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Mathematics and statistics for science [electronic resource] / James Sneyd, Rachel M. Fewster, Duncan McGillivray.

Sneyd, James.; Fewster, Rachel.; McGillivray, Duncan.
2022
QA276
Available Online
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Title
Mathematics and statistics for science [electronic resource] / James Sneyd, Rachel M. Fewster, Duncan McGillivray.
Author
Sneyd, James.
ISBN
9783031053184 (electronic bk.)
3031053184 (electronic bk.)
3031053176
9783031053177
DOI
https://doi.org/10.1007/978-3-031-05318-4
Published
Cham, Switzerland : Springer, 2022.
Language
English
Description
1 online resource
Item Number
10.1007/978-3-031-05318-4 doi
Call Number
QA276
Dewey Decimal Classification
519.5
Summary
Mathematics and statistics are the bedrock of modern science. No matter which branch of science you plan to work in, you simply cannot avoid quantitative approaches. And while you wont always need to know a great deal of theory, you will need to know how to apply mathematical and statistical methods in realistic scenarios. That is precisely what this book teaches. It covers the mathematical and statistical topics that are ubiquitous in early undergraduate courses, but does so in a way that is directly linked to science. Beginning with the use of units and functions, this book covers key topics such as complex numbers, vectors and matrices, differentiation (both single and multivariable), integration, elementary differential equations, probability, random variables, inference and linear regression. Each topic is illustrated with widely-used scientific equations (such as the ideal gas law or the Nernst equation) and real scientific data, often taken directly from recent scientific papers. The emphasis throughout is on practical solutions, including the use of computational tools (such as Wolfram Alpha or R), not theoretical development. There is a large number of exercises, divided into mathematical drills and scientific applications, and full solutions to all the exercises are available to instructors. Mathematics and Statistics for Science covers the core methods in mathematics and statistics necessary for a university degree in science, highlighting practical solutions and scientific applications. Its pragmatic approach is ideal for students who need to apply mathematics and statistics in a real scientific setting, whether in the physical sciences, life sciences or medicine.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed July 18, 2022).
Added Author
Fewster, Rachel.
McGillivray, Duncan.
Available in Other Form
Print version: 9783031053177
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Part I Units and Measurement
1 Units
2 Measurement, rounding and uncertainty
Part II Functions and Complex Numbers
3 Functions
4 Exponential and log functions
5 Periodic functions
6 Linearising functions
7 Complex numbers
Part III Vectors, Matrices and Linear Systems
8 Vectors
9 Matrices
10 Systems of linear equations
11 Solving systems of linear equations using matrices
Part IV Differentiation: Functions of One Variable
12 Limits
13 Differentiation as a limit
14. Differentiation in practice
15 Numerical differentiation
16 Implicit differentiation
17 Maxima and minima
Part V Differentiation: Functions of Multiple Variables
18 Functions of multiple variables
19 Partial derivatives
20 Extreme of functions of two (or more) variables
Part VI Integration
21 The area under a curve
22 Calculating antiderivatives and areas
23 Integration techniques
24 Numerical integration
Part VII Differential Equations
25 First-order ordinary differential equations
26 Numerical solutions of differential equations
Part VIII Probability
27 Probability foundations
28 Random variables
29 Binomial distribution
30 Conditional probability
31 Total probability rule
Part IX Statistical inference
32 Hypothesis test
33 Hypothesis testing in practice
34 Estimation and likelihood
Part X Discrete Probability Distributions
35 Simulation and visualisation
36 Mean
37 Variance
38 Discrete probability models
Part XI Continuous Probability Distributions
39 Continuous random variables
40 Common continuous probability models
41 Normal distribution and inference
Part XII Linear Regression
42 Fitting linear functions: theory and practice
43 Quantifying relationships
References
Index.

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