Experimental statistics and data analysis for mechanical and aerospace engineers:
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Boca Raton ; London ; New York
CRC Press
2022
|
| Edition: | First edition |
| Series: | Advances in applied mathematics
|
| Links: | https://ebookcentral.proquest.com/lib/munchentech/detail.action?docID=6792197 |
| Physical Description: | 1 Online-Ressource Illustrationen, Diagramme |
| ISBN: | 9781000469615 9781003094227 |
Staff View
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| 245 | 1 | 0 | |a Experimental statistics and data analysis for mechanical and aerospace engineers |c James A. Middleton |
| 250 | |a First edition | ||
| 264 | 1 | |a Boca Raton ; London ; New York |b CRC Press |c 2022 | |
| 264 | 4 | |c © 2022 | |
| 300 | |a 1 Online-Ressource |b Illustrationen, Diagramme | ||
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| 490 | 0 | |a Advances in applied mathematics | |
| 505 | 8 | |a Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- List of Figures -- List of Tables -- Symbols -- I -- 1. Introduction -- 1.1. Approach of This Book -- 1.1.1. Data Modeling -- 1.1.2. Building an Empirical Mindset -- 1.2. The Role of Data -- 1.3. References -- 1.4. Chapter 1 Study Problems -- 2. Dealing with Variation -- 2.1. Measurement -- 2.1.1. Natural Variation -- 2.1.1.1. Shape of Data -- 2.2. Distribution -- 2.2.1. Histogram -- 2.2.1.1. How to Draw a Histogram -- 2.2.1.2. Comparing Histograms -- 2.3. Accuracy and Precision of Measurements -- 2.3.1. Accuracy-Systematic Error -- 2.3.1.1. Sources of Systematic Error -- 2.3.2. Precision-Random Error -- 2.4. Continuous Versus Discrete Data -- 2.4.1. Discrete Random Variables -- 2.4.2. Continuous Random Variables -- 2.5. Law of Large Numbers -- 2.6. Central Limit Theorem -- 2.7. Representativeness -- 2.7.1. "Simple" Random Sampling -- 2.8. References -- 2.9. Chapter 2 Study Problems -- 3. Types of Data -- 3.1. Scales of Measure -- 3.1.1. Nominal Data -- 3.1.2. Ordinal Data -- 3.1.3. Interval Data -- 3.1.4. Ratio Data -- 3.2. Population Parameters and Sample Statistics -- 3.2.1. Parameters -- 3.2.1.1. Population Parameters -- 3.2.1.2. What Are the Important Sample Statistics That Model Population Parameters? -- 3.2.1.3. Nominal Data: -- 3.2.1.4. Symmetry of the Binomial Distribution -- 3.2.1.5. Ordinal Data: -- 3.2.1.6. Interval -- 3.3. The Sample Mean and Standard Deviation as Robust Estimators -- 3.4. References -- 3.5. Study Problems for Chapter 3 -- 4. Introduction to Probability -- 4.1. Simple Probability -- 4.2. Conditional Probability -- 4.3. Moments of a Distribution -- 4.3.1. The Mean as a Moment -- 4.3.2. The Variance as a Moment -- 4.3.3. Summary: Bringing Probability, Moments, and Sample Statistics | |
| 505 | 8 | |a 4.4. Probability Density Function and Cumulative Distribution -- 4.5. Summary of Probability -- 4.6. Study Problems for Chapter 4 -- 5. The Sampling Distribution of the Mean -- 5.1. The General Logic of the Sampling Distribution -- 5.2. Sampling Distribution of the Mean -- 5.3. The Standard Normal Distribution -- 5.3.1. Probability Density of the Standard Normal Distribution -- 5.3.2. Now Let's Do Some Real Stats with the Normal Distribution! -- 5.3.3. The Z-test -- 5.4. Summary -- 5.5. References -- 5.6. Study Problems for Chapter 5 -- II. Testing Hypotheses -- 6. The Ten Building Blocks of Experimental Design -- 6.0.1. Notation -- 6.1. Basic Experimental Designs -- 6.1.1. One-shot Case Study -- 6.1.2. One-sample, Pre-post Design -- 6.1.3. Static Sample Comparison -- 6.1.4. Random Sample Design -- 6.1.5. Pre-post Randomized Sample -- 6.1.6. Factorial Designs -- 6.1.7. Randomized Block Factorial Designs -- 6.1.8. One-shot Repeated Measures -- 6.1.9. Randomized Factors Repeated Measures -- 6.1.10. Ex-post-facto -- 6.1.11. Time Series -- 6.2. Summary -- 6.3. References -- 6.4. Study Problems for Chapter 6 -- 7. Sampling Distribution of the Proportion -- 7.1. Sampling Distribution of a Proportion: Binomial Distribution -- 7.1.1. Bernoulli Process -- 7.1.2. Binomial Distribution -- 7.1.3. Binomial Probabilities in an Interval -- 7.1.4. Using the Symmetry of the Binomial Distribution -- 7.1.5. The Normal Approximation to the Binomial Distribution -- 7.1.6. Sampling with and without Replacement -- 7.1.7. The Hypergeometric Distribution -- 7.2. Summary -- 7.3. References -- 7.4. Study Problems for Chapter 7 -- 8. Hypothesis Testing Using 1-Sample Statistics -- 8.1. Philosophy -- 8.1.1. Falsification -- 8.1.2. The Double-Negative: The Null Hypothesis -- 8.2. The Consequences of Being Wrong: -- 8.2.1. Type I Error Rate: | |
