Statistics MyLab Revision with Tech Updates (13th Edition)[2021] - Original PDF

دانلود کتاب Statistics MyLab Revision with Tech Updates (13th Edition)[2021] - Original PDF

Author: James T. McClave; Terry Sincich

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Statistics , 13th Edition by James McClave and Terry Sincich offers a trusted

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This print textbook is available for students to rent for their classes. The Pearson print rental program provides students with affordable access to learning materials, so they come to class ready to succeed. 

 

 

For courses in Introductory Statistics.

 

A contemporary classic

Statistics , 13th Edition by James McClave and Terry Sincich offers a trusted, comprehensive introduction to statistics that emphasizes inference and integrates real data throughout. The authors stress the development of statistical thinking, the assessment of credibility, and value of the inferences made from data. Ideal for one- or two-semester courses in introductory statistics, the text assumes a mathematical background of basic algebra. Flexibility is built in for instructors who teach a more advanced course, with optional footnotes about calculus and the underlying theory. The print book has been reprinted with new and updated statistical software screenshots. 

 

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By combining trusted author content with digital tools and a flexible platform, MyLab™ Statistics personalizes the learning experience and improves results for each student. With MyLab Statistics and StatCrunch®, an integrated web-based statistical software program, students learn the skills they need to interact with data in the real world. For  Statistics, 13th Edition, a new MyLab Revision is available that increases coverage of the end-of-section exercises throughout the book, and adds 22 brand-new, author created videos that further explore section topics and real-world case studies. 

 

0135820103 / 9780135820100  STATISTICS [RENTAL EDITION], 13/e

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این کتاب درسی چاپی در اختیار دانش‌آموزان است تا برای کلاس‌هایشان اجاره کنند. برنامه اجاره چاپ پیرسون دسترسی مقرون به صرفه ای به مطالب آموزشی برای دانش آموزان فراهم می کند، بنابراین آنها برای موفقیت به کلاس می آیند.

 

 

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آمار ، نسخه سیزدهم توسط جیمز مک کلاو و تری سینجیچ مقدمه ای قابل اعتماد و جامع برای آمار ارائه می دهد که بر استنتاج تاکید می کند و داده های واقعی را در سراسر ادغام می کند. . نویسندگان بر توسعه تفکر آماری، ارزیابی اعتبار و ارزش استنباط‌های حاصل از داده‌ها تاکید دارند. ایده آل برای دوره های یک یا دو ترم در آمار مقدماتی، متن پیش زمینه ریاضی از جبر پایه را در نظر می گیرد. انعطاف‌پذیری برای مدرسانی که دوره‌های پیشرفته‌تری را با پاورقی‌های اختیاری درباره حساب دیفرانسیل و انتگرال و تئوری زیربنایی تدریس می‌کنند، تعبیه شده است. کتاب چاپی با اسکرین شات های نرم افزار آماری جدید و به روز تجدید چاپ شده است.

 

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MyLab™ Statistics با ترکیب محتوای نویسنده قابل اعتماد با ابزارهای دیجیتال و یک پلتفرم انعطاف پذیر، تجربه یادگیری را شخصی می کند و نتایج را برای هر دانش آموز بهبود می بخشد. با MyLab Statistics و StatCrunch®، یک برنامه نرم افزار آماری یکپارچه مبتنی بر وب، دانش آموزان مهارت هایی را که برای تعامل با داده ها در دنیای واقعی نیاز دارند، یاد می گیرند. برای  آمار، ویرایش سیزدهم، یک نسخه MyLab جدید موجود است که پوشش تمرین‌های پایان بخش را در سراسر کتاب افزایش می‌دهد و اضافه می‌کند. 22 ویدیوی کاملاً جدید، نویسنده ایجاد شده که موضوعات بخش و مطالعات موردی در دنیای واقعی را بیشتر بررسی می کند.

