{"product_id":"intermediate-statistical-investigations-isbn-9781119634522","title":"Intermediate Statistical Investigations","description":"\u003cp\u003e\u003ci\u003eIntermediate Statistical Investigations\u003c\/i\u003e provides a unified framework for explaining variation across study designs and variable types, helping students increase their statistical literacy and appreciate the indispensable role of statistics in scientific research. Requiring only a single introductory statistics course as a prerequisite, the program uses the immersive, simulation-based inference approach for which the author team is known.Students engage with various aspects of data collection and analysis using real examples and clear explanations designed to strengthen multivariable understanding and reinforce first-course concepts. \u003c\/p\u003e \u003cp\u003eEach chapter contains in-depth exercises which follow a consistent  six-step statistical exploration and investigation method (ask a research question, design a study, explore the data, draw inferences, formulate conclusions, and look back and ahead) enabling students to assess a variety of concepts in a single assignment. Challenging questions based on research articles strengthen critical reading skills, fully worked examples demonstrate essential concepts and methods, and engaging visualizations illustrate key themes of explained variation. End-of-chapter investigations use real data from popular culture and published research studies in a variety of disciplines, exposing students to various applications of statistics in the real world. Throughout the text, user-friendly Rossman Chance web applets allow students to conduct the simulations and analyses covered in the book.\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e \u003cp\u003ePreliminaries Multivariable Thinking and Sources of Variation 1\u003c\/p\u003e \u003cp\u003eExample P.A: Graduate School Admissions at Berkeley 2\u003c\/p\u003e \u003cp\u003eExploration P.A: Salary Discrimination 9\u003c\/p\u003e \u003cp\u003eExample P.B: Predicting Birth Weights 15\u003c\/p\u003e \u003cp\u003eExploration P.B: Housing Prices in Michigan 21\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 \u003c\/b\u003e\u003cb\u003eSources of Variation 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSection 1.1: Sources of Variation in an Experiment 32\u003c\/p\u003e \u003cp\u003eExample 1.1: Scents and Consumer Behavior 33\u003c\/p\u003e \u003cp\u003eExploration 1.1: Memorizing Letters 40\u003c\/p\u003e \u003cp\u003eSection 1.2: Quantifying Sources of Variation 44\u003c\/p\u003e \u003cp\u003eExample 1.2: Scents and Consumer Behavior cont. 44\u003c\/p\u003e \u003cp\u003eExploration 1.2: Starry Navigation 50\u003c\/p\u003e \u003cp\u003eSection 1.3: Is the Variation Explained Statistically Significant? 56\u003c\/p\u003e \u003cp\u003eExample 1.3: Scents and Consumer Behavior cont. 57\u003c\/p\u003e \u003cp\u003eExploration 1.3: Starry Navigation cont. 65\u003c\/p\u003e \u003cp\u003eSection 1.4: Comparing Several Groups 71\u003c\/p\u003e \u003cp\u003eExample 1.4: Fish Consumption and Omega-3 72\u003c\/p\u003e \u003cp\u003eExploration 1.4: Golden Squirrels 83\u003c\/p\u003e \u003cp\u003eSection 1.5: Confidence and Prediction Intervals 88\u003c\/p\u003e \u003cp\u003eExample 1.5: Fish Consumption and Omega-3 cont. 89\u003c\/p\u003e \u003cp\u003eExploration 1.5: Golden Squirrels cont. 97\u003c\/p\u003e \u003cp\u003eSection 1.6: More Study Design Considerations 101\u003c\/p\u003e \u003cp\u003eExample 1.6: Fish Consumption and Omega-3 (revisited) 101\u003c\/p\u003e \u003cp\u003eExploration 1.6: Who Is Spending More Time Parenting on Average? 109\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 \u003c\/b\u003e\u003cb\u003eControlling Additional Sources of Variation 138\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSection 2.1: Paired Data 139\u003c\/p\u003e \u003cp\u003eExample 2.1: Texts vs. Visual Distractions (Facebook vs. Instagram) 140\u003c\/p\u003e \u003cp\u003eExploration 2.1: Chip Melting Times 148\u003c\/p\u003e \u003cp\u003eSection 2.2: Randomized Complete Block Designs 152\u003c\/p\u003e \u003cp\u003eExample 2.2: What’s All the Fuss about Caffeine? 