An imaginative introduction to statistics, reorienting the course towards an understanding of statistical thinking and its meaning and use in daily life and work. Gudmund Iversen and Mary Gergen bring their years of experience and insight into teaching the subject, incorporating such innovations and insights as a sustained emphasis on the process of statistical analysis and what statistics can and cannot do as well as careful exposition of the ideas of developing statistical and graphical literacy. In the spirit of contemporary pedagogy and by using technology, the authors break down the traditional barriers of statistical formulas and lengthy computations encountered by students without strong quantitative skills. Further, formulas are grouped at the end of each chapter along with related problems, and, with only algebra as a prerequisite, the book is ideal for students in the liberal arts and the behavioural and social sciences.
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Editorial Reviews
Review
A fantastic, welcome addition to the "Introductory Statistics" literature. Well-written, topical, humorous, and sound...Excellent exercises are divided into Review, Interpretation, and Analysis questions. This text deserves a careful look from anyone teaching an introductory statistics course. -- The American Mathematical Monthly, April, 1998
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Most Helpful Customer Reviews
3 of 3 people found the following review helpful:
5.0 out of 5 stars
A thoughtful, well written book,
By A Customer
This review is from: Statistics: the Conceptual Approach (Hardcover)
I examined a number of statistics texts from the university library and this one was streets ahead of any other in the quality of its explanations and discussion of statistical concepts. Statistics is not mathematics and, perhaps, for mathematicians this book might be a little simple. It is my experience that those obsessed with the maths of stats really know very little about the problems of the sciences for which stats is a useful tool. This book is for those who want a `conceptual' approach
2 of 2 people found the following review helpful:
5.0 out of 5 stars
Outstanding introduction to important statistical concepts.,
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This review is from: Statistics: the Conceptual Approach (Hardcover)
If you learn things more easily when you understand the underlying theory or concepts behind how they work, this book is for you. If you are comfortable just knowing the formulas and doing the math, this book is proabably not a good choice. My learning style is the former, so I gobbled this book up and enjoyed every page. I am not new to statistics but have never liked the terse, poorly explained, math-speak that turns most statistics books into barely comprehensible jargon-choked slogs. If you have to use statistics to do your job or need to understand why to use certain types of tests rather than others, read this book.
5.0 out of 5 stars
Statistics : The Conceptual Approach,
By A Customer
This review is from: Statistics: the Conceptual Approach (Hardcover)
As the title of this book states, it takes a "conceptual approach." For an introductory audience, it takes great steps to de-mystify statistics. It instills an intuitive knowledge of statistics in the reader, a feature sorely lacking in most, more technical books. This book is user friendly and even, dare I say, FUN to read.
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You probably are reading this book because you think it is important to know something about the subject of statistics. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
partial phi, calorie variable, two metric variables, more extreme data, true population percentage, metric dependent variable, nican number, residual variable, ticular interval, rank variables, graphical excellence, egorical variables, fat variable, nonrush hour, mill hypothesis, many different samples, collecting observational data, ice cream consumption, nonresponse error, calorie values, melanoma incidences, null hypothesis states Key Phrases - Capitalized Phrases (CAPs): (learn more)
United States, New York, New England, Quantitative Applications, Sage University Paper Series, High Street, Beverly Hills, Description of Data, Pacific Coast, Bureau of the Census, East Coast, Swarthmore College, The Free Press, World War, North Dakota, African Americans, Los Angeles, Newbury Park, American Sociological Review, Census Bureau, New Jersey, Springer Web, Alf Landon, Englewood Cliffs, Health Statistics New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
You probably are reading this book because you think it is important to know something about the subject of statistics. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
partial phi, calorie variable, two metric variables, more extreme data, true population percentage, metric dependent variable, nican number, residual variable, ticular interval, rank variables, graphical excellence, egorical variables, fat variable, nonrush hour, mill hypothesis, many different samples, collecting observational data, ice cream consumption, nonresponse error, calorie values, melanoma incidences, null hypothesis states Key Phrases - Capitalized Phrases (CAPs): (learn more)
United States, New York, New England, Quantitative Applications, Sage University Paper Series, High Street, Beverly Hills, Description of Data, Pacific Coast, Bureau of the Census, East Coast, Swarthmore College, The Free Press, World War, North Dakota, African Americans, Los Angeles, Newbury Park, American Sociological Review, Census Bureau, New Jersey, Springer Web, Alf Landon, Englewood Cliffs, Health Statistics New!
Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 26
books:
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- The Social Sciences by William H. Kruskal on page 59, page 60, and page 332
- Applied Statistics: Analysis of Variance and Regression (Probability & Mathematical Statistics) by Olive Jean Dunn on page 390
- The Psychology of Women by Margaret Martin on page 118
- Central Tendency and Variability (Quantitative Applications in the Social Sciences) by Herbert F. Weisberg on page 158
- Multiple Regression in Practice (Quantitative Applications in the Social Sciences) by William D. Berry on page 595
See all 26 books this book cites
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