 

0135820103 / 9780135820100  STATISTICS [نسخه اجاره ای]، 13/e

 

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Ebook details:
عنوان: Statistics, Updated Edition [RENTAL EDITION] (13th Edition) (9780135820100) McClave, James T., Sincich, Terry
نویسنده: Books
ناشر: Pearson; 13 edition (January 12, 2020)
زبان: English
شابک: 0135820103, 978-0135820100

9780136755975, 0136755976

9780136755951, 013675595X
حجم: 328 Mb
فرمت: Original PDF

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Statistics Statistics MyLab Revision with Tech Updates Contents Preface New in the 13th Edition Content-Specific Changes to This Edition Hallmark Strengths Flexibility in Coverage Get the most out of MyLab Statistics Resources for Success Applications Index 1 Statistics, Data, and Statistical Thinking Contents Where We’re Going 1.1 The Science of Statistics 1.2 Types of Statistical Applications Study 1.1 “Best-Selling Girl Scout Cookies” Study 1.2 “Are Action Video Game Players Better than Non-gamers at Complex, Divided Attention Tasks?” Study 1.3 “Does Rudeness Really Matter in the Workplace?” 1.3 Fundamental Elements of Statistics 1.4 Types of Data 1.5 Collecting Data: Sampling and Related Issues 1.6 The Role of Statistics in Critical Thinking and Ethics Chapter Notes Key Terms Key Ideas Types of Statistical Applications Descriptive Inferential Types of Data Data Collection Methods Types of Random Samples Problems with Nonrandom Samples Exercises 1.1–1.36 Understanding the Principles [Optional Applet Exercise] Applet Exercise 1.1 [Optional Applet Exercise] Applet Exercise 1.2 Applying the Concepts—Basic Applying the Concepts–Intermediate Applying the Concepts—Advanced Critical Thinking Challenges References 2 Methods for Describing Sets of Data Contents Where We’ve Been Where We’re Going 2.1 Describing Qualitative Data Exercises 2.1–2.24 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 2.2 Graphical Methods for Describing Quantitative Data Dot Plots Stem-and-Leaf Display Histograms Exercises 2.25–2.48 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 2.3 Numerical Measures of Central Tendency Exercises 2.49–2.72 Understanding the Principles Learning the Mechanics [Optional Applet Exercise] Applet Exercise 2.1 [Optional Applet Exercise] Applet Exercise 2.2 [Optional Applet Exercise] Applet Exercise 2.3 Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 2.4 Numerical Measures of Variability Exercises 2.73–2.92 Understanding the Principles Learning the Mechanics [Optional Applet Exercise] Applet Exercise 2.4 [Optional Applet Exercise] Applet Exercise 2.5 [Optional Applet Exercise] Applet Exercise 2.6 Applying the Concepts—Basic Applying the Concepts—Intermediate 2.5 Using the Mean and Standard Deviation to Describe Data Exercises 2.93–2.113 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 2.6 Numerical Measures of Relative Standing Exercises 2.114–2.131 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 2.7 Methods for Detecting Outliers: Box Plots and z-Scores Exercises 2.132–2.153 Understanding the Principles Learning the Mechanics [Optional Applet Exercise] Applet Exercise 2.7 Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 2.8 Graphing Bivariate Relationships (Optional) Exercises 2.154–2.169 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 2.9 Distorting the Truth with Descriptive Statistics Graphical Distortions Misleading Numerical Descriptive Statistics Exercises 2.170–2.173 Applying the Concepts—Intermediate Chapter Notes Key Terms Key Symbols Key Ideas Describing Qualitative Data Graphing Quantitative Data Rules for Describing Quantitative Data Rules for Detecting Quantitative Outliers Guide to Selecting the Data Description Method Supplementary Exercises 2.174–2.213 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Critical Thinking Challenges References 3 Probability Contents Where We’ve Been Where We’re Going 3.1 Events, Sample Spaces, and Probability Exercises 3.1–3.37 Understanding the Principles Learning the Mechanics [Optional Applet Exercise] Applet Exercise 3.1 [Optional Applet Exercise] Applet Exercise 3.2 Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 3.2 Unions and Intersections 3.3 Complementary Events 3.4 The Additive Rule and Mutually Exclusive Events Exercises 3.38–3.67 Understanding the Principles Learning the Mechanics [Optional Applet Exercise] Applet Exercise 3.3 [Optional Applet Exercise] Applet Exercise 3.4 Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 3.5 Conditional Probability 3.6 The Multiplicative Rule and Independent Events Exercises 3.68–3.103 Understanding the Principles Learning the Mechanics [Optional Applet Exercise] Applet Exercise 3.5 Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 3.7 Some Additional Counting Rules (Optional) Exercises 3.104–3.131 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 3.8 Bayes’s Rule (Optional) Exercises 3.132–3.147 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Chapter Notes Key Terms Key Symbols Key Ideas Probability Rules for k Sample Points, S1, S2, S3, …, Sk Combinations Rule Bayes’s Rule Guide to Selecting Probability Rules Supplementary Exercises 3.148–3.196 Understanding the Principles Learning the Mechanics [Optional Applet Exercise] Applet Exercise 3.6 Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Critical Thinking Challenges References 4 Discrete Random Variables Contents Where We’ve Been Where We’re Going 4.1 Two Types of Random Variables Exercises 4.1–4.16 Understanding the Principles Applying the Concepts—Basic Applying the Concepts—Intermediate 4.2 Probability Distributions for Discrete Random Variables Exercises 4.17–4.39 Understanding the Principles Learning the Mechanics [Optional Applet Exercise] Applet Exercise 4.1 [Optional Applet Exercise] Applet Exercise 4.2 Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 4.3 Expected Values of Discrete Random Variables Exercises 4.40–4.56 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 4.4 The Binomial Random Variable Using Tables and Technology for Binomial Probabilities Exercises 4.57–4.84 Understanding the Principles Learning the Mechanics [Optional Applet Exercise] Applet Exercise 4.3 [Optional Applet Exercise] Applet Exercise 4.4 [Optional Applet Exercise] Applet Exercise 4.5 Applying the Concepts—Basic Apply the Concepts—Intermediate Applying the Concepts—Advanced 4.5 The Poisson Random Variable (Optional) Exercises 4.85–4.106 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 4.6 The Hypergeometric Random Variable (Optional) Exercises 4.107–4.127 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Chapter Notes Key Terms Key Symbols Key Ideas Properties of Discrete Probability Distributions Guide to Selecting a Discrete Probability Distribution Key Formulas Supplementary Exercises 4.128–4.160 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Critical Thinking Challenge References 5 Continuous Random Variables Contents Where We’ve Been Where We’re Going 5.1 Continuous Probability Distributions 5.2 The Uniform Distribution Exercises 5.1–5.20 Understanding the Principles Learning the Mechanics [Optional Applet Exercise] Applet Exercise 5.1 [Optional Applet Exercise] Applet Exercise 5.2 Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 5.3 The Normal Distribution Exercises 5.21–5.54 Understanding the Principles Learning the Mechanics [Optional Applet Exercise] Applet Exercise 5.3 Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 5.4 Descriptive Methods for Assessing Normality Exercises 5.55–5.76 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 5.5 Approximating a Binomial Distribution with a Normal Distribution (Optional) Exercises 5.77–5.96 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 5.6 The Exponential Distribution (Optional) Exercises 5.97–5.114 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Chapter Notes Key Terms Key Symbols Key Ideas Properties of Continuous Probability Distributions Methods for Assessing Normality Normal Approximation to Binomial Key Formulas Guide to Selecting a Probability Distribution Supplementary Exercises 5.115–5.157 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Critical Thinking Challenges References 6 Sampling Distributions Contents Where We’ve Been Where We’re Going 6.1 The Concept of a Sampling Distribution Exercises 6.1–6.9 Understanding the Principles Learning the Mechanics 6.2 Properties of Sampling Distributions: Unbiasedness and Minimum Variance Exercises 6.10–6.20 Understanding the Principles Learning the Mechanics 6.3 The Sampling Distribution of x¯ and the Central Limit Theorem Exercises 6.21–6.48 Understanding the Principles Learning the Mechanics [Optional Applet Exercise] Applet Exercise 6.1 [Optional Applet Exercise] Applet Exercise 6.2 Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 6.4 The Sampling Distribution of the Sample Proportion Exercises 6.49–6.62 Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Chapter Notes Key Terms Key Formulas Key Ideas Key Symbols Generating the Sampling Distribution of x¯ Supplementary Exercises 6.63–6.93 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Critical Thinking Challenge References 7 Inferences Based on a Single Sample Estimation with Confidence Intervals Contents Where We’ve Been Where We’re Going 7.1 Identifying and Estimating the Target Parameter 7.2 Confidence Interval for a Population