Statistics Super Review (Super Reviews Study Guides) - Softcover

Editors Of REA

Softcover

ISBN 10: 0738611247 ISBN 13: 9780738611242

Publisher: Research & Education Association, 2013

Synopsis

Need help with Statistics? Want a quick review or refresher for class? This is the book for you!

REA’s Statistics Super Review gives you everything you need to know!

This Super Review can be used as a supplement to your high school or college textbook, or as a handy guide for anyone who needs a fast review of the subject.

· Comprehensive, yet concise coverage – review covers the material that students must know about statistics. Each topic is presented in a clear and easy-to-understand format that makes learning easier.

· Questions and answers for each topic – let you practice what you’ve learned and build your statistics skills.

· End-of-chapter quizzes – gauge your understanding of the important information you need to know, so you’ll be ready for any homework assignment, quiz, or test.

Whether you need a quick refresher on the subject, or are prepping for your next exam, we think you’ll agree that REA’s Super Review provides all you need to know!

About this title

From the Back Cover:

STATISTICS SUPER REVIEW
Need help with statistics? Want a quick review or refresher for class? This is the book for you!

CONCISE SUBJECT REVIEW
Covers the material students typically learn in an introductory statistics course. Clear, easy-to-understand format makes learning easier.

PACKED WITH PRACTICE
Topic-level questions with detailed explanations let you practice what you’ve learned and build your statistics skills.

TEST WHAT YOU’VE LEARNED
End-of-chapter quizzes reinforce key concepts, so you’ll be ready for any statistics problem you encounter on your next quiz or test.

Excerpt. © Reprinted by permission. All rights reserved.:

1.1 What is Statistics?

Statistics is the science of assembling, organizing, and analyzing data, as well as drawing conclusions about what the data means. Often, these conclusions come in the form of predictions. Hence, the importance of statistics.

The first task of a statistician conducting a study is to define the population and to choose a sample. In most cases, it is impractical or impossible to examine the entire group, which is called the population or universe. This being the case, one must instead examine a part of the population called a sample. The chosen sample has to reflect as closely as possible the characteristics of the population. A population can be finite or infinite.

For example, statistical methods were used in testing the Salk vaccine, which protects children against polio. The sample consisted of 400,000 children. Half of the children, chosen at random, received the Salk vaccine; the other half received an inactive solution. After the study was completed, it turned out that 50 cases of polio appeared in the group that received the vaccine, and 150 cases appeared in the group that did not receive the vaccine. Based on that study of a sample of 400,000 children, it was decided that the Salk vaccine was effective for the entire population.

EXAMPLES:

The population of all television sets in the United States is finite. The population consisting of all possible outcomes in successive tosses of a coin is infinite. Descriptive or deductive statistics collects data concerning a given group and analyzes it without drawing conclusions. From an analysis of a sample, inductive statistics or statistical inference draws conclusions about the population.

Problem Solving Example:
Suppose that a sociologist desires to study the religious habits of 20-year-old males in the United States. He draws a sample from the 20-year-old males of a large city to make his study. Describe the sample and target populations. What problems arise in drawing conclusions from this data?

The sample population consists of the 20-year-old males in the city that the sociologist samples. The target population consists of the 20-year-old males in the United States. In drawing conclusions about the target population, the researcher must be careful. His data may not reflect religious habits of 20-year-old males in the United States but rather the religious habits of 20-year-old males in the city that was surveyed. The reliability of the extrapolation cannot be measured in probabilistic terms.

1.2 Variables

Variables will be denoted by symbols, such as x, y, z, t, a, M, etc. A variable can assume any of its prescribed values. A set of all the possible values of a variable is called its domain.

EXAMPLE:

For a toss of a die, the domain is {1, 2, 3, 4, 5, 6}. The variable can be discrete or continuous.

EXAMPLE:

The income of an individual is a discrete variable. It can be $1,000 or $1,000.01, but it cannot be between these two numbers.

EXAMPLE:

The height of a person can be 70 inches or 70.1 inches. It can also assume any value between these two numbers. Hence, height is a continuous variable. Similarly, data can be discrete or continuous. Usually, countings and enumerations yield discrete data, while measurements yield continuous data.

About this edition

• Publisher: Research & Education Association
• Publication date: 2013
• ISBN 10: 0738611247
• ISBN 13: 9780738611242
• Binding: Paperback
• Edition number: 2
• Number of pages: 320