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STAT2: Modeling with Regression and ANOVA: Modelling with Regression and ANOVA 2nd ed. 2019 [Hardback]

  • Formāts: Hardback, 624 pages, height x width: 235x155 mm, 1 Hardback
  • Izdošanas datums: 21-Dec-2018
  • Izdevniecība: W.H.Freeman & Co Ltd
  • ISBN-10: 1319054072
  • ISBN-13: 9781319054076
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  • Cena: 102,82 €
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  • Formāts: Hardback, 624 pages, height x width: 235x155 mm, 1 Hardback
  • Izdošanas datums: 21-Dec-2018
  • Izdevniecība: W.H.Freeman & Co Ltd
  • ISBN-10: 1319054072
  • ISBN-13: 9781319054076
Citas grāmatas par šo tēmu:

Now available with Macmillan’s online learning platform Achieve Essentials, STAT2 introduces students to statistical modeling beyond what they have learned in a Stat 101 college course or an AP Statistics course.  Building on basic concepts and methods learned in that course, STAT2 empowers students to analyze richer datasets that include more variables and address a broader range of research questions.



Other than a working understanding of exponential and logarithmic functions, there are no prerequisites beyond successful completion of their first statistics course. To help all students make a smooth transition to this course, Chapter 0 reminds students of basic statistical terminology and also uses the familiar two-sample t-test as a way to illustrate the approach of specifying, estimating, and testing a statistical model.


Using STAT2, students will:



  • Go beyond their Stat 101 experience by learning to develop and apply models with both quantitative and categorical response variables, and with multiple explanatory variables. STAT2 Chapters are grouped into units that consider models based on the type of response and type of predictors.

  • Discover that the practice of statistical modeling involves applying an interactive process. STAT2 employs a four-step process in all statistical modeling: Choose a form for the model, fit the model to the data, assess how well the model describes the data, and use the model to address the question of interest.

  • Learn how to apply their developing judgment about statistical modeling. STAT2 introduces the idea of constructing statistical models at the very beginning, in a setting that students encountered in their Stat 101 course. This modeling focus continues throughout the course as students encounter new and increasingly more complicated scenarios.

  • Analyze and draw conclusions from real data, which is crucial for preparing students to use statistical modeling in their professional lives. STAT2 incorporates real and rich data throughout the text. Using real data to address genuine research questions helps motivate students to study statistics. The richness stems not only from interesting contexts in a variety of disciplines, but also from the multivariable nature of most datasets.


Achieve Essentials for Stat2 connects the problem-solving techniques and real world examples in the book to rich digital resources that foster further understanding and application of statistics. Assets in Achieve Essentials support learning before, during, and after class for students, while providing instructors with class performance analytics in an easy-to-use interface.


 