Mean: Normal (z) Statistic Exercises 7.1–7.28 Understanding the Principles Learning the Mechanics [Optional Applet Exercise] Applet Exercise 7.1 [Optional Applet Exercise] Applet Exercise 7.2 Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 7.3 Confidence Interval for a Population Mean: Student’s t-Statistic Problem 1 Solution to Problem 1 Problem 2 Solution to Problem 2 Exercises 7.29–7.51 Understanding the Principles [Optional Applet Exercise] Applet Exercise 7.3 [Optional Applet Exercise] Applet Exercise 7.4 Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 7.4 Large-Sample Confidence Interval for a Population Proportion Exercises 7.52–7.73 Understanding the Principles [Optional Applet Exercise] Applet Exercise 7.5 [Optional Applet Exercise] Applet Exercise 7.6 Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 7.5 Determining the Sample Size Estimating a Population Mean Estimating a Population Proportion Exercises 7.74–7.98 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 7.6 Confidence Interval for a Population Variance (Optional) Exercises 7.99–7.117 Understanding the Principles Learning the Mechanics Applying the Concepts—Basics Applying the Concepts—Intermediate Chapter Notes Key Terms Key Symbols Key Ideas Population Parameters, Estimators, & Standard Errors Determining the Sample Size n: Key Words for Identifying the Target Parameter: Commonly Used z-values for a Large-Sample Confidence Interval for μ or p: Illustrating the Notion of “95% Confidence” Guide to Forming a Confidence Interval Supplementary Exercises 7.118–7.152 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Critical Thinking Challenge References 8 Inferences Based on a Single Sample Tests of Hypothesis Contents Where We’ve Been Where We’re Going 8.1 The Elements of a Test of Hypothesis 8.2 Formulating Hypotheses and Setting Up the Rejection Region Exercises 8.1–8.21 Understanding the Principles Learning the Mechanics [Optional Applet Exercise] Applet Exercise 8.1 Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 8.3 Observed Significance Levels: p-Values Exercises 8.22–8.30 Learning the Mechanics 8.4 Test of Hypothesis about a Population Mean: Normal (z) Statistic Exercises 8.31–8.50 Understanding the Principles Learning the Mechanics [Optional Applet Exercise] Applet Exercise 8.2 [Optional Applet Exercise] Applet Exercise 8.3 [Optional Applet Exercise] Applet Exercise 8.4 Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 8.5 Test of Hypothesis about a Population Mean: Student’s t-Statistic Exercises 8.51–8.72 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 8.6 Large-Sample Test of Hypothesis about a Population Proportion Small samples Exercises 8.73–8.93 Understanding the Principles Learning the Mechanics [Optional Applet Exercise] Applet Exercise 8.5 [Optional Applet Exercise] Applet Exercise 8.6 Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 8.7 Calculating Type II Error Probabilities: More about β (Optional) Exercises 8.94–8.107 Understanding the Principles Learning the Mechanics Applying the Concepts—Intermediate 8.8 Test of Hypothesis about a Population Variance (Optional) Exercises 8.108–8.128 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Chapter Notes Key Terms Key Symbols Key Ideas Key Words for Identifying the Target Parameter Elements of a Hypothesis Test Forms of Alternative Hypothesis Using p-Values to Decide Guide to Selecting a One-Sample Hypothesis Test Supplementary Exercises 8.129–8.170 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Critical Thinking Challenges References 9 Inferences Based on Two Samples Confidence Intervals and Tests of Hypotheses Contents Where We’ve Been Where We’re Going 9.1 Identifying the Target Parameter 9.2 Comparing Two Population Means: Independent Sampling Large Samples Small Samples Exercises 9.1–9.30 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 9.3 Comparing Two Population Means: Paired Difference Experiments Exercises 9.31–9.54 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 9.4 Comparing Two Population Proportions: Independent Sampling Exercises 9.55–9.77 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 9.5 Determining the Sample Size Exercises 9.78–9.95 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate 9.6 Comparing Two Population Variances: Independent Sampling (Optional) Exercises 9.96–9.117 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Chapter Notes Key Terms Key Symbols Key Ideas Key Words for Identifying the Target Parameter Determining the Sample Size Conditions Required for Inferences about μ1 − μ2 Large samples: Small samples: *Conditions Required for Inferences about σ12/σ22 Large or small