| 505 | 8 | |a 8.3. How Many Tails? Or Knowing Your Ass From the Hole in -- 8.3.1. Confidence Intervals for the One-sample Z-test -- 8.4. Summary of Z-test -- 8.5. One Sample -- 8.5.1. Guinness and the Invention of -- 8.5.2. The One-sample -- 8.6. Summary of Basic Hypothesis Testing -- 8.7. References -- 8.8. Study Problems for Chapter 8 -- 9. 2-Sample Statistics -- 9.1. 2-sample -- 9.1.1. E(x) of x1 x2 Under the Null Hypothesis -- 9.1.2. 2sample -- 9.1.3. 2-sample -- 9.1.3.1. Assumption of Independence of Observations -- 9.1.3.2. Assumption of Normal Population Distribution(s) -- 9.1.3.3. Assumption of Homogeneity of Variance -- 9.2. Paired Sample -- 9.2.1. What Does the Paired-Sample -- 9.3. 2. Test of Independence: Testing the Independence of Proportions for Two or More Samples -- 9.3.1. Null and Alternative Hypotheses for Proportions -- 9.3.2. Assumptions of the -- 9.3.2.1. Independence -- 9.3.2.2. Cell Frequencies Greater Than 5 -- 9.4. F-test of Equal Variances -- 9.4.1. Assumptions of the F-test -- 9.5. Summary of 2-sample Statistics -- 9.6. References -- 9.7. Study Problems for Chapter 9 -- 10. Simple Linear Regression -- 10.1. Finding the Line of Best Fit -- 10.1.1. Goodness of Fit -- 10.1.1.1. R2: The Coe -- 10.1.2. When is a Linear Model NOT Appropriate? -- 10.2. Residual Analysis -- 10.2.1. Heteroscedasticity -- 10.3. Hypothesis Testing in Regression: -- 10.4. General Procedure for Performing Regression Analyses -- 10.5. Summary of Simple Linear Regression -- 10.6. References -- 10.7. Study Problems for Chapter 10 -- III. Applications of the General Linear Model -- 11. The General Linear Model: Regression with Multiple Predictors -- 11.1. Linear Algebra Approach to Regression -- 11.2. Calculus Approach to Regression -- 11.3. Fitting a Line -- 11.4. Expanding to Multiple Predictor Variables: Multiple Linear -- 11.4.1. Prediction -- 11.4.2. Extrapolation | |
| 505 | 8 | |a 11.4.3. Assumptions of Multiple Regression -- 11.4.4. Covariance and Correlation -- 11.4.5. Collinearity: Covariance Among Independent Variables -- 11.5. The General Linear Model -- 11.6. Extended Example -- 11.7. Summary -- 11.8. References -- 11.9. Chapter 11 Study Problems -- 12. The GLM with Categorical Independent Variables: The Analysis of Variance -- 12.1. The 2-sample -- 12.2. Expanding the -- 12.2.1. Residual Analysis -- 12.2.2. Multiple Comparisons: What to Do if You Find Significant Results -- 12.2.2.1. Dunn-Bonferroni Correction -- 12.2.2.2. Sche -- 12.2.3. Assumptions of the ANOVA -- 12.3. Extended Example -- 12.4. Summary -- 12.5. References -- 12.6. Study Problems for Chapter 12 -- 13. The General Linear Model: Randomized Block Factorial ANOVA -- 13.1. It is All Just Regression -- 13.2. Randomized Block ANOVA -- 13.2.1. A Quick Note on Notation -- 13.2.2. Now Back to Analysis -- 13.2.3. Partial -- 13.2.3.1. E -- 13.3. Summary -- 13.4. References -- 13.5. Study Problems for Chapter 13 -- 14. Factorial Analysis of Variance -- 14.1. Interactions as Additional Factors -- 14.1.1. Post Hoc Tests -- 14.1.2. Fixed vs. Random E -- 14.1.3. Assumptions of Factorial ANOVA -- 14.2. Nested Factors in ANOVA -- 14.3. Summary -- 14.4. References -- 14.5. Study Problems for Chapter 14 -- IV. Introduction to Computational Methods and Machine Learning -- 15. The Bootstrap -- 15.0.1. What It Means to Be -- 15.1. The Bootstrap Method -- 15.1.1. Basic Logic Computing a Bootstrap Confidence Interval -- 15.2. Empirical Distribution Function -- 15.3. Bootstrap Sampling Distribution of the Median -- 15.3.1. 2-sample Confidence Interval -- 15.4. Regression Coe -- 15.5. Summary -- 15.6. References -- 15.7. Study Problems for Chapter 15 -- 16. Data Reduction: Principal Components Analysis -- 16.1. Data Reduction -- 16.1.1. Feature Elimination | |
| 505 | 8 | |a 16.1.2. Feature Extraction -- 16.2. PCA as a Projection -- 16.3. PCA as Matrix Factorization -- 16.3.1. Extended Example: Acoustics -- 16.4. Principal Components Regression -- 16.5. Dimension Reduction: Feature Elimination -- 16.5.1. The Scree Test -- 16.6. Summary -- 16.7. References -- 16.8. Study Problems for Chapter 16 -- Index | |
