Statistics for Management and Economics (with Online Content Printed Access Card)

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  • Edition: 9th
  • Format: Hardcover
  • Copyright: 1/1/2011
  • Publisher: South-Western College Pub
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This worldwide best-selling business statistics book teaches readers how to apply statistics to real business problems through the author's unique three-step approach to problem solving. Readers learn to IDENTIFY the right technique by focusing on the problem objective and data type. They then learn to COMPUTE the statistics either by hand, using Excel, or using MINITAB. Finally, they INTERPRET the results in the context of the problem. Keller's approach enhances user comprehension as well as practical skills.

Table of Contents

What is Statistics?
Key Statistical Concepts
Statistical Applications in Business
Statistics and the Computer
World Wide Web and Learning Center
Instructions for the CD-ROM
Graphical Descriptive Techniques I
Types of Data and Information
Describing a Set of Nominal Data
Describing the Relationship between Two Nominal Variables and Comparing Two or More Nominal Data Sets
Graphical Descriptive Techniques II
Graphical Techniques to Describe a Set of Interval Data
Describing Time-Series Data
Describing the Relationship between Two Interval Variables
Art and Science of Graphical Presentations
Numerical Descriptive Techniques
Measures of Central Location
Measures of Variability
Measures of Relative Standing and Box Plots
Measures of Linear Relationship
(Options) Applications In Professional Sports: Baseball
(