Student Solutions Manual to accompany Statistics: Unlocking the Power of Data, 2e

This is the Student Solutions Manual to Accompany Statistics: Unlocking the Power of Data, 2nd Edition.

Statistics, 2^nd Edition moves the curriculum in innovative ways while still looking relatively familiar. Statistics, 2e utilizes intuitive methods to introduce the fundamental idea of statistical inference. These intuitive methods are enabled through statistical software and are accessible at very early stages of a course. The text also includes the more traditional methods such as t-tests, chi-square tests, etc., but only after students have developed a strong intuitive understanding of inference through randomization methods. The text is designed for use in a one-semester introductory statistics course. 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 text is driven by real data and real applications. Students completing the course should be able to accurately interpret statistical results and to analyze straightforward data sets.

Patti Frazer Lock is the Cummings Professor of Mathematics in the Department of Mathematics, Computer Science, and Statistics at St. Lawrence University. She is a member of the Calculus Consortium for Higher Education (formerly the Calculus Consortium based at Harvard).  She is a co-author with the Consortium of texts in Calculus, Applied Calculus, Multivariable Calculus, Precalculus, and Algebra.  She is currently working on a text in Introductory Statistics.  She does workshops around the country on the teaching of undergraduate mathematics.  She is a member of the Committee on the Undergraduate Program in Mathematics of the Mathematics Association of America, is on the Editorial Board of PRIMUS Journal, and is a Consultant to Project NExT of the MAA.  She loves to teach and teaches courses across the spectrum of mathematics and statistics, and she enjoys collaborating with undergraduates on her research in graph theory. She received her BA from Colgate University and her Ph.D. from the University of Massachusetts at Amherst.

Chapter 1 Collecting Data 1

Chapter 2 Describing Data 8

Unit A Essential Synthesis 36

Chapter 3 Confidence Intervals 51

Chapter 4 Hypothesis Tests 71

Unit B Essential Synthesis 100

Chapter 5 Approximating with a Distribution 115

Chapter 6 Inference for Means and Proportions 126

Unit C Essential Synthesis 176

Chapter 7 Chi-Square Tests for Categorical Variables 198

Chapter 8 ANOVA to Compare Means 210

Chapter 9 Inference for Regression 221

Chapter 10 Multiple Regression 233

Unit D Essential Synthesis 246

The Big Picutre Final Essential Synthesis 254

Chapter P Probability Basics 261

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