| 776 | 0 | 8 | |i Erscheint auch als |a Middleton, James A. |t Experimental Statistics and Data Analysis for Mechanical and Aerospace Engineers |d Milton : CRC Press LLC,c2021 |n Druck-Ausgabe, Hardcover |z 978-0-367-55596-2 |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe, Paperback |z 978-1-032-06636-3 |
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| contents | Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- List of Figures -- List of Tables -- Symbols -- I -- 1. Introduction -- 1.1. Approach of This Book -- 1.1.1. Data Modeling -- 1.1.2. Building an Empirical Mindset -- 1.2. The Role of Data -- 1.3. References -- 1.4. Chapter 1 Study Problems -- 2. Dealing with Variation -- 2.1. Measurement -- 2.1.1. Natural Variation -- 2.1.1.1. Shape of Data -- 2.2. Distribution -- 2.2.1. Histogram -- 2.2.1.1. How to Draw a Histogram -- 2.2.1.2. Comparing Histograms -- 2.3. Accuracy and Precision of Measurements -- 2.3.1. Accuracy-Systematic Error -- 2.3.1.1. Sources of Systematic Error -- 2.3.2. Precision-Random Error -- 2.4. Continuous Versus Discrete Data -- 2.4.1. Discrete Random Variables -- 2.4.2. Continuous Random Variables -- 2.5. Law of Large Numbers -- 2.6. Central Limit Theorem -- 2.7. Representativeness -- 2.7.1. "Simple" Random Sampling -- 2.8. References -- 2.9. Chapter 2 Study Problems -- 3. Types of Data -- 3.1. Scales of Measure -- 3.1.1. Nominal Data -- 3.1.2. Ordinal Data -- 3.1.3. Interval Data -- 3.1.4. Ratio Data -- 3.2. Population Parameters and Sample Statistics -- 3.2.1. Parameters -- 3.2.1.1. Population Parameters -- 3.2.1.2. What Are the Important Sample Statistics That Model Population Parameters? -- 3.2.1.3. Nominal Data: -- 3.2.1.4. Symmetry of the Binomial Distribution -- 3.2.1.5. Ordinal Data: -- 3.2.1.6. Interval -- 3.3. The Sample Mean and Standard Deviation as Robust Estimators -- 3.4. References -- 3.5. Study Problems for Chapter 3 -- 4. Introduction to Probability -- 4.1. Simple Probability -- 4.2. Conditional Probability -- 4.3. Moments of a Distribution -- 4.3.1. The Mean as a Moment -- 4.3.2. The Variance as a Moment -- 4.3.3. Summary: Bringing Probability, Moments, and Sample Statistics 4.4. Probability Density Function and Cumulative Distribution -- 4.5. Summary of Probability -- 4.6. Study Problems for Chapter 4 -- 5. The Sampling Distribution of the Mean -- 5.1. The General Logic of the Sampling Distribution -- 5.2. Sampling Distribution of the Mean -- 5.3. The Standard Normal Distribution -- 5.3.1. Probability Density of the Standard Normal Distribution -- 5.3.2. Now Let's Do Some Real Stats with the Normal Distribution! -- 5.3.3. The Z-test -- 5.4. Summary -- 5.5. References -- 5.6. Study Problems for Chapter 5 -- II. Testing Hypotheses -- 6. The Ten Building Blocks of Experimental Design -- 6.0.1. Notation -- 6.1. Basic Experimental Designs -- 6.1.1. One-shot Case Study -- 6.1.2. One-sample, Pre-post Design -- 6.1.3. Static Sample Comparison -- 6.1.4. Random Sample Design -- 6.1.5. Pre-post Randomized Sample -- 6.1.6. Factorial Designs -- 6.1.7. Randomized Block Factorial Designs -- 6.1.8. One-shot Repeated Measures -- 6.1.9. Randomized Factors Repeated Measures -- 6.1.10. Ex-post-facto -- 6.1.11. Time Series -- 6.2. Summary -- 6.3. References -- 6.4. Study Problems for Chapter 6 -- 7. Sampling Distribution of the Proportion -- 7.1. Sampling Distribution of a Proportion: Binomial Distribution -- 7.1.1. Bernoulli Process -- 7.1.2. Binomial Distribution -- 7.1.3. Binomial Probabilities in an Interval -- 7.1.4. Using the Symmetry of the Binomial Distribution -- 7.1.5. The Normal Approximation to the Binomial Distribution -- 7.1.6. Sampling with and without Replacement -- 7.1.7. The Hypergeometric Distribution -- 7.2. Summary -- 7.3. References -- 7.4. Study Problems for Chapter 7 -- 8. Hypothesis Testing Using 1-Sample Statistics -- 8.1. Philosophy -- 8.1.1. Falsification -- 8.1.2. The Double-Negative: The Null Hypothesis -- 8.2. The Consequences of Being Wrong: -- 8.2.1. Type I Error Rate: 8.3. How Many Tails? Or Knowing Your Ass From the Hole in -- 8.3.1. Confidence Intervals for the One-sample Z-test -- 8.4. Summary of Z-test -- 8.5. One Sample -- 8.5.1. Guinness and the Invention of -- 8.5.2. The One-sample -- 8.6. Summary of Basic Hypothesis Testing -- 8.7. References -- 8.8. Study Problems for Chapter 8 -- 9. 2-Sample Statistics -- 9.1. 2-sample -- 9.1.1. E(x) of x1 x2 Under the Null Hypothesis -- 9.1.2. 2sample -- 9.1.3. 