To the Teacher vi
Media and Supplements xi
Acknowledgments xiii
To the Student xvi
Chapter 0 What Is a Statistical Model?
1(20)
0.1 Model Basics
2(3)
0.2 A Four-Step Process
5(16)
UNIT A Linear Regression
Chapter 1 Simple Linear Regression
21(38)
1.1 The Simple Linear Regression Model
22(5)
1.2 Conditions for a Simple Linear Model
27(2)
1.3 Assessing Conditions
29(5)
1.4 Transformations/Reexpressions
34(9)
1.5 Outliers and Influential Points
43(16)
Chapter 2 Inference for Simple Linear Regression
59(28)
2.1 Inference for Regression Slope
60(4)
2.2 Partitioning Variability---ANOVA
64(3)
2.3 Regression and Correlation
67(3)
2.4 Intervals for Predictions
70(2)
2.5 Case Study: Butterfly Wings
72(15)
Chapter 3 Multiple Regression
87(66)
3.1 Multiple Linear Regression Model
90(2)
3.2 Assessing a Multiple Regression Model
92(6)
3.3 Comparing Two Regression Lines
98(9)
3.4 New Predictors from Old
107(14)
3.5 Correlated Predictors
121(6)
3.6 Testing Subsets of Predictors
127(5)
3.7 Case Study: Predicting in Retail Clothing
132(21)
Chapter 4 Additional Topics in Regression
153(44)
4.1 Topic: Added Variable Plots
154(2)
4.2 Topic: Techniques for Choosing Predictors
156(8)
4.3 Topic: Cross-validation
164(4)
4.4 Topic: Identifying Unusual Points in Regression
168(7)
4.5 Topic: Coding Categorical Predictors
175(6)
4.6 Topic: Randomization Test for a Relationship
181(3)
4.7 Topic: Bootstrap for Regression
184(13)
UNIT B Analysis of Variance
Chapter 5 One-way ANOVA and Randomized Experiments
197(60)
5.1 Overview of ANOVA
198(4)
5.2 The One-way Randomized Experiment and Its Observational Sibling
202(4)
5.3 Fitting the Model
206(10)
5.4 Formal Inference: Assessing and Using the Model
216(9)
5.5 How Big Is the Effect?: Confidence Intervals and Effect Sizes
225(6)
5.6 Using Plots to Help Choose a Scale for the Response
231(8)
5.7 Multiple Comparisons and Fisher's Least Significant Difference
239(3)
5.8 Case Study: Words with Friends
242(15)
Chapter 6 Blocking and Two-way ANOVA
257(42)
6.1 Choose: RCB Design and Its Observational Relatives
257(10)
6.2 Exploring Data from Block Designs
267(5)
6.3 Fitting the Model for a Block Design
272(5)
6.4 Assessing the Model for a Block Design
277(8)
6.5 Using the Model for a Block Design
285(14)
Chapter 7 ANOVA with interaction and Factorial Designs
299(44)
7.1 Interaction
300(5)
7.2 Design: The Two-way Factorial Experiment
305(3)
7.3 Exploring Two-way Data
308(7)
7.4 Fitting a Two-way Balanced ANOVA Model
315(6)
7.5 Assessing Fit: Do We Need a Transformation?
321(1)
7.6 Using a Two-way ANOVA Model
322(21)
Chapter 8 Additional Topics in Analysis of Variance
343(70)
8.1 Topic: Levene's Test for Homogeneity of Variances
344(4)
8.2 Topic: Multiple Tests
348(5)
8.3 Topic: Comparisons and Contrasts
353(7)
8.4 Topic: Nonparametric Statistics
360(5)
8.5 Topic: Randomization F-Test
365(9)
8.6 Topic: Repeated Measures Designs and Datasets
374(5)
8.7 Topic: ANOVA and Regression with Indicators
379(10)
8.8 Topic: Analysis of Covariance
389(24)
8.9 Repeated Measures: Mixed Designs
8.10 Repeated Measures: Advanced Material
8.11 Randomization Testing for Repeated Measures
UNIT C Logistic Regression
Chapter 9 Logistic Regression
413(40)
9.1 Choosing a Logistic Regression Model
414(10)
9.2 Logistic Regression and Odds Ratios
424(6)
9.3 Assessing the Logistic Regression Model
430(7)
9.4 Formal Inference: Tests and Intervals
437(16)
Chapter 10 Multiple Logistic Regression
453(44)
10.1 Overview
454(2)
10.2 Choosing, Fitting, and Interpreting Models
456(9)
10.3 Checking Conditions
465(8)
10.4 Formal Inference: Tests and Intervals
473(8)
10.5 Case Study: Attractiveness and Fidelity
481(16)
Chapter 11 Additional Topics in Logistic Regression
497(42)
11.1 Topic: Fitting the Logistic Regression Model
498(4)
11.2 Topic: Assessing Logistic Regression Models
502(12)
11.3 Topic: Randomization Tests for Logistic Regression
514(2)
11.4 Topic: Analyzing Two-Way Tables with Logistic Regression
516(6)
11.5 Topic: Simpson's Paradox
522(17)
UNIT D Time Series Analysis
Chapter 12 Time Series Analysis
539(50)
12.1 Functions of Time
540(11)
12.2 Measuring Dependence on Past Values: Autocorrelation
551(7)
12.3 ARIMA Models
558(13)
12.4 Case Study: Residual Oil
571(18)
Answers to Selected Exercises 589(9)
Notes and Data Sources 598(5)
General Index 603(3)
Dataset Index 606