Schaum's Outline of Elements of Statistics I: Descriptive Statistics and Probability
by Bernstein, Stephen; Bernstein, RuthEdition: 1st
Format: Paperback
Pub. Date: 1999-01-21
Publisher(s): McGraw-Hill Education
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Summary
Specially designed for nonmathematics majors, this user-friendly tool for learning statistics begins by reviewing all the math needed. Providing a complete introduction to statistics for students in a variety of fields, this guide reinforces learning with fully-solved problems that arise in such areas as anthropology, biology, business, government, medicine, psychology and sociology. The clear, step-by-step presentation and question-and-answer format make learning easier, important points memorable and higher grades well within reach.
Table of Contents
Mathematics Required for Statistics. Characteristics of the Data. Populations, Samples, and Statistics. Descriptive Statistics: Organizing the Data Into Tables. Descriptive Statistics: Graphing the Data. Descriptive Statistics: Measures of Central Tendency, Average Value, and Location. Descriptive Statistics: Measures of Dispersion. Probability: The Classical, Relative Frequency, Set Theory, and Subjective Interpretations. Probability: Rules for Multiplication and Division, Marginal Probabilities and Bayes' Theorem, Tree Diagrams and Counting Rules. Random Variables, Probability Distributions, Cumulative Distribution Functions, and Expected Values.
Populations, Samples, and Statistics. Descriptive Statistics: Organizing the Data Into Tables. Descriptive Statistics: Graphing the Data. Descriptive Statistics: Measures of Central Tendency, Average Value, and Location. Descriptive Statistics: Measures of Dispersion. Probability: The Classical, Relative Frequency, Set Theory, and Subjective Interpretations. Probability: Rules for Multiplication and Division, Marginal Probabilities and Bayes' Theorem, Tree Diagrams and Counting Rules. Random Variables, Probability Distributions, Cumulative Distribution Functions, and Expected Values.
Descriptive Statistics: Graphing the Data. Descriptive Statistics: Measures of Central Tendency, Average Value, and Location. Descriptive Statistics: Measures of Dispersion. Probability: The Classical, Relative Frequency, Set Theory, and Subjective Interpretations. Probability: Rules for Multiplication and Division, Marginal Probabilities and Bayes' Theorem, Tree Diagrams and Counting Rules. Random Variables, Probability Distributions, Cumulative Distribution Functions, and Expected Values.
Descriptive Statistics: Measures of Dispersion. Probability: The Classical, Relative Frequency, Set Theory, and Subjective Interpretations. Probability: Rules for Multiplication and Division, Marginal Probabilities and Bayes' Theorem, Tree Diagrams and Counting Rules. Random Variables, Probability Distributions, Cumulative Distribution Functions, and Expected Values.
Probability: Rules for Multiplication and Division, Marginal Probabilities and Bayes' Theorem, Tree Diagrams and Counting Rules. Random Variables, Probability Distributions, Cumulative Distribution Functions, and Expected Values.
Distribution Functions, and Expected Values.
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