2-sample -- 9.1.3.1. Assumption of Independence of Observations -- 9.1.3.2. Assumption of Normal Population Distribution(s) -- 9.1.3.3. Assumption of Homogeneity of Variance -- 9.2. Paired Sample -- 9.2.1. What Does the Paired-Sample -- 9.3. 2. Test of Independence: Testing the Independence of Proportions for Two or More Samples -- 9.3.1. Null and Alternative Hypotheses for Proportions -- 9.3.2. Assumptions of the -- 9.3.2.1. Independence -- 9.3.2.2. Cell Frequencies Greater Than 5 -- 9.4. F-test of Equal Variances -- 9.4.1. Assumptions of the F-test -- 9.5. Summary of 2-sample Statistics -- 9.6. References -- 9.7. Study Problems for Chapter 9 -- 10. Simple Linear Regression -- 10.1. Finding the Line of Best Fit -- 10.1.1. Goodness of Fit -- 10.1.1.1. R2: The Coe -- 10.1.2. When is a Linear Model NOT Appropriate? -- 10.2. Residual Analysis -- 10.2.1. Heteroscedasticity -- 10.3. Hypothesis Testing in Regression: -- 10.4. General Procedure for Performing Regression Analyses -- 10.5. Summary of Simple Linear Regression -- 10.6. References -- 10.7. Study Problems for Chapter 10 -- III. Applications of the General Linear Model -- 11. The General Linear Model: Regression with Multiple Predictors -- 11.1. Linear Algebra Approach to Regression -- 11.2. Calculus Approach to Regression -- 11.3. Fitting a Line -- 11.4. Expanding to Multiple Predictor Variables: Multiple Linear -- 11.4.1. Prediction -- 11.4.2. Extrapolation 11.4.3. Assumptions of Multiple Regression -- 11.4.4. Covariance and Correlation -- 11.4.5. Collinearity: Covariance Among Independent Variables -- 11.5. The General Linear Model -- 11.6. Extended Example -- 11.7. Summary -- 11.8. References -- 11.9. Chapter 11 Study Problems -- 12. The GLM with Categorical Independent Variables: The Analysis of Variance -- 12.1. The 2-sample -- 12.2. Expanding the -- 12.2.1. Residual Analysis -- 12.2.2. Multiple Comparisons: What to Do if You Find Significant Results -- 12.2.2.1. Dunn-Bonferroni Correction -- 12.2.2.2. Sche -- 12.2.3. Assumptions of the ANOVA -- 12.3. Extended Example -- 12.4. Summary -- 12.5. References -- 12.6. Study Problems for Chapter 12 -- 13. The General Linear Model: Randomized Block Factorial ANOVA -- 13.1. It is All Just Regression -- 13.2. Randomized Block ANOVA -- 13.2.1. A Quick Note on Notation -- 13.2.2. Now Back to Analysis -- 13.2.3. Partial -- 13.2.3.1. E -- 13.3. Summary -- 13.4. References -- 13.5. Study Problems for Chapter 13 -- 14. Factorial Analysis of Variance -- 14.1. Interactions as Additional Factors -- 14.1.1. Post Hoc Tests -- 14.1.2. Fixed vs. Random E -- 14.1.3. Assumptions of Factorial ANOVA -- 14.2. Nested Factors in ANOVA -- 14.3. Summary -- 14.4. References -- 14.5. Study Problems for Chapter 14 -- IV. Introduction to Computational Methods and Machine Learning -- 15. The Bootstrap -- 15.0.1. What It Means to Be -- 15.1. The Bootstrap Method -- 15.1.1. Basic Logic Computing a Bootstrap Confidence Interval -- 15.2. Empirical Distribution Function -- 15.3. Bootstrap Sampling Distribution of the Median -- 15.3.1. 2-sample Confidence Interval -- 15.4. Regression Coe -- 15.5. Summary -- 15.6. References -- 15.7. Study Problems for Chapter 15 -- 16. Data Reduction: Principal Components Analysis -- 16.1. Data Reduction -- 16.1.1. Feature Elimination 16.1.2. Feature Extraction -- 16.2. PCA as a Projection -- 16.3. PCA as Matrix Factorization -- 16.3.1. Extended Example: Acoustics -- 16.4. Principal Components Regression -- 16.5. Dimension Reduction: Feature Elimination -- 16.5.1. The Scree Test -- 16.6. Summary -- 16.7. References -- 16.8. Study Problems for Chapter 16 -- Index |
| ctrlnum | (ZDB-30-PQE)EBC6792197 (ZDB-30-PAD)EBC6792197 (ZDB-89-EBL)EBL6792197 (OCoLC)1273732023 (DE-599)BVBBV048220998 |
| dewey-full | 519.5 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5 |
| dewey-search | 519.5 |
| dewey-sort | 3519.5 |
| dewey-tens | 510 - Mathematics |
| discipline | Technik Mathematik |
| edition | First edition |
| format | Electronic eBook |
