Student Solutions to Accompany Taylor’s An Introduction to Error Analysis, 3rd ed

John R. Taylor is Emeritus Professor of Physics and Presidential Teaching Scholar at the University of Colorado at Boulder. He received his B.A. in mathematics from Cambridge University and his Ph.D. in physics from the University of California at Berkeley. In 1966, he joined the faculty at Colorado, where his research interests include quantum scattering theory and the foundations of quantum theory. Professor Taylor has won numerous teaching awards, served as Associate Editor of the American Journal of Physics, received an Emmy Award for his television series called “Physics 4 Fun,” and given “Mr. Wizard” lectures to some 40,000 school children throughout Colorado. He has written three internationally best-selling textbooks, including the landmark Classical Mechanics, Introduction to Error Analysis, and Modern Physics for Scientists and Engineers.Michael R. Buche is a scientist and engineer at Sandia National Laboratories. He received his BS in Mechanical Engineering from the University of Notre Dame in 2017, and his MS and PhD in Theoretical and Applied Mechanics from Cornell University in 2020 and 2021. His research and development interests include computational mechanics, constitutive theory, engineering analysis, and statistical thermodynamics. He enjoys writing solutions to excellent textbooks in order to learn from them as well as honor them.Peter N. Saeta is Professor of Physics at Harvey Mudd College, the science and engineering college of the Claremont Colleges. He earned his B.S. in physics from Stanford University and Ph.D. from Harvard University, where he was an AT&T Fellow. He worked on terahertz spectroscopy as a post-doctoral fellow at Bell Laboratories and on silicon luminescence as a NRC post-doc at JILA. At HMC he has worked in the fields of nonlinear optics and ultrafast science, in addition to teaching a broad range of theory and laboratory courses. He has also conducted research on ultrafast magnetic response at IPCMS in Strasbourg and on plasmonic enhancement to photovoltaic efficiency at Caltech.

• Preface; List of Spreadsheets; List of Python Examples1 Preliminary Description of Error Analysis2 How to Report and Use Uncertainties3 Propagation of Uncertainties4 Statistical Analysis of Random Uncertainties5 The Normal Distribution6 Rejection of Data7 Weighted Averages8 Least-Squares Fitting9 Covariance and Correlation10 The Binomial Distribution11 The Poisson Distribution12 The Chi-Squared Test for a Distribution13 Bayesian StatisticsS.A SpreadsheetsS.A.1 Cells and RangesS.A.2 Significant FiguresS.A.3 FunctionsS.A.4 Example: Plotting LorentziansS.A.5 Statistical FunctionsS.A.6 Linear RegressionS.B PythonS.B.1 InstallationS.B.2 Coding EnvironmentS.B.3 Getting HelpS.B.4 Example: Plotting LorentziansS.B.5 Statistical Functions