152\u003c\/p\u003e \u003cp\u003eExploration 2.2: Strawberry Storage 164\u003c\/p\u003e \u003cp\u003eSection 2.3: Observational Studies with Two Explanatory Variables 173\u003c\/p\u003e \u003cp\u003eExample 2.3: Salary Discrimination cont. 174\u003c\/p\u003e \u003cp\u003eExploration 2.3: Car Acceleration 182\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 \u003c\/b\u003e\u003cb\u003eMulti-factor Studies and Interactions 210\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSection 3.1: Multi-factor Experiments 211\u003c\/p\u003e \u003cp\u003eExample 3.1: Corporate Credibility, Endorser, and Purchase Intent 212\u003c\/p\u003e \u003cp\u003eExploration 3.1: Pig Growth 222\u003c\/p\u003e \u003cp\u003eSection 3.2: Statistical Interactions 228\u003c\/p\u003e \u003cp\u003eExample 3.2: Pistachio Bleaching 228\u003c\/p\u003e \u003cp\u003eExploration 3.2: Optimizing Ads 239\u003c\/p\u003e \u003cp\u003eSection 3.3: Replication 248\u003c\/p\u003e \u003cp\u003eExample 3.3: Optimizing Vitamin C 248\u003c\/p\u003e \u003cp\u003eExploration 3.3: Hurricane Names 257\u003c\/p\u003e \u003cp\u003eSection 3.4: Interactions in Observational Studies 262\u003c\/p\u003e \u003cp\u003eExample 3.4: Salary Discrimination revisited 262\u003c\/p\u003e \u003cp\u003eExploration 3.4: Hopelessness and Exercise 267\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Including a Quantitative Explanatory Variable 294\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSection 4.1: Quantitative Explanatory Variables 295\u003c\/p\u003e \u003cp\u003eExample 4.1: Recovering Polyphenols from Grape Seed 295\u003c\/p\u003e \u003cp\u003eExploration 4.1: Fatty Acids and DNA 304\u003c\/p\u003e \u003cp\u003eSection 4.2: Inference for Simple Linear Regression 308\u003c\/p\u003e \u003cp\u003eExample 4.2: Recovering Polyphenols from Grape Seed cont. 309\u003c\/p\u003e \u003cp\u003eExploration 4.2: Fatty Acids and DNA cont. 317\u003c\/p\u003e \u003cp\u003eSection 4.3: Quantitative and Categorical Explanatory Variables 322\u003c\/p\u003e \u003cp\u003eExample 4.3: Michigan Housing Prices 323\u003c\/p\u003e \u003cp\u003eExploration 4.3: Predicting Height 332\u003c\/p\u003e \u003cp\u003eSection 4.4: Quantitative\/Categorical Interactions 338\u003c\/p\u003e \u003cp\u003eExample 4.4: Michigan Housing Prices cont. 338\u003c\/p\u003e \u003cp\u003eExploration 4.4: FEV and Smoking 344\u003c\/p\u003e \u003cp\u003eSection 4.5: Multi-level Categorical Variables 348\u003c\/p\u003e \u003cp\u003eExample 4.5: Diamonds 348\u003c\/p\u003e \u003cp\u003eExploration 4.5: Patient Satisfaction 358\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Multiple Quantitative Explanatory Variables 383\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSection 5.1: Experiments with Multiple Quantitative Explanatory Variables 384\u003c\/p\u003e \u003cp\u003eExample 5.1: Pistachio Bleaching 384\u003c\/p\u003e \u003cp\u003eExploration 5.1: Biodiesel 397\u003c\/p\u003e \u003cp\u003eSection 5.2: Observational Studies with Multiple Quantitative Explanatory Variables 403\u003c\/p\u003e \u003cp\u003eExample 5.2: Brain Size and IQ 403\u003c\/p\u003e \u003cp\u003eExploration 5.2: SLO Real Estate Data 410\u003c\/p\u003e \u003cp\u003eSection 5.3: Modeling Nonlinear Associations Part I—Polynomial Models 414\u003c\/p\u003e \u003cp\u003eExample 5.3: Arctic Sea Ice 414\u003c\/p\u003e \u003cp\u003eExploration 5.3: Kentucky Derby Winning Times 419\u003c\/p\u003e \u003cp\u003eSection 5.4: Modeling Nonlinear Associations Part II—Transformations 421\u003c\/p\u003e \u003cp\u003eExample 5.4: Salary Discrimination cont. 422\u003c\/p\u003e \u003cp\u003eExploration 5.4A: Stopping Distances 424\u003c\/p\u003e \u003cp\u003eExploration 5.4B: Kentucky Derby Winning Times cont. 426\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Categorical Response Variable 447\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSection 6.1: Comparing Proportions 448\u003c\/p\u003e \u003cp\u003eExample 6.1: Encouraging Organ Donation 448\u003c\/p\u003e \u003cp\u003eExploration 6.1: Infant Attachment 460\u003c\/p\u003e \u003cp\u003eSection 6.2: Introduction to Logistic Regression 465\u003c\/p\u003e \u003cp\u003eExample 6.2: Smoking and Survival Rates 466\u003c\/p\u003e \u003cp\u003eExploration 6.2: Alcohol Abuse in Ukraine 472\u003c\/p\u003e \u003cp\u003eSection 6.3: Multiple Logistic Regression Models 476\u003c\/p\u003e \u003cp\u003eExample 6.3: Smoking and Survival Rates cont. 477\u003c\/p\u003e \u003cp\u003eExploration 6.3: Alcohol Abuse in Ukraine cont. 483\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Practical Issues 503\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSection 7.1: Dealing with the Messes Created by Messy Data 504\u003c\/p\u003e \u003cp\u003eExample 7.1: Public Health Screening Data for the Omega-3 Index 504\u003c\/p\u003e \u003cp\u003eExploration 7.1: Evaluating the Impact of a Water Filter Intervention 516\u003c\/p\u003e \u003cp\u003eSection 7.2: Multiple Regression with Many Explanatory Variables 524\u003c\/p\u003e \u003cp\u003eExample 7.2: Predicting Real Estate Prices 524\u003c\/p\u003e \u003cp\u003eExploration 7.2: Predicting Changes in Omega-3 Index Values 536\u003c\/p\u003e \u003cp\u003eSolutions to Selected Exercises 543\u003c\/p\u003e \u003cp\u003eIndex 579\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989444870373,"sku":"NP9781119634522","price":111.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119634522.jpg?v=1761784125","url":"https:\/\/k12savings.com\/products\/intermediate-statistical-investigations-isbn-9781119634522","provider":"K12savings","version":"1.0","type":"link"}