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Introduction -- 1.1. Approach of This Book -- 1.1.1. Data Modeling -- 1.1.2. Building an Empirical Mindset -- 1.2. The Role of Data -- 1.3. References -- 1.4. Chapter 1 Study Problems -- 2. Dealing with Variation -- 2.1. Measurement -- 2.1.1. Natural Variation -- 2.1.1.1. Shape of Data -- 2.2. Distribution -- 2.2.1. Histogram -- 2.2.1.1. How to Draw a Histogram -- 2.2.1.2. Comparing Histograms -- 2.3. Accuracy and Precision of Measurements -- 2.3.1. Accuracy-Systematic Error -- 2.3.1.1. Sources of Systematic Error -- 2.3.2. Precision-Random Error -- 2.4. Continuous Versus Discrete Data -- 2.4.1. Discrete Random Variables -- 2.4.2. Continuous Random Variables -- 2.5. Law of Large Numbers -- 2.6. Central Limit Theorem -- 2.7. Representativeness -- 2.7.1. "Simple" Random Sampling -- 2.8. References -- 2.9. Chapter 2 Study Problems -- 3. Types of Data -- 3.1. Scales of Measure -- 3.1.1. Nominal Data -- 3.1.2. Ordinal Data -- 3.1.3. Interval Data -- 3.1.4. Ratio Data -- 3.2. Population Parameters and Sample Statistics -- 3.2.1. Parameters -- 3.2.1.1. Population Parameters -- 3.2.1.2. What Are the Important Sample Statistics That Model Population Parameters? -- 3.2.1.3. Nominal Data: -- 3.2.1.4. Symmetry of the Binomial Distribution -- 3.2.1.5. Ordinal Data: -- 3.2.1.6. Interval -- 3.3. The Sample Mean and Standard Deviation as Robust Estimators -- 3.4. References -- 3.5. Study Problems for Chapter 3 -- 4. Introduction to Probability -- 4.1. Simple Probability -- 4.2. Conditional Probability -- 4.3. Moments of a Distribution -- 4.3.1. The Mean as a Moment -- 4.3.2. The Variance as a Moment -- 4.3.3. Summary: Bringing Probability, Moments, and Sample Statistics</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4.4. Probability Density Function and Cumulative Distribution -- 4.5. Summary of Probability -- 4.6. Study Problems for Chapter 4 -- 5. The Sampling Distribution of the Mean -- 5.1. The General Logic of the Sampling Distribution -- 5.2. Sampling Distribution of the Mean -- 5.3. The Standard Normal Distribution -- 5.3.1. Probability Density of the Standard Normal Distribution -- 5.3.2. Now Let's Do Some Real Stats with the Normal Distribution! -- 5.3.3. The Z-test -- 5.4. Summary -- 5.5. References -- 5.6. Study Problems for Chapter 5 -- II. Testing Hypotheses -- 6. The Ten Building Blocks of Experimental Design -- 6.0.1. Notation -- 6.1. Basic Experimental Designs -- 6.1.1. One-shot Case Study -- 6.1.2. One-sample, Pre-post Design -- 6.1.3. Static Sample Comparison -- 6.1.4. Random Sample Design -- 6.1.5. Pre-post Randomized Sample -- 6.1.6. Factorial Designs -- 6.1.7. Randomized Block Factorial Designs -- 6.1.8. One-shot Repeated Measures -- 6.1.9. Randomized Factors Repeated Measures -- 6.1.10. Ex-post-facto -- 6.1.11. Time Series -- 6.2. Summary -- 6.3. References -- 6.4. Study Problems for Chapter 6 -- 7. Sampling Distribution of the Proportion -- 7.1. Sampling Distribution of a Proportion: Binomial Distribution -- 7.1.1. Bernoulli Process -- 7.1.2. Binomial Distribution -- 7.1.3. Binomial Probabilities in an Interval -- 7.1.4. Using the Symmetry of the Binomial Distribution -- 7.1.5. The Normal Approximation to the Binomial Distribution -- 7.1.6. Sampling with and without Replacement -- 7.1.7. The Hypergeometric Distribution -- 7.2. Summary -- 7.3. References -- 7.4. Study Problems for Chapter 7 -- 8. Hypothesis Testing Using 1-Sample Statistics -- 8.1. Philosophy -- 8.1.1. Falsification -- 8.1.2. The Double-Negative: The Null Hypothesis -- 8.2. The Consequences of Being Wrong: -- 8.2.1. Type I Error Rate:</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">8.3. How Many Tails? Or Knowing Your Ass From the Hole in -- 8.3.1. Confidence Intervals for the One-sample Z-test -- 8.4. Summary of Z-test -- 8.5. One Sample -- 8.5.1. Guinness and the Invention of -- 8.5.2. The One-sample -- 8.6. Summary of Basic Hypothesis Testing -- 8.7. References -- 8.8. Study Problems for Chapter 8 -- 9. 2-Sample Statistics -- 9.1. 2-sample -- 9.1.1. E(x) of x1 x2 Under the Null Hypothesis -- 9.1.2. 2sample -- 9.1.3. 2-sample -- 9.1.3.1. Assumption of Independence of Observations -- 9.1.3.2. Assumption of Normal Population Distribution(s) -- 9.1.3.3. Assumption of Homogeneity of Variance -- 9.2. Paired Sample -- 9.2.1. What Does the Paired-Sample -- 9.3. 