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Statistics: Principles and Methods, 6th Edition,9780470409275
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Statistics: Principles and Methods, 6th Edition

by
Edition:
6th
ISBN13:

9780470409275

ISBN10:
0470409274
Format:
Hardcover
Pub. Date:
12/1/2009
Publisher(s):
Wiley
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Questions About This Book?

What version or edition is this?
This is the 6th edition with a publication date of 12/1/2009.
What is included with this book?
  • The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any CDs, lab manuals, study guides, etc.

Summary

Johnson provides a comprehensive, accurate introduction to statistics for business professionals who need to learn how to apply key concepts. The chapters have been updated with real-world data to make the material more relevant. The revised pedagogy will help them contextualize statistical concepts and procedures. The numerous examples clearly demonstrate the important points of the methods. New What Will We Learn opening paragraphs set the stage for the material being discussed. Using Statistics Wisely boxes summarize key lessons. In addition, Statistics in Context sections give business professionals an understanding of applications in which a statistical approach to variation is needed.

Table of Contents

Introduction
What is Statistics?
Statistics in Our Everyday Life
Statistics in Aid of Scientific Inquiry
Two Basic Concepts- Population and Sample
The Purposeful Collection of Data
Statistics in Context
Objectives of Statistics
Organization and Description of Data
Introduction
Main Types of Data
Describing Data by Tables and Graphs
Measures of Center
Measures of Variation
Checking the Stability of the Observations over Time
More on Graphics
Statistics in Context
Descriptive Study of Bivariate Data
Introduction
Summarization of Bivariate Categorical Data
A Designed Experiment for Making a Comparison
Scatter Diagram of Bivariate Measurement Data
The Correlation Coefficient- A Measure of Linear Relation
Prediction of One Variable from Another (Linear Regression)
Probability
Introduction
Probability of an Event
Methods of Assigning Probability
Event Relations and Two Laws of Probability
Conditional Probability and Independence
Bayes' Theorem
Random Sampling from a Finite Population
Probability Distributions
Introduction
Random Variables
Probability Distribution of a Discrete Random Variable
Expectation (Mean) and Standard Deviation of a Probability Distribution
Success and Failures- Bernoulli Trials
The Binomal Distribution
The Binomal Distribution in Context
The Normal Distribution
Probability Model for a Continuous Random Variable
The Normal Distribution-Its General Features
The Standard Normal Distribution
Probability Calculations with Normal Distributions
The Normal Approximation to the Binomial
Checking the Plausibility of a Normal Model
Transforming Observations to Attain Near Normality
Variation in Repeated Samples-Sampling Distribution
Introduction
The Sampling Distribution of a Statistic
Distribution of the Sample Mean and the Central Limit Theorem
Statistics in Context
Drawing Inferences From Large Samples
Introduction
Point Estimation of Population Mean
Confidence Interval for a Population Mean
Testing Hypotheses about a Population Mean
Inferences about a Population Proportion
Small-Sample Inferences for Normal Populations
Introduction
Student's t Distribution
Inferences about -Small Sample Size
Relationship between Tests and Confidence Intervals
Inferences About the Standard Deviation (The Chi-Square Distribution)
Robustness of Inference Procedures
Comparing Two Treatments
Introduction
Independent Random Samples from Two Populations
Large Samples Inference about Difference of Two Means
Inferences from Small Samples: Normal Populations with Equal Variances
Inferences from Small Samples: Normal Populations but Unequal Variances
Randomization and its Role in Inference
Matched Pairs Comparisons
Choosing Between Independent Samples and a Matched Pairs Sample
Comparing Two Population Proportions
Regression Analysis I (Simple Linear Regression)
Introduction
Regression with a Single Predictor
A Straight-Line Regression Model
The Method of Least Squares
The Sampling Variability of the Least Squares Estimators-Tools for Inference
Important Inference Problems
The Strength of a Linear Relation
Remarks About the Straight Line Model Assumption
Regression Analysis- II Multiple Linear Regression and Other Topics
Introduction
Nonlinear Relations and Linearizing Transformations
Multiple Linear Regression
Residual Plots to Check the Adequacy of a Statistical Model
Review Exercises
Analysis of Categorical Data
Introduction
Pearson's x^2 Test for Goodness of Fit
Contingency Table with One Margin Fixed (Test of Homogeneity)
Contingency Table with Neither Margin Fixed (Test of Independence)
Review Exercises
Analysis of Variance (ANOVA)
Introduction
Comparison of Several Treatments- The Completely Randomized Design
Population Model and Inferences for a Completely Randomized Design
Simultaneous Confidence Intervals
Graphical Diagnostics and Displays to Supplement ANOVA
Randomized Block Experiments for Comparing k Treatments
Review Exercises
Nonparametric Inference
Introduction
The Wilcoxon Rank-Sum Test for Comparing Two Treatments
Matched Pair Comparisons
Measure of Correlation Based on Ranks
Concluding Remarks
Using Statistics Wisely
Key Ideas and Formulas
Technology
Review Exercises
Summation Notation
Rules for Counting
Expectation and Standard Deviation-Properties
The Expected Value and Standard Deviation of X
Tables
Table of Contents provided by Publisher. All Rights Reserved.


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