Engineering Mathematics. SOLUTION MANUAL

About this Book 2 Cover Page 2 Title Page 3 Copyright Page 6 Brief Contents 8 Contents 10 To the Teacher 16 Media and Supplements 31 Acknowledgments 36 To the Student 53 Dedication 55 Chapter 0: What Is a Statistical Model? 56 0.1 Model Basics 61 0.2 A Four-Step Process 69 Chapter Summary 85 Exercises 87 Unit A: Linear Regression 97 Chapter 1: Simple Linear Regression 101 1.1 The Simple Linear Regression Model 104 1.2 Conditions for a Simple Linear Model 117 1.3 Assessing Conditions 124 1.4 Transformations/Reexpressions 137 1.5 Outliers and Influential Points 158 Chapter Summary 171 Exercises 175 Chapter 2: Inference for Simple Linear Regression 197 2.1 Inference for Regression Slope 202 2.2 Partitioning Variability—ANOVA 214 2.3 Regression and Correlation 221 2.4 Intervals for Predictions 231 2.5 Case Study: Butterfly Wings 238 Chapter Summary 246 Exercises 250 Chapter 3: Multiple Regression 277 3.1 Multiple Linear Regression Model 285 3.2 Assessing a Multiple Regression Model 292 3.3 Comparing Two Regression Lines 303 3.4 New Predictors from Old 323 3.5 Correlated Predictors 351 3.6 Testing Subsets of Predictors 366 3.7 Case Study: Predicting in Retail Clothing 379 Chapter Summary 396 Exercises 401 Chapter 4: Additional Topics in Regression 430 Topic 4.1 Added Variable Plots 434 Topic 4.2 Techniques for Choosing Predictors 440 Topic 4.3 Cross-validation 460 Topic 4.4 Identifying Unusual Points in Regression 470 Topic 4.5 Coding Categorical Predictors 484 Topic 4.6 Randomization Test for a Relationship 499 Topic 4.7 Bootstrap for Regression 506 Exercises 517 Unit B: Analysis of Variance 531 Chapter 5: One-way ANOVA and Randomized Experiments 535 5.1 Overview of ANOVA 539 5.2 The One-way Randomized Experiment and Its Observational Sibling 549 5.3 Fitting the Model 559 5.4 Formal Inference: Assessing and Using the Model 576 5.5 How Big Is the Effect?: Confidence Intervals and Effect Sizes 597 5.6 Using Plots to Help Choose a Scale for the Response 612 5.7 Multiple Comparisons and Fisher’s Least Significant Difference 630 5.8 Case Study: Words with Friends 636 Chapter Summary 642 Exercises 643 Chapter 6: Blocking and Two-way ANOVA 673 6.1 Choose: RCB Design and Its Observational Relatives 676 6.2 Exploring Data from Block Designs 698 6.3 Fitting the Model for a Block Design 711 6.4 Assessing the Model for a Block Design 722 6.5 Using the Model for a Block Design 739 Chapter Summary 751 Exercises 753 Chapter 7: ANOVA with Interaction and Factorial Designs 766 7.1 Interaction 769 7.2 Design: The Two-way Factorial Experiment 783 7.3 Exploring Two-way Data 791 7.4 Fitting a Two-way Balanced ANOVA Model 810 7.5 Assessing Fit: Do We Need a Transformation? 823 7.6 Using a Two-way ANOVA Model 828 Chapter Summary 842 Exercises 847 Chapter 8: Additional Topics in Analysis of Variance 869 Topic 8.1 Levene’s Test for Homogeneity of Variances 873 Topic 8.2 Multiple Tests 883 Topic 8.3 Comparisons and Contrasts 898 Topic 8.4 Nonparametric Statistics 917 Topic 8.5 Randomization F-Test 929 Topic 8.6 Repeated Measures Designs and Datasets 949 Topic 8.7 ANOVA and Regression with Indicators 961 Topic 8.8 Analysis of Covariance 985 Exercises 1008 Chapter 8: Online Sections: More on Repeated Measures 1038 Topic 8.9 Repeated Measures: Mixed Designs 1041 Topic 8.10 Repeated Measures: Advanced Material 1063 Topic 8.11 Randomization Testing for Repeated Measures 1084 Exercises 1091 Unit C: Logistic Regression 1098 Chapter 9: Logistic Regression 1101 9.1 Choosing a Logistic Regression Model 1105 9.2 Logistic Regression and Odds Ratios 1120 9.3 Assessing the Logistic Regression Model 1137 9.4 Formal Inference: Tests and Intervals 1149 Chapter Summary 1164 Exercises 1167 Chapter 10: Multiple Logistic Regression 1192 10.1 Overview 1196 10.2 Choosing, Fitting, and Interpreting Models 1200 10.3 Checking Conditions 1220 10.4 Formal Inference: Tests and Intervals 1239 10.5 Case Study: Attractiveness and Fidelity 1259 Chapter Summary 1269 Exercises 1270 Chapter 11: Additional Topics in Logistic Regression 1303 Topic 11.1 Fitting the Logistic Regression Model 1307 Topic 11.2 Assessing Logistic Regression Models 1315 Topic 11.3 Randomization Tests for Logistic Regression 1342 Topic 11.4 Analyzing Two-way Tables with Logistic Regression 1348 Topic 11.5 Simpson’s Paradox 1363 Exercises 1374 Unit D: Time Series Analysis 1399 Chapter 12: Time Series Analysis 1401 12.1 Functions of Time 1405 12.2 Measuring Dependence on Past Values: Autocorrelation 1429 12.3 ARIMA Models 1445 12.4 Case Study: Residual Oil 1463 Chapter Summary 1482 Exercises 1485 Answers to Selected Exercises 1502 Notes and Data Sources 1577 General Index 1603 Dataset Index 1627 Statistics