2. Test of Independence: Testing the Independence of Proportions for Two or More Samples -- 9.3.1. Null and Alternative Hypotheses for Proportions -- 9.3.2. Assumptions of the -- 9.3.2.1. Independence -- 9.3.2.2. Cell Frequencies Greater Than 5 -- 9.4. F-test of Equal Variances -- 9.4.1. Assumptions of the F-test -- 9.5. Summary of 2-sample Statistics -- 9.6. References -- 9.7. Study Problems for Chapter 9 -- 10. Simple Linear Regression -- 10.1. Finding the Line of Best Fit -- 10.1.1. Goodness of Fit -- 10.1.1.1. R2: The Coe -- 10.1.2. When is a Linear Model NOT Appropriate? -- 10.2. Residual Analysis -- 10.2.1. Heteroscedasticity -- 10.3. Hypothesis Testing in Regression: -- 10.4. General Procedure for Performing Regression Analyses -- 10.5. Summary of Simple Linear Regression -- 10.6. References -- 10.7. Study Problems for Chapter 10 -- III. Applications of the General Linear Model -- 11. The General Linear Model: Regression with Multiple Predictors -- 11.1. Linear Algebra Approach to Regression -- 11.2. Calculus Approach to Regression -- 11.3. Fitting a Line -- 11.4. Expanding to Multiple Predictor Variables: Multiple Linear -- 11.4.1. Prediction -- 11.4.2. Extrapolation</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">11.4.3. Assumptions of Multiple Regression -- 11.4.4. Covariance and Correlation -- 11.4.5. Collinearity: Covariance Among Independent Variables -- 11.5. The General Linear Model -- 11.6. Extended Example -- 11.7. Summary -- 11.8. References -- 11.9. Chapter 11 Study Problems -- 12. The GLM with Categorical Independent Variables: The Analysis of Variance -- 12.1. The 2-sample -- 12.2. Expanding the -- 12.2.1. Residual Analysis -- 12.2.2. Multiple Comparisons: What to Do if You Find Significant Results -- 12.2.2.1. Dunn-Bonferroni Correction -- 12.2.2.2. Sche -- 12.2.3. Assumptions of the ANOVA -- 12.3. Extended Example -- 12.4. Summary -- 12.5. References -- 12.6. Study Problems for Chapter 12 -- 13. The General Linear Model: Randomized Block Factorial ANOVA -- 13.1. It is All Just Regression -- 13.2. Randomized Block ANOVA -- 13.2.1. A Quick Note on Notation -- 13.2.2. Now Back to Analysis -- 13.2.3. Partial -- 13.2.3.1. E -- 13.3. Summary -- 13.4. References -- 13.5. Study Problems for Chapter 13 -- 14. Factorial Analysis of Variance -- 14.1. Interactions as Additional Factors -- 14.1.1. Post Hoc Tests -- 14.1.2. Fixed vs. Random E -- 14.1.3. Assumptions of Factorial ANOVA -- 14.2. Nested Factors in ANOVA -- 14.3. Summary -- 14.4. References -- 14.5. Study Problems for Chapter 14 -- IV. Introduction to Computational Methods and Machine Learning -- 15. The Bootstrap -- 15.0.1. What It Means to Be -- 15.1. The Bootstrap Method -- 15.1.1. Basic Logic Computing a Bootstrap Confidence Interval -- 15.2. Empirical Distribution Function -- 15.3. Bootstrap Sampling Distribution of the Median -- 15.3.1. 2-sample Confidence Interval -- 15.4. Regression Coe -- 15.5. Summary -- 15.6. References -- 15.7. Study Problems for Chapter 15 -- 16. Data Reduction: Principal Components Analysis -- 16.1. Data Reduction -- 16.1.1. Feature Elimination</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">16.1.2. Feature Extraction -- 16.2. PCA as a Projection -- 16.3. PCA as Matrix Factorization -- 16.3.1. Extended Example: Acoustics -- 16.4. Principal Components Regression -- 16.5. Dimension Reduction: Feature Elimination -- 16.5.1. The Scree Test -- 16.6. Summary -- 16.7. References -- 16.8. Study Problems for Chapter 16 -- Index</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="a">Middleton, James A.</subfield><subfield code="t">Experimental Statistics and Data Analysis for Mechanical and Aerospace Engineers</subfield><subfield code="d">Milton : CRC Press LLC,c2021</subfield><subfield code="n">Druck-Ausgabe, Hardcover</subfield><subfield code="z">978-0-367-55596-2</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe, Paperback</subfield><subfield code="z">978-1-032-06636-3</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-033601737</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://ebookcentral.proquest.com/lib/munchentech/detail.action?docID=6792197</subfield><subfield code="l">DE-91</subfield><subfield code="p">ZDB-30-PQE</subfield><subfield code="q">TUM_PDA_PQE_Kauf</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV048220998 |
| illustrated | Illustrated |
| indexdate | 2025-03-04T05:00:11Z |
| institution | BVB |
| isbn | 9781000469615 9781003094227 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033601737 |
| oclc_num | 1273732023 |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource Illustrationen, Diagramme |
| psigel | ZDB-30-PQE ZDB-30-PQE TUM_PDA_PQE_Kauf |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | CRC Press |
| record_format | marc |
| series2 | Advances in applied mathematics |
