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A STATISTICAL METHOD

TYPE OF DATA

Type of Analysis

Numerical

Categorical

Describing a group
or several groups

Ordered array, stem-and-leaf display, frequency
distribution, relative frequency distribution,
percentage distribution, cumulative percentage
distribution, histogram, polygon, cumulative
percentage polygon (Sections 2.3, 2.5)
Mean, median, mode, quartiles, geometric mean,
range, interquartile range, standard deviation,
variance, coefficient of variation, boxplot
(Sections 3.1, 3.2, 3.3)
Index numbers (Online Topic 16.8)

Summary table, bar chart, pie
chart, Pareto chart (Sections 2.2, 2.4)

Confidence interval estimate of the mean
(Sections 8.1 and 8.2)
t test for the mean (Section 9.2)
Chi-square test for a variance (Section 12.5)

Confidence interval estimate of the
proportion (Section 8.3)
Z test for the proportion
(Section 9.4)

Comparing two groups

Tests for the difference in the means of two
independent populations (Section 10.1)
Paired t test (Section 10.2)
F test for the difference between two variances
(Section 10.4)
Wilcoxon rank sum test (Section 12.6)
Wilcoxon signed ranks test (Online Topic 12.8)

Z test for the difference between
two proportions (Section 10.3)
Chi-square test for the difference
between two proportions
(Section 12.1)
McNemar test for the difference
between two proportions in related
samples (Section 12.4)

Comparing more
than two groups

One-way analysis of variance (Section 11.1)
Randomized block design (Section 11.2)
Two-way analysis of variance (Section 11.3)
Kruskal-Wallis test (Section 12.7)
Friedman rank test (Online Topic 12.9)

Chi-square test for differences
among more than two proportions
(Section 12.2)

Analyzing the
relationship between
two variables

Scatter plot, time series plot (Section 2.6)
Covariance, coefficient of correlation (Section 3.5)
Simple linear regression (Chapter 13)
t test of correlation (Section 13.7)
Time series forecasting (Chapter 16)

Contingency table, side-by-side
bar chart, (Sections 2.2, 2.4)
Chi-square test of independence
(Section 12.3)

Analyzing the
relationship between
two or more variables

Multiple regression (Chapters 14 and 15)

Multidimensional contingency
tables (Section 2.7)
Logistic regression (Section 14.7)

Statistics: Concepts and
Applications
TWELFTH EDITION

Statistics: Concepts and
Applications
TWELFTH EDITION

Mark L. Berenson
Department of Management and Information Systems
School of Business, Montclair State University

David M. Levine
Department of Statistics and Computer Information Systems
Zicklin School of Business, Baruch College, City University of New York

Timothy C. Krehbiel
Department of Management
Richard T. Farmer School of Business, Miami University

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Photo Credits: front, page viii, courtesy of Rudy Krehbiel; pp. 2–3: Photos.com; pp. 3, 7: Maga, Shutterstock;
pp. 14–15: Don Farrall, PhotoDisc/Getty Images; pp. 15, 59:Steve Coleccs, iStockphoto; pp. 84–85: Don Farrall,
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pp. 161, 183: Monkey Business Images, Shutterstock; pp. 192–193: Alexander Kalina, Shutterstock; pp. 193, 215:
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CIP data for this title is available on file at the Library of Congress

10 9 8 7 6 5 4 3 2 1
ISBN 10: 0-13-216838-3
ISBN 13: 978-0-13-216838-0

To our wives,
Rhoda B., Marilyn L., and, Patti K.,
and to our children,
Kathy, Lori, Sharyn, Ed, Rudy, and Rhonda

The textbook authors meet to discuss statistics at a Mets
baseball game. Shown left to right: David Levine, Mark
Berenson, and Tim Krehbiel.

Mark L. Berenson is Professor of Management and Information Systems at
Montclair State University (Montclair, New Jersey) and also Professor Emeritus of
Statistics and Computer Information Systems at Bernard M. Baruch College (City
University of New York). He currently teaches graduate and undergraduate courses in statistics and in operations management in the School of Business and an undergraduate
course in international justice and human rights that he co-developed in the College of
Humanities and Social Sciences.
Berenson received a B.A. in economic statistics and an M.B.A. in business statistics from
City College of New York and a Ph.D. in business from the City University of New York.
Berenson’s research has been published in Decision Sciences Journal of Innovative
Education, Review of Business Research, The American Statistician, Communications in
Statistics, Psychometrika, Educational and Psychological Measurement, Journal of
Management Sciences and Applied Cybernetics, Research Quarterly, Stats Magazine, The
New York Statistician, Journal of Health Administration Education, Journal of Behavioral
Medicine, and Journal of Surgical Oncology. His invited articles have appeared in The
Encyclopedia of Measurement &amp; Statistics and Encyclopedia of Statistical Sciences. He is
co-author of 11 statistics texts published by Prentice Hall, including Statistics for
Managers Using Microsoft Excel, Basic Business Statistics: Concepts and Applications,
and Business Statistics: A First Course.
Over the years, Berenson has received several awards for teaching and for innovative contributions to statistics education. In 2005, he was the first recipient of The Catherine A.
Becker Service for Educational Excellence Award at Montclair State University.

David M. Levine

is Professor Emeritus of Statistics and Computer Information Systems at Baruch College (City University of New York). He received B.B.A. and
M.B.A. degrees in Statistics from City College of New York and a Ph.D. from New York

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