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Data Analysis for Physical Scientists: Featuring Excel® 2nd Revised edition [Kõva köide]

(University of Technology, Sydney)
  • Formaat: Hardback, 528 pages, kõrgus x laius x paksus: 253x180x29 mm, kaal: 1180 g, Worked examples or Exercises; 36 Halftones, unspecified; 113 Line drawings, unspecified
  • Ilmumisaeg: 16-Feb-2012
  • Kirjastus: Cambridge University Press
  • ISBN-10: 0521883725
  • ISBN-13: 9780521883726
  • Formaat: Hardback, 528 pages, kõrgus x laius x paksus: 253x180x29 mm, kaal: 1180 g, Worked examples or Exercises; 36 Halftones, unspecified; 113 Line drawings, unspecified
  • Ilmumisaeg: 16-Feb-2012
  • Kirjastus: Cambridge University Press
  • ISBN-10: 0521883725
  • ISBN-13: 9780521883726
"Thorough analysis of experimental data frequently requires extensive numerical manipulation. Many tools exist to assist in the analysis of data, ranging from the pocket calculator to specialist computer based statistics packages. Despite limited editing and display options, the pocket calculator remains a well-used tool for basic analysis due to its low cost, convenience and reliability. Intensive data analysis may require a statistics package such as Systat or Origin . As well as standard functions, such as those used to determine means and standard deviations, these packages possess advanced features routinely required by researchers and professionals. Between the extremes of the pocket calculator and specialised statistics package is the spreadsheet.While originally designed for business users, spreadsheet packages are popular with other users due to their accessibility, versatility and ease of use. The inclusion of advanced features into spreadsheets means that, in many situations, a spreadsheet isa viable alternative to a statistics package"--

Provided by publisher.

Arvustused

Reviews of the first edition: 'This book is extremely well structured. It both describes the main functionality of Excel with special emphasis on scientific data analysis, as well as the statistical background to the methods definitely one of the best on the market in this important area the author should be congratulated on doing a wonderful job.' Richard Brereton, Chemistry Industry 'Overall, I found the book excellent.' S. Middleton, The Physicist Review of previous edition: ' a simple and straightforward introduction to the use of spreadsheet calculations and data display The coverage of this book will be more than adequate for undergraduate courses and will be sufficient for many postgraduate and research readers a good introduction to Excel® for data analysis for the first time user and covers the data analysis methods that most physical scientists will need.' B. W. James, Contemporary Physics 'The fundamental observation of experimental science is that repeated measurements of the same quantity by the same person using the same equipment do not repeatedly give the same value. Thus, statistical analysis of experimental data is a large part of experimental physics [ This book] deals with all these problems at a level appropriate to an undergraduate course on data analysis. introduces useful ideas and many examples on data visualisation, with examples chosen from many fields of science data distributions, and their results are then exploited in a very extensive discussion of experimental errors. and provides useful information on how to combine random and calibration errors The rest of the book applies these statistical methods to the bread and butter of experimental data analysis - linear and non-linear least squares curve fitting, and hypothesis testing. The theoretical basis is given, and its numerical implementation through Excel® described.' P. T. Greenland, Contemporary Physics