samples: Conditions Required for Inferences about μd Large samples: Small samples: Conditions Required for Inferences about p1 − p2 Large samples: Using a Confidence Interval for (μ1 − μ2) or (p1 − p2) to Determine whether a Difference Exists Guide to Selecting a Two-Sample Hypothesis Test and Confidence Interval Supplementary Exercises 9.118–9.152 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Critical Thinking Challenges References 10 Analysis of Variance Comparing More than Two Means Contents Where We’ve Been Where We’re Going 10.1 Elements of a Designed Study Exercises 10.1–10.16 Understanding the Principles Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 10.2 The Completely Randomized Design: Single Factor Exercises 10.17–10.43 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 10.3 Multiple Comparisons of Means Exercises 10.44–10.64 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 10.4 The Randomized Block Design Exercises 10.65–10.84 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 10.5 Factorial Experiments: Two Factors Test for Treatment Means Test for Factor Interaction Test for Main Effect of Factor A Test for Main Effect of Factor B Exercises 10.85–10.109 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Chapter Notes Key Terms Guide to Selecting an Experimental Design Guide to Conducting ANOVA F–Tests Key Symbols/Notation Key Ideas Key Elements of a Designed Experiment Balanced Design Tests for Main Effects in a Factorial Design Conditions Required for Valid F-Test in a Completely Randomized Design Conditions Required for Valid F-Tests in a Randomized Block Design Conditions Required for Valid F-Tests in a Complete Factorial Design Robust Method Experimentwise Error Rate Number of Pairwise Comparisons with k Treatment Means Multiple-Comparison-of-Means Methods Tukey Bonferroni Scheffé Supplementary Exercises 10.110–10.141 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Critical Thinking Challenges References 11 Simple Linear Regression Contents Where We’ve Been Where We’re Going 11.1 Probabilistic Models Exercises 11.1–11.14 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic 11.2 Fitting the Model: The Least Squares Approach Exercises 11.15–11.36 Understanding the Principles Learning the Mechanics [Optional Applet Exercise] Applet Exercise 11.1 Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 11.3 Model Assumptions Exercises 11.37–11.52 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 11.4 Assessing the Utility of the Model: Making Inferences about the Slope β1 Exercises 11.53–11.76 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 11.5 The Coefficients of Correlation and Determination Coefficient of Correlation Coefficient of Determination Exercises 11.77–11.100 Understanding the Principles Learning the Mechanics [Optional Applet Exercise] Applet Exercise 11.2 Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 11.6 Using the Model for Estimation and Prediction Exercises 11.101–11.121 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 11.7 A Complete Example Exercises 11.122–11.125 Applying the Concepts—Intermediate Chapter Notes Key Terms Key Symbols/Notation Key Ideas Simple Linear Regression Variables Method of least squares properties First-order (straight-line) model Practical interpretation of y-intercept Practical interpretation of slope Coefficient of correlation, r Coefficient of determination, r2 Practical interpretation of model standard deviation s Comparing Intervals in Step 5 Key Formulas Guide to Simple Linear Regression Supplementary Exercises 11.126–11.153 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Critical Thinking Challenge References 12 Multiple Regression and Model Building Contents Where We’ve Been Where We’re Going 12.1 Multiple-Regression Models Part I: First-Order Models with Quantitative Independent Variables 12.2 Estimating and Making Inferences about the β Parameters 12.3 Evaluating Overall Model Utility Exercises 12.1–12.30 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 12.4 Using the Model for Estimation and Prediction Exercises 12.31–12.41 Understanding the Principles Applying the Concepts—Basic Applying the Concepts—Intermediate Part II: Model Building in Multiple Regression 12.5 Interaction Models Exercises 12.42–12.59 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate 12.6 Quadratic and Other Higher Order Models Exercises 12.60–12.80 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 12.7 Qualitative (Dummy) Variable Models Exercises 12.81–12.99 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 12.8 Models with Both Quantitative and Qualitative Variables (Optional) Exercises 12.100–12.116 