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Author:   Ive Barreiros
Publisher:   Kendall/Hunt Publishing Co ,U.S.
Imprint:   Kendall/Hunt Publishing Co ,U.S.
ISBN:  

9781524990596

ISBN 10:   1524990590
Pages:   277
Publication Date:   07 January 2020
Audience:   College/higher education ,  Tertiary & Higher Education
Format:   Spiral bound
Publisher's Status:   Active
Availability:   Temporarily unavailable  
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Table of Contents

Preface Chapter 1 Introduction 1.1 The Nature of Statistics 1.2 How Statistics Works 1.3 Mathematics and Statistics 1.3.1 American Community Service (ACS) 1.3.2 Current Population Survey 1.3.3 On the Benefit Effects of Taking Aspirin Chapter 2 The Data 2.1 Variables 2.2 Variables and Measurement Levels 2.2.1 Examples of Data Sets of Several Variables 2.2.2 Another Example of a Multivariable Data Set 2.3 Selecting a Sample for an Observational Study 2.3.1 Simple Random Sampling 2.3.2 Selecting a Simple Random Variable 2.4 Other Sampling Methods 2.5 Observational Studies and Designed Experiments 2.5.1 Examples of Information Obtained by Observational Studies and from Experiments 2.6 The Importance of the Definition of the Variables 2.6.1 More Examples and Problems Chapter 3 Description of Data 3.1 Frequency: Frequency Tables and Graphics 3.2 Graphic Representation of Quantitative Data: The Histogram 3.2.1 Example of the Construction of a Histogram 3.3 Descriptive Measures of Quantitative Data 3.4 Notation 3.5 Definitions and Examples 3.6 Descriptive Measures of Variability 3.7 Specific Formulas in the Case of Data Given in a Frequency Table 3.8 Descriptive Measures of Relative Standing 3.9 Summary of Formulas for This Chapter: Examples Chapter 4 The Role of the Standard Deviation in the Description of Quantitative Data 4.1 Chebyshev's Inequality 4.2 The Particular Quantitative Data We Call Normal 4.3 Skewness of Data 4.4 Parameters and Statistics 4.5 Examples Chapter 5 Elements of Probability 5.1 Randomness and Probability 5.2 Probability Space 5.3 Operation between Events to Obtain New Events 5.4 Probability Formulas 5.4.1 Additive Rule 5.4.2 Multiplicative Rule and Conditional Probability 5.4.3 Examples 5.5 Odds and Probability 5.6 More Examples 5.7 Probability Tree 5.8 Summary of Probability Formulas 5.9 Counting Ways by Counting Formulas 5.9.1 Multiplicative Rule for Counting Formulas 5.9.2 More Examples of Counting Ways Chapter 6 Discrete Random Variable 6.1 Formal Definition of a Discrete Random Variable 6.2 Expected Value of a Discrete Random Variable 6.3 Variance and Standard Deviation of a Discrete Random Variable 6.4 Variance and the Notion of Uncertainty 6.5 Binomial Experiment and Binomial Variable 6.5.1 Binomial Probability Formula 6.5.2 Mean and Variance of a Binomial Random Variable 6.5.3 Skewness of a Binomial Probability Distribution 6.5.4 Examples of Applications of Binomial Distribution Chapter 7 Continuous Random Variables 7.1 Continuous Uniform Density 7.2 Standard Normal Probability Density and z Standard Normal 7.3 Standard Normal Table 7.4 General Normal Probability Density 7.5 Conversion Formula for a General Normal Variable x 7.6 Critical Values and Significance Level 7.7 Approximating a Binomial Probabilities by a Normal Density 7.7.1 Other Examples of the Approximation of a Binomial Probability by a Normal Distribution Chapter 8 The Central Limit Theorem (CLT) and Applications 8.1 Role of the CLT in the Way Statistics Works 8.2 Sample Size and Sampling Error 8.3 Two Probability Distributions Derived from Normal Distribution 8.3.1 The t-random Variable 8.3.2 Examples of Reading the t-table 8.4 The Chi-Square Random Variable 8.4.1 Properties of Chi-Square Distribution Chapter 9 Estimation 9.1 Introduction 9.2 Estimation of a Population Mean 9.3 The t-interval Procedure for the Estimation of a Population Mean When the Sigma is not Known 9.4 The z-interval Procedure for a Confidence Interval Estimation of a Population Proportion p 9.5 Minimum Sample Size for a Given Margin of Error E and a Given Level of Confidence 9.6 A Confidence Interval Estimate of a Population Variance: The Chi-Square Interval Procedure Chapter 10 Test of a Hypothesis 10.1 Decision Making: Rejecting the Null Hypothesis or Failing to Reject It 10.2 Hypothesis Testing: Steps and Reasoning 10.3 Risk of Error and Important Symbols 10.3.1 Direction of Tests 10.4 Test Statistic: Formulas and Assumptions 10.4.1 Test Statistic for Hypothesis on the Population Mean 10.4.2 Test Statistic for Hypothesis on the Population Proportion 10.4.3 Test Statistic for Hypothesis on the Population Variance 10.5 Test of Hypothesis - Main Elements and Computations 10.5.1 Critical Values for the z-Distribution and Their Significance Levels 10.6 Steps in a Hypothesis Testing: The Use of P-value 10.7 More Examples 10.8 Concepts and More Examples about P-value Chapter 11 Comparing Two Populations: Independent and Dependent Samples 11.1 Inferences for the Difference between Two Means: Independent Large Samples 11.2 Pooled Variance 11.3 Small Samples from Two Normal Populations: Assumptions and Formulas 11.4 Inferences for the Difference between Two Population Proportions 11.5 Inferences on the Differences of Means: Dependent Samples or Matched Pairs Design Chapter 12 A Model in Science: The Simple Linear Model 12.1 Simple Linear Model 12.2 Estimation of the Parameters in the Simple Linear Model 12.3 Formulas to Estimate the Population Parameters 12.4 Scatter Plot: A Descriptive Statistical Tool 12.4.1 Inferential Interpretation of r and r2 12.4.2 More Inferences in the Simple Linear Model 12.4.3 Making Inference about the Slope b? 12.4.4 Applications of the Simple Linear Model: Estimation of the Mean of y for a Given x 12.5 An Excel Feature to Fit the Simple Linear Model 12.6 A Last Word about Modeling and How Statistics Works Appendix A.1 Recommended Readings A.2 Current Developments A.2.1 Nonparametric Statistics A.2.2 Bayesian Statistics A.2.3 New Developments in Meta-Analysis A.3 Formulas A.3.1 Probability Formulas A.3.2 Interval Estimation Formulas-One Sample A.3.3 Interval Estimation Formulas-Two Samples A.3.4 Test of Hypothesis Formulas-One Sample A.3.5 Test of Hypothesis Formulas-Two Samples A.3.6 Simple Linear Model Formulas A.4 Sample Tests

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