國立虎尾科技大學 |

Mathematics and Statistics for Science

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Mathematics and Statistics for Science/ by James Sneyd, Rachel M. Fewster, Duncan McGillivray.
作者:	Sneyd, James.
其他作者:	McGillivray, Duncan.
面頁冊數:	XVI, 964 p. 360 illus., 321 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. -
電子資源:	https://doi.org/10.1007/978-3-031-05318-4
ISBN:	9783031053184

Mathematics and Statistics for Science
Sneyd, James.

Mathematics and Statistics for Science[electronic resource] /by James Sneyd, Rachel M. Fewster, Duncan McGillivray. - 1st ed. 2022. - XVI, 964 p. 360 illus., 321 illus. in color.online resource.

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.

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 won’t 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.

ISBN: 9783031053184

Standard No.: 10.1007/978-3-031-05318-4doiSubjects--Topical Terms:

1366002
Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

LC Class. No.: QA1-939

Dewey Class. No.: 510

Mathematics and Statistics for Science
LDR:04870nam a22003855i 4500 001 1087844
003 DE-He213
005 20220627215334.0
007 cr nn 008mamaa
008 221228s2022 sz | s |||| 0|eng d
020 $a 9783031053184 $9 978-3-031-05318-4
024 7 $a 10.1007/978-3-031-05318-4 $2 doi
035 $a 978-3-031-05318-4
050 4 $a QA1-939
072 7 $a PB $2 bicssc
072 7 $a MAT000000 $2 bisacsh
072 7 $a PB $2 thema
082 0 4 $a 510 $2 23
100 1 $a Sneyd, James. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 676244
245 1 0 $a Mathematics and Statistics for Science $h [electronic resource] / $c by James Sneyd, Rachel M. Fewster, Duncan McGillivray.
250 $a 1st ed. 2022.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a XVI, 964 p. 360 illus., 321 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a 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.
520 $a 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 won’t 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.
650 2 4 $a Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 1366002
650 2 4 $a Statistics. $3 556824
650 2 4 $a Applications of Mathematics. $3 669175
650 1 4 $a General Mathematics. $3 1365847
650 0 $a Statistics . $3 1253516
650 0 $a Mathematics. $3 527692
700 1 $a McGillivray, Duncan. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1394937
700 1 $a Fewster, Rachel M. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1394936
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783031053177
776 0 8 $i Printed edition: $z 9783031053191
856 4 0 $u https://doi.org/10.1007/978-3-031-05318-4
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)