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 12.9 Comparing Nested Models (Optional) Exercises 12.117–12.134 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 12.10 Stepwise Regression (Optional) Exercises 12.135–12.145 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Part III: Multiple Regression Diagnostics 12.11 Residual Analysis: Checking the Regression Assumptions Checking Assumption #1: Mean ε=0 Checking Assumption #2: Constant Error Variance Checking Assumption #3: Errors Normally Distributed Checking Assumption #4: Errors Independent Summary 12.12 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation Problem 1: Parameter Estimability Problem 2: Multicollinearity Problem 3: Prediction Outside the Experimental Region Exercises 12.146–12.168 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Guide to Multiple Regression Chapter Notes Key Terms Key Symbols Key Ideas Multiple-Regression Variables First-Order Model in k Quantitative x's Interaction Model in 2 Quantitative x's Quadratic Model in 1 Quantitative x Complete Second-Order Model in 2 Quantitative x's Dummy Variable Model for 1 Qualitative x *Complete Second-Order Model in 1 Quantitative x and 1 Qualitative x (Two Levels, A and B) Interaction between x1 and x2 Adjusted Coefficient of Determination, Ra2 Parsimonious Model *Nested Models Recommendation for Assessing Model Adequacy Recommendation for Testing Individual β's *Problems with Using Stepwise Regression Model as the “Final” Model Analysis of Residuals Multicollinearity Extrapolation Key Formulas Supplementary Exercises 12.169–12.205 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Critical Thinking Challenges References 13 Categorical Data Analysis Contents Where We’ve Been Where We’re Going 13.1 Categorical Data and the Multinomial Experiment 13.2 Testing Categorical Probabilities: One-Way Table Exercises 13.1–13.21 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 13.3 Testing Categorical Probabilities: Two-Way (Contingency) Table Contingency Tables with Fixed Marginals Exercises 13.22–13.46 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 13.4 A Word of Caution about Chi-Square Tests Chapter Notes Key Terms Key Symbols/Notation Key Ideas Multinomial Data Properties of a Multinomial Experiment One-Way Table Two-Way (Contingency) Table Chi-Square (χ2) Statistic Chi-square tests for independence Conditions Required for Valid χ2 Tests Categorical Data Analysis Guide Supplementary Exercises 13.47–13.73 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced Critical Thinking Challenge References 14 Nonparametric Statistics Contents Where We’ve Been Where We’re Going 14.1 Introduction: Distribution-Free Tests 14.2 Single-Population Inferences Exercises 14.1–14.17 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate 14.3 Comparing Two Populations: Independent Samples Exercises 14.18–14.39 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts-Intermediate 14.4 Comparing Two Populations: Paired Difference Experiment Exercises 14.40–14.58 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Applying the Concepts—Advanced 14.5 Comparing Three or More Populations: Completely Randomized Design Exercises 14.59–14.74 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate 14.6 Comparing Three or More Populations: Randomized Block Design Exercises 14.75–14.90 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate 14.7 Rank Correlation Exercises 14.91—14.109 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Chapter Notes Key Terms Key Symbols Key Ideas Distribution-free Tests Nonparametrics Key Formulas Guide to Selecting a Nonparametric Method Supplementary Exercises 14.110–14.138 Understanding the Principles Learning the Mechanics Applying the Concepts—Basic Applying the Concepts—Intermediate Critical Thinking Challenge References TI-84 Plus Graphing Calculator: Nonparametric Tests Appendix A: Summation Notation Appendix B: Tables Appendix C: Calculation Formulas for Analysis of Variance C.1 Completely Randomized Design C.2 Randomized Block Design C.3 Two-Factor Factorial Experiment C.4 Tukey’s Multiple Comparisons Procedure (Equal Sample Sizes) C.5 Bonferroni Multiple Comparisons Procedure (Pairwise Comparisons) C.6 Scheffé’s Multiple Comparisons Procedure (Pairwise Comparisons) Short Answers to Selected Odd Exercises Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 Chapter 11 Chapter 12 Chapter 13 Chapter 14 Index Text and Photo Credits Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 Chapter 11 Chapter 12 Chapter 13 Chapter 14 (available online) Cover Images: Left to right Making Connections Using Data

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