| spellingShingle | Middleton, James A. Experimental statistics and data analysis for mechanical and aerospace engineers Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- List of Figures -- List of Tables -- Symbols -- I -- 1. Introduction -- 1.1. Approach of This Book -- 1.1.1. Data Modeling -- 1.1.2. Building an Empirical Mindset -- 1.2. The Role of Data -- 1.3. References -- 1.4. Chapter 1 Study Problems -- 2. Dealing with Variation -- 2.1. Measurement -- 2.1.1. Natural Variation -- 2.1.1.1. Shape of Data -- 2.2. Distribution -- 2.2.1. Histogram -- 2.2.1.1. How to Draw a Histogram -- 2.2.1.2. Comparing Histograms -- 2.3. Accuracy and Precision of Measurements -- 2.3.1. Accuracy-Systematic Error -- 2.3.1.1. Sources of Systematic Error -- 2.3.2. Precision-Random Error -- 2.4. Continuous Versus Discrete Data -- 2.4.1. Discrete Random Variables -- 2.4.2. Continuous Random Variables -- 2.5. Law of Large Numbers -- 2.6. Central Limit Theorem -- 2.7. Representativeness -- 2.7.1. "Simple" Random Sampling -- 2.8. References -- 2.9. Chapter 2 Study Problems -- 3. Types of Data -- 3.1. Scales of Measure -- 3.1.1. Nominal Data -- 3.1.2. Ordinal Data -- 3.1.3. Interval Data -- 3.1.4. Ratio Data -- 3.2. Population Parameters and Sample Statistics -- 3.2.1. Parameters -- 3.2.1.1. Population Parameters -- 3.2.1.2. What Are the Important Sample Statistics That Model Population Parameters? -- 3.2.1.3. Nominal Data: -- 3.2.1.4. Symmetry of the Binomial Distribution -- 3.2.1.5. Ordinal Data: -- 3.2.1.6. Interval -- 3.3. The Sample Mean and Standard Deviation as Robust Estimators -- 3.4. References -- 3.5. Study Problems for Chapter 3 -- 4. Introduction to Probability -- 4.1. Simple Probability -- 4.2. Conditional Probability -- 4.3. Moments of a Distribution -- 4.3.1. The Mean as a Moment -- 4.3.2. The Variance as a Moment -- 4.3.3. Summary: Bringing Probability, Moments, and Sample Statistics 4.4. Probability Density Function and Cumulative Distribution -- 4.5. Summary of Probability -- 4.6. Study Problems for Chapter 4 -- 5. The Sampling Distribution of the Mean -- 5.1. The General Logic of the Sampling Distribution -- 5.2. Sampling Distribution of the Mean -- 5.3. The Standard Normal Distribution -- 5.3.1. Probability Density of the Standard Normal Distribution -- 5.3.2. Now Let's Do Some Real Stats with the Normal Distribution! -- 5.3.3. The Z-test -- 5.4. Summary -- 5.5. References -- 5.6. Study Problems for Chapter 5 -- II. Testing Hypotheses -- 6. The Ten Building Blocks of Experimental Design -- 6.0.1. Notation -- 6.1. Basic Experimental Designs -- 6.1.1. One-shot Case Study -- 6.1.2. One-sample, Pre-post Design -- 6.1.3. Static Sample Comparison -- 6.1.4. Random Sample Design -- 6.1.5. Pre-post Randomized Sample -- 6.1.6. Factorial Designs -- 6.1.7. Randomized Block Factorial Designs -- 6.1.8. One-shot Repeated Measures -- 6.1.9. Randomized Factors Repeated Measures -- 6.1.10. Ex-post-facto -- 6.1.11. Time Series -- 6.2. Summary -- 6.3. References -- 6.4. Study Problems for Chapter 6 -- 7. Sampling Distribution of the Proportion -- 7.1. Sampling Distribution of a Proportion: Binomial Distribution -- 7.1.1. Bernoulli Process -- 7.1.2. Binomial Distribution -- 7.1.3. Binomial Probabilities in an Interval -- 7.1.4. Using the Symmetry of the Binomial Distribution -- 7.1.5. The Normal Approximation to the Binomial Distribution -- 7.1.6. Sampling with and without Replacement -- 7.1.7. The Hypergeometric Distribution -- 7.2. Summary -- 7.3. References -- 7.4. Study Problems for Chapter 7 -- 8. Hypothesis Testing Using 1-Sample Statistics -- 8.1. Philosophy -- 8.1.1. Falsification -- 8.1.2. The Double-Negative: The Null Hypothesis -- 8.2. The Consequences of Being Wrong: -- 8.2.1. Type I Error Rate: 8.3. How Many Tails? Or Knowing Your Ass From the Hole in -- 8.3.1. Confidence Intervals for the One-sample Z-test -- 8.4. Summary of Z-test -- 8.5. One Sample -- 8.5.1. Guinness and the Invention of -- 8.5.2. The One-sample -- 8.6. Summary of Basic Hypothesis Testing -- 8.7. References -- 8.8. Study Problems for Chapter 8 -- 9. 2-Sample Statistics -- 9.1. 2-sample -- 9.1.1. E(x) of x1 x2 Under the Null Hypothesis -- 9.1.2. 2sample -- 9.1.3. 2-sample -- 9.1.3.1. Assumption of Independence of Observations -- 9.1.3.2. Assumption of Normal Population Distribution(s) -- 9.1.3.3. Assumption of Homogeneity of Variance -- 9.2. Paired Sample -- 9.2.1. What Does the Paired-Sample -- 9.3. 