Statistics, Updated Edition, 13th edition
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Overview
Statistics, 13th Edition is a contemporary classic, a trusted and comprehensive introduction to statistics that emphasizes inference and integrates real data throughout. McClave and Sincich emphasize the development of statistical thinking, the assessment of credibility, and value of the inferences made from data. They provide ample support when you are learning to solve problems and when you are studying and reviewing the material. Case studies, applications and biographies keep you motivated and demonstrate the relevance of statistics. Ideal for 1 or 2semester courses, Statistics assumes a mathematical background of basic algebra. For more advanced courses, it offers optional footnotes about calculus and the underlying theory. The print book has been reprinted with new and updated statistical software screenshots.
Published by Pearson (July 15th 2020)  Copyright © 2021
ISBN13: 9780136881285
Subject: Introductory Statistics
Category: Statistics, Updated Edition
Overview
1. Statistics, Data, and Statistical Thinking
 1.1 The Science of Statistics
 1.2 Types of Statistical Applications
 1.3 Fundamental Elements of Statistics
 1.4 Types of Data
 1.5 Collecting Data: Sampling and Related Issues
 1.6 The Role of Statistics in Critical Thinking and Ethics
2. Methods for Describing Sets of Data
 2.1 Describing Qualitative Data
 2.2 Graphical Methods for Describing Quantitative Data
 2.3 Numerical Measures of Central Tendency
 2.4 Numerical Measures of Variability
 2.5 Using the Mean and Standard Deviation to Describe Data
 2.6 Numerical Measures of Relative Standing
 2.7 Methods for Detecting Outliers: Box Plots and zScores
 2.8 Graphing Bivariate Relationships (Optional)
 2.9 Distorting the Truth with Descriptive Statistics
3. Probability
 3.1 Events, Sample Spaces, and Probability
 3.2 Unions and Intersections
 3.3 Complementary Events
 3.4 The Additive Rule and Mutually Exclusive Events
 3.5 Conditional Probability
 3.6 The Multiplicative Rule and Independent Events
 3.7 Some Additional Counting Rules (Optional)
 3.8 Bayes's Rule (Optional)
4. Discrete Random Variables
 4.1 Two Types of Random Variables
 4.2 Probability Distributions for Discrete Random Variables
 4.3 Expected Values of Discrete Random Variables
 4.4 The Binomial Random Variable
 4.5 The Poisson Random Variable (Optional)
 4.6 The Hypergeometric Random Variable (Optional)
5. Continuous Random Variables
 5.1 Continuous Probability Distributions
 5.2 The Uniform Distribution
 5.3 The Normal Distribution
 5.4 Descriptive Methods for Assessing Normality
 5.5 Approximating a Binomial Distribution with a Normal Distribution (Optional)
 5.6 The Exponential Distribution (Optional)
6. Sampling Distributions
 6.1 The Concept of a Sampling Distribution
 6.2 Properties of Sampling Distributions: Unbiasedness and Minimum Variance
 6.3 The Sampling Distribution of (xbar) and the Central Limit Theorem
 6.4 The Sampling Distribution of the Sample Proportion
7. Inferences Based on a Single Sample: Estimation with Confidence Intervals
 7.1 Identifying and Estimating the Target Parameter
 7.2 Confidence Interval for a Population Mean: Normal (z) Statistic
 7.3 Confidence Interval for a Population Mean: Student's tStatistic
 7.4 LargeSample Confidence Interval for a Population Proportion
 7.5 Determining the Sample Size
 7.6 Confidence Interval for a Population Variance (Optional)
8. Inferences Based on a Single Sample: Tests of Hypothesis
 8.1 The Elements of a Test of Hypothesis
 8.2 Formulating Hypotheses and Setting Up the Rejection Region
 8.3 Observed Significance Levels: pValues
 8.4 Test of Hypothesis about a Population Mean: Normal (z) Statistic
 8.5 Test of Hypothesis about a Population Mean: Student's tStatistic
 8.6 LargeSample Test of Hypothesis about a Population Proportion
 8.7 Calculating Type II Error Probabilities: More about β (Optional)
 8.8 Test of Hypothesis about a Population Variance (Optional)
9. Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses
 9.1 Identifying the Target Parameter
 9.2 Comparing Two Population Means: Independent Sampling
 9.3 Comparing Two Population Means: Paired Difference Experiments
 9.4 Comparing Two Population Proportions: Independent Sampling
 9.5 Determining the Sample Size
 9.6 Comparing Two Population Variances: Independent Sampling (Optional)
10. Analysis of Variance: Comparing More than Two Means
 10.1 Elements of a Designed Study
 10.2 The Completely Randomized Design: Single Factor
 10.3 Multiple Comparisons of Means
 10.4 The Randomized Block Design
 10.5 Factorial Experiments: Two Factors
11. Simple Linear Regression
 11.1 Probabilistic Models
 11.2 Fitting the Model: The Least Squares Approach
 11.3 Model Assumptions
 11.4 Assessing the Utility of the Model: Making Inferences about the Slope β1
 11.5 The Coefficients of Correlation and Determination
 11.6 Using the Model for Estimation and Prediction
 11.7 A Complete Example
12. Multiple Regression and Model Building
 12.1 MultipleRegression Models
 PART I: FirstOrder Models with Quantitative Independent Variables
 12.2 Estimating and Making Inferences about the β Parameters
 12.3 Evaluating Overall Model Utility
 12.4 Using the Model for Estimation and Prediction
 PART II: Model Building in Multiple Regression
 12.5 Interaction Models
 12.6 Quadratic and Other Higher Order Models
 12.7 Qualitative (Dummy) Variable Models
 12.8 Models with Both Quantitative and Qualitative Variables (Optional)
 12.9 Comparing Nested Models (Optional)
 12.10 Stepwise Regression (Optional)
 PART III: Multiple Regression Diagnostics
 12.11 Residual Analysis: Checking the Regression Assumptions
 12.12 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation
13. Categorical Data Analysis
 13.1 Categorical Data and the Multinomial Experiment
 13.2 Testing Categorical Probabilities: OneWay Table
 13.3 Testing Categorical Probabilities: TwoWay (Contingency) Table
 13.4 A Word of Caution about ChiSquare Tests
14. Nonparametric Statistics (available online)
 14.1 Introduction: DistributionFree Tests
 14.2 SinglePopulation Inferences
 14.3 Comparing Two Populations: Independent Samples
 14.4 Comparing Two Populations: Paired Difference Experiment
 14.5 Comparing Three or More Populations: Completely Randomized Design
 14.6 Comparing Three or More Populations: Randomized Block Design
 14.7 Rank Correlation
APPENDICES
A: Summation Notation
B: Tables
C: Calculation Formulas for Analysis of Variance
Short Answers to Selected OddNumbered Exercises
Index
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