Muu info

Introducing data analysis techniques to help undergraduate students develop the tools necessary for studying and working in the physical sciences.
Preface to the second edition xi
Preface to the first edition xiii
1 Introduction to scientific data analysis
1(39)
1.1 Introduction
1(1)
1.2 Scientific experimentation
2(3)
1.3 The vocabulary of measurement
5(1)
1.4 Units and standards
6(7)
1.5 Picturing experimental data
13(8)
1.6 Key numbers summarise experimental data
21(5)
1.7 Population and sample
26(7)
1.8 Experimental error
33(2)
1.9 Modern tools of data analysis - the computer based spreadsheet
35(1)
1.10 Review
35(5)
End of chapter problems
36(4)
2 Excel and data analysis
40(50)
2.1 Introduction
40(1)
2.2 What is a spreadsheet?
41(1)
2.3 Introduction to Excel
42(20)
2.4 Built in mathematical functions
62(2)
2.5 Built in statistical functions
64(4)
2.6 Presentation options
68(2)
2.7 Charts in Excel
70(7)
2.8 Data analysis tools
77(7)
2.9 Review
84(6)
End of chapter problems
84(6)
3 Data distributions I
90(56)
3.1 Introduction
90(1)
3.2 Probability
91(2)
3.3 Probability distributions
93(5)
3.4 Distributions of real data
98(3)
3.5 The normal distribution
101(10)
3.6 From area under a normal curve to an interval
111(8)
3.7 Distribution of sample means
119(2)
3.8 The central limit theorem
121(5)
3.9 The t distribution
126(7)
3.10 The log-normal distribution
133(2)
3.11 Assessing the normality of data
135(2)
3.12 Population mean and continuous distributions
137(2)
3.13 Population mean and expectation value
139(1)
3.14 Review
140(6)
End of chapter problems
140(6)
4 Data distributions II
146(22)
4.1 Introduction
146(1)
4.2 The binomial distribution
146(11)
4.3 The Poisson distribution
157(8)
4.4 Review
165(3)
End of chapter problems
165(3)
5 Measurement, error and uncertainty
168(58)
5.1 Introduction
168(3)
5.2 The process of measurement
171(3)
5.3 True value and error
174(2)
5.4 Precision and accuracy
176(1)
5.5 Random and systematic errors
177(1)
5.6 Random errors
178(11)
5.7 Uncertainty in measurement
189(10)
5.8 Combining uncertainties
199(9)
5.9 Expanded uncertainty
208(5)
5.10 Relative standard uncertainty
213(1)
5.11 Coping with outliers
214(4)
5.12 Weighted mean
218(3)
5.13 Review
221(5)
End of chapter problems
221(5)
6 Least squares I
226(71)
6.1 Introduction
226(1)
6.2 The equation of a straight line
227(13)
6.3 Excel's LINEST() function
240(3)
6.4 Using the line of best fit
243(9)
6.5 Fitting a straight line to data when random errors are confined to the x quantity
252(1)
6.6 Linear correlation coefficient, r
253(8)
6.7 Residuals
261(5)
6.8 Data rejection
266(4)
6.9 Transforming data for least squares analysis
270(7)
6.10 Weighted least squares
277(7)
6.11 Review
284(13)
End of chapter problems
285(12)
7 Least squares II
297(38)
7.1 Introduction
297(1)
7.2 Extending linear least squares
298(2)
7.3 Formulating equations to solve for parameter estimates
300(2)
7.4 Matrices and Excel
302(6)
7.5 Fitting equations with more than one independent variable
308(4)
7.6 Standard uncertainties in parameter estimates
312(4)
7.7 Weighting the fit
316(2)
7.8 Coefficients of multiple correlation and multiple determination
318(2)
7.9 Estimating more than two parameters using the LINEST() function
320(2)
7.10 Choosing equations to fit to data
322(5)
7.11 Review
327(8)
End of chapter problems
328(7)
8 Non-linear least squares
335(47)
8.1 Introduction
335(3)
8.2 Excel's Solver add-in
338(14)
8.3 More on fitting using non-linear least squares
352(6)
8.4 Weighted non-linear least squares
358(8)
8.5 More on Solver
366(4)
8.6 Review
370(12)
End of chapter problems
371(11)
9 Tests of significance
382(46)
9.1 Introduction
382(1)
9.2 Hypothesis testing
383(9)
9.3 Comparing χ with μ0 when sample sizes are small
392(2)
9.4 Significance testing for least squares parameters
394(3)
9.5 Comparison of the means of two samples
397(8)
9.6 Comparing variances using the F test
405(5)
9.7 Comparing expected and observed frequencies using the Χ2 test
410(8)
9.8 Analysis of variance
418(5)
9.9 Review
423(5)
End of chapter problems
423(5)
10 Data Analysis tools in Excel and the Analysis ToolPak
428(16)
10.1 Introduction
428(1)
10.2 Activating the Data Analysis tools
429(2)
10.3 Anova: Single Factor
431(1)
10.4 Correlation
432(1)
10.5 F-test two-sample for variances
433(1)
10.6 Random Number Generation
434(1)
10.7 Regression
435(4)
10.8 t tests
439(1)
10.9 Other tools
440(3)
10.10 Review
443(1)
Appendix 1 Statistical tables 444(9)
Appendix 2 Propagation of uncertainties 453(2)
Appendix 3 Least squares and the principle of maximum likelihood 455(6)
Appendix 4 Standard uncertainties in mean, intercept and slope 461(5)
Appendix 5 Introduction to matrices for least squares analysis 466(5)
Appendix 6 Useful formulae 471(4)
Answers to exercises and end of chapter problems 475(27)
References 502(4)
Index 506
Les Kirkup is Associate Professor in the School of Physics and Advanced Materials, University of Technology, Sydney. A dedicated lecturer, many of his educational developments have focused on enhancing the laboratory experience of undergraduate students. In May 2011, he was awarded an Australian Learning and Teaching Council National Teaching Fellowship.