Data Analysis for Physical Scientists 2nd Edition by Les Kirkup – Ebook PDF Instant Download/Delivery: 0521883725, 9780521883726
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ISBN 10: 0521883725
ISBN 13: 9780521883726
Author: Les Kirkup
The ability to summarise data, compare models and apply computer-based analysis tools are vital skills necessary for studying and working in the physical sciences. This textbook supports undergraduate students as they develop and enhance these skills. Introducing data analysis techniques, this textbook pays particular attention to the internationally recognised guidelines for calculating and expressing measurement uncertainty. This new edition has been revised to incorporate Excel® 2010. It also provides a practical approach to fitting models to data using non-linear least squares, a powerful technique which can be applied to many types of model. Worked examples using actual experimental data help students understand how the calculations apply to real situations. Over 200 in-text exercises and end-of-chapter problems give students the opportunity to use the techniques themselves and gain confidence in applying them. Answers to the exercises and problems are given at the end of the book.
Data Analysis for Physical Scientists 2nd Table of contents:
Chapter 1: Introduction to Scientific Data Analysis
1.1 Introduction
1.2 Scientific Experimentation
1.3 The Vocabulary of Measurement
1.4 Units and Standards
1.5 Picturing Experimental Data
1.6 Key Numbers Summarise Experimental Data
1.7 Population and Sample
1.8 Experimental Error
1.9 Modern Tools of Data Analysis – The Computer Based Spreadsheet
1.10 Review
End of Chapter Problems
Chapter 2: Excel and Data Analysis
2.1 Introduction
2.2 What is a Spreadsheet?
2.3 Introduction to Excel
2.4 Built-in Mathematical Functions
2.5 Built-in Statistical Functions
2.6 Presentation Options
2.7 Charts in Excel
2.8 Data Analysis Tools
2.9 Review
End of Chapter Problems
Chapter 3: Data Distributions I
3.1 Introduction
3.2 Probability
3.3 Probability Distributions
3.4 Distributions of Real Data
3.5 The Normal Distribution
3.6 From Area Under a Normal Curve to an Interval
3.7 Distribution of Sample Means
3.8 The Central Limit Theorem
3.9 The t Distribution
3.10 The Log-normal Distribution
3.11 Assessing the Normality of Data
3.12 Population Mean and Continuous Distributions
3.13 Population Mean and Expectation Value
3.14 Review
End of Chapter Problems
Chapter 4: Data Distributions II
4.1 Introduction
4.2 The Binomial Distribution
4.3 The Poisson Distribution
4.4 Review
End of Chapter Problems
Chapter 5: Measurement, Error and Uncertainty
5.1 Introduction
5.2 The Process of Measurement
5.3 True Value and Error
5.4 Precision and Accuracy
5.5 Random and Systematic Errors
5.6 Random Errors
5.7 Uncertainty in Measurement
5.8 Combining Uncertainties
5.9 Expanded Uncertainty
5.10 Relative Standard Uncertainty
5.11 Coping with Outliers
5.12 Weighted Mean
5.13 Review
End of Chapter Problems
Chapter 6: Least Squares I
6.1 Introduction
6.2 The Equation of a Straight Line
6.3 Excel’s LINEST() Function
6.4 Using the Line of Best Fit
6.5 Fitting a Straight Line to Data When Random Errors Are Confined to the x Quantity
6.6 Linear Correlation Coefficient, r
6.7 Residuals
6.8 Data Rejection
6.9 Transforming Data for Least Squares Analysis
6.10 Weighted Least Squares
6.11 Review
End of Chapter Problems
Chapter 7: Least Squares II
7.1 Introduction
7.2 Extending Linear Least Squares
7.3 Formulating Equations to Solve for Parameter Estimates
7.4 Matrices and Excel
7.5 Fitting Equations with More Than One Independent Variable
7.6 Standard Uncertainties in Parameter Estimates
7.7 Weighting the Fit
7.8 Coefficients of Multiple Correlation and Multiple Determination
7.9 Estimating More Than Two Parameters Using the LINEST() Function
7.10 Choosing Equations to Fit to Data
7.11 Review
End of Chapter Problems
Chapter 8: Non-linear Least Squares
8.1 Introduction
8.2 Excel’s Solver Add-in
8.3 More on Fitting Using Non-linear Least Squares
8.4 Weighted Non-linear Least Squares
8.5 More on Solver
8.6 Review
End of Chapter Problems
Chapter 9: Tests of Significance
9.1 Introduction
9.2 Hypothesis Testing
9.3 Comparing x̄ with μ0 When Sample Sizes Are Small
9.4 Significance Testing for Least Squares Parameters
9.5 Comparison of the Means of Two Samples
9.6 Comparing Variances Using the F Test
9.7 Comparing Expected and Observed Frequencies Using the χ² Test
9.8 Analysis of Variance
9.9 Review
End of Chapter Problems
Chapter 10: Data Analysis Tools in Excel and the Analysis ToolPak
10.1 Introduction
10.2 Activating the Data Analysis Tools
10.3 ANOVA: Single Factor
10.4 Correlation
10.5 F-test Two-Sample for Variances
10.6 Random Number Generation
10.7 Regression
10.8 t Tests
10.9 Other Tools
10.10 Review
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