2. Test of Independence: Testing the Independence of Proportions for Two or More Samples -- 9.3.1. Null and Alternative Hypotheses for Proportions -- 9.3.2. Assumptions of the -- 9.3.2.1. Independence -- 9.3.2.2. Cell Frequencies Greater Than 5 -- 9.4. F-test of Equal Variances -- 9.4.1. Assumptions of the F-test -- 9.5. Summary of 2-sample Statistics -- 9.6. References -- 9.7. Study Problems for Chapter 9 -- 10. Simple Linear Regression -- 10.1. Finding the Line of Best Fit -- 10.1.1. Goodness of Fit -- 10.1.1.1. R2: The Coe -- 10.1.2. When is a Linear Model NOT Appropriate? -- 10.2. Residual Analysis -- 10.2.1. Heteroscedasticity -- 10.3. Hypothesis Testing in Regression: -- 10.4. General Procedure for Performing Regression Analyses -- 10.5. Summary of Simple Linear Regression -- 10.6. References -- 10.7. Study Problems for Chapter 10 -- III. Applications of the General Linear Model -- 11. The General Linear Model: Regression with Multiple Predictors -- 11.1. Linear Algebra Approach to Regression -- 11.2. Calculus Approach to Regression -- 11.3. Fitting a Line -- 11.4. Expanding to Multiple Predictor Variables: Multiple Linear -- 11.4.1. Prediction -- 11.4.2. Extrapolation 11.4.3. Assumptions of Multiple Regression -- 11.4.4. Covariance and Correlation -- 11.4.5. Collinearity: Covariance Among Independent Variables -- 11.5. The General Linear Model -- 11.6. Extended Example -- 11.7. Summary -- 11.8. References -- 11.9. Chapter 11 Study Problems -- 12. The GLM with Categorical Independent Variables: The Analysis of Variance -- 12.1. The 2-sample -- 12.2. Expanding the -- 12.2.1. Residual Analysis -- 12.2.2. Multiple Comparisons: What to Do if You Find Significant Results -- 12.2.2.1. Dunn-Bonferroni Correction -- 12.2.2.2. Sche -- 12.2.3. Assumptions of the ANOVA -- 12.3. Extended Example -- 12.4. Summary -- 12.5. References -- 12.6. Study Problems for Chapter 12 -- 13. The General Linear Model: Randomized Block Factorial ANOVA -- 13.1. It is All Just Regression -- 13.2. Randomized Block ANOVA -- 13.2.1. A Quick Note on Notation -- 13.2.2. Now Back to Analysis -- 13.2.3. Partial -- 13.2.3.1. E -- 13.3. Summary -- 13.4. References -- 13.5. Study Problems for Chapter 13 -- 14. Factorial Analysis of Variance -- 14.1. Interactions as Additional Factors -- 14.1.1. Post Hoc Tests -- 14.1.2. Fixed vs. Random E -- 14.1.3. Assumptions of Factorial ANOVA -- 14.2. Nested Factors in ANOVA -- 14.3. Summary -- 14.4. References -- 14.5. Study Problems for Chapter 14 -- IV. Introduction to Computational Methods and Machine Learning -- 15. The Bootstrap -- 15.0.1. What It Means to Be -- 15.1. The Bootstrap Method -- 15.1.1. Basic Logic Computing a Bootstrap Confidence Interval -- 15.2. Empirical Distribution Function -- 15.3. Bootstrap Sampling Distribution of the Median -- 15.3.1. 2-sample Confidence Interval -- 15.4. Regression Coe -- 15.5. Summary -- 15.6. References -- 15.7. Study Problems for Chapter 15 -- 16. Data Reduction: Principal Components Analysis -- 16.1. Data Reduction -- 16.1.1. Feature Elimination 16.1.2. Feature Extraction -- 16.2. PCA as a Projection -- 16.3. PCA as Matrix Factorization -- 16.3.1. Extended Example: Acoustics -- 16.4. Principal Components Regression -- 16.5. Dimension Reduction: Feature Elimination -- 16.5.1. The Scree Test -- 16.6. Summary -- 16.7. References -- 16.8. Study Problems for Chapter 16 -- Index |
| title | Experimental statistics and data analysis for mechanical and aerospace engineers |
| title_auth | Experimental statistics and data analysis for mechanical and aerospace engineers |
| title_exact_search | Experimental statistics and data analysis for mechanical and aerospace engineers |
| title_full | Experimental statistics and data analysis for mechanical and aerospace engineers James A. Middleton |
| title_fullStr | Experimental statistics and data analysis for mechanical and aerospace engineers James A. Middleton |
| title_full_unstemmed | Experimental statistics and data analysis for mechanical and aerospace engineers James A. Middleton |
| title_short | Experimental statistics and data analysis for mechanical and aerospace engineers |
| title_sort | experimental statistics and data analysis for mechanical and aerospace engineers |
| work_keys_str_mv | AT middletonjamesa experimentalstatisticsanddataanalysisformechanicalandaerospaceengineers |