Robin H. Lock

unlocking the power of data

Statistics

Dennis F. Lock

Kari Lock Morgan

Eric F. Lock

Patti Frazer Lock

Statistics: Unlocking the Power of Data, 3rd Edition is designed for an introductory statistics course focusing on data analysis with real-world applications. Students use simulation methods to effectively collect, analyze, and interpret data to draw conclusions. Randomization and bootstrap interval methods introduce the fundamentals of statistical inference, bringing concepts to life through authentically relevant examples. More traditional methods like t-tests, chi-square tests, etc. are introduced after students have developed a strong intuitive understanding of inference through randomization methods. While any popular statistical software package may be used, the authors have created StatKey to perform simulations using data sets and examples from the text. A variety of videos, activities, and a modular chapter on probability are adaptable to many classroom formats and approaches.

Unlocking the Power of Data

Statistics: Unlocking the Power of Data, 4th Edition, with corequisite support content, is designed for an introductory statistics course focusing on data analysis using real-world data and applications. Students use simulation methods to effectively collect, analyze, and interpret data to draw conclusions. Randomization and bootstrap interval methods introduce the fundamentals of statistical inference, bringing concepts to life through authentically relevant examples. More traditional methods like t-tests, chi-square tests, etc. are introduced after students have developed a strong intuitive understanding of inference through randomization methods. While any popular statistical software package may be used, the authors have created StatKey to perform simulations using data sets and examples from the text. A variety of videos, activities, and a modular chapter on probability are adaptable to many classroom formats and approaches.

George W. Cobb

Allan J. Rossman

Thomas L. Moore

Ann R. Cannon

Bradley A. Hartlaub

Julie M. Legler

Jeffrey A. Witmer

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.

Modeling with Regression and ANOVA

STAT2

"This book takes a unique approach of utilizing computer simulation methods to introduce students to the key ideas of statistical inference.Methods such as bootstrap intervals and randomization tests are very intuitive to novice students and capitalize on visual learning skills students bring to the classroom. With proper use of computer support, they are accessible at very early stages of a course with little formal background. Our text introduces statistical inference through these resampling and randomization methods, not only because these methods are becoming increasingly important for statisticians in their own right but also because they are outstanding in building students' conceptual understanding of the key ideas"--

Modeling With Regression and ANOVA

Stat2

Beth L. Chance

This book focuses on probability and the Bayesian viewpoint. It presents basic material on probability and then introduces inference by means of Bayes'rule. The emphasis is on statistical thinking and how one learns from data. The objective is to present the basic tenets of statistical inference. Unique in its format, the text allows students to discover statistical concepts, explore statistical principles, and apply statistical techniques. In addition to the numerous activities and exercises around which the text is built, the book includes a basic text exposition for each topic, and data appendices.

Discovery with Data and Fathom

Workshop Statistics

Statistics: Unlocking the Power of Data, 2nd Edition Loose-leaf Print Companion continues to utilize these intuitive methods like randomization and bootstrap intervals to introduce the fundamental idea of statistical inference. These methods are brought to life through authentically relevant examples, enabled through easy to use statistical software, and are accessible at very early stages of a course. The program includes the more traditional methods like t-tests, chi-square texts, etc. but only after students have developed a strong intuitive understanding of inference through randomization methods. The focus throughout is on data analysis and the primary goal is to enable students to effectively collect data, analyze data, and interpret conclusions drawn from data. The program is driven by real data and real applications.

Statistics: Unlocking the Power of Data, Loose-leaf Print Companion

Statistics: Unlocking the Power of Data, 2nd Edition continues to utilize these intuitive methods like randomization and bootstrap intervals to introduce the fundamental idea of statistical inference. These methods are brought to life through authentically relevant examples and are accessible at very early stages of the text. Applications are drawn from a wide variety of disciplines, chosen primarily on the basis of perceived interest to students and instructors. Problems and exercises are plentiful and span a wide range of difficulty levels, from very straightforward short answer problems to extended projects.

Robin H. Lock

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