for scientists seeking to improve their data analysis skills and contribute to the growing body of scientific knowledge through rigorous and reliable research.
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