Barrons - AP Statistics with 6 Practice Tests

Martin Sternstein, Ph.D.
Professor Emeritus
Department of Mathematics
Ithaca College
Ithaca, New York

AP and Advanced Placement Program are registered trademarks of the College Board, which was not involved in the production of, and does not endorse, this product.

Dr. Martin Sternstein, Professor Emeritus at Ithaca College, was honored by Princeton Review as one of the nations 300 Best College Professors. He is a long-time College Board consultant and has been a Reader and Table Leader for the AP Statistics exam for many years. He has strong interests in national educational and social issues concerning equal access to math education for all. For two years, he was a Fulbright Professor in Liberia, West Africa, after which he developed a popular Math in Africa course, and he is the only mathematician to have given a presentation at the annual Conference on African Linguistics. He also taught the first U.S. course for college credit in chess theory.

Thanks to Annie Bernberg, editor at Kaplan, for her guidance. Thanks to my brother, Allan, my sister-in-law, Marilyn, my sons, Jonathan and Jeremy, my daughters-in-law, Asia and Cheryl, and my grandchildren, Jaiden, Jordan, Josiah, Luna, and Jayme, for their heartfelt love and support. Most of all, thanks are due to my wife, Faith, whose love, warm encouragement, and always calm and optimistic perspective on life provide a home environment in which deadlines can be met and goals easily achieved. My sincere appreciation goes to the participants who have attended my AP Statistics workshops for teaching me just as much as I have taught them, and special thanks for their many useful suggestions are due to the following exceptional teachers:

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Copyright 2004, 2000, 1998 by Kaplan, Inc., d/b/a Barrons Educational Series, under the title How to Prepare for the AP Advanced Placement Exam in Statistics.

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ISBN: 978-1-5062-6355-7

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As you review the content in this book and work toward earning that on your AP STATISTICS exam, here are five things that you MUST know:

1. Graders want to give you credithelp them! Make them understand what you are doing, why you are doing it, and how you are doing it. Dont make the reader guess at what you are doing.
   • Communication is just as important as statistical knowledge!
   • Be sure you understand exactly what you are being asked to do or find or explain and approach each problem systematically.
   • Some problems look scary on first reading but are not overly difficult and are surprisingly straightforward. Questions that take you beyond the scope of the AP curriculum will be phrased in ways that you should be able to answer them based on what you have learned in your AP Statistics class.
     
   • Naked or bald answers will receive little or no credit. You must show where answers come from.
   • On the other hand, dont give more than one solution to the same problemyou will receive credit only for the weaker one.
2. Random sampling and random assignment are different ideas.
   • Random sampling is the use of chance in selecting a sample from a population and is critical in being able to generalize from a sample to a population.
     • A simple random sample (SRS) is when every possible sample of a given size has the same chance of being selected.
     • A stratified random sample is when the population is divided into homogeneous units called strata, and random samples are chosen from each strata.
     • A cluster sample is when the population is divided into heterogeneous units called clusters, and a random sample of the clusters is chosen.
     • A systematic sample is when a random starting point is followed by choosing every kth member of a list.
   • Random assignment in experiments is when subjects are randomly assigned to treatments and is critical in minimizing the effect of possible confounding variables.
     • This randomization evens out effects over which you have no control and allows for a valid comparison of treatments.
     • Randomized block design refers to when the randomization occurs only within groups of similar experimental units called blocks.
3. Distributions describe variability, and variability is the most fundamental concept in statistics. Understand the difference between:
   • population distribution (variability in an entire population),
   • sample distribution (variability within a particular sample), and
   • sampling distribution (variability between samples).
   • The larger the sample size, the more the sample distribution looks like the population distribution.
   • Central Limit Theorem: the larger the sample size, the more the sampling distribution (probability distribution of the sample means) looks like a normal distribution.
4. Choosing the correct procedure and performing proper checks are critical.
   • Categorical variables lead to proportions or chi-square procedures, while quantitative variables lead to means or linear regression.
     
   • Decide whether there is a single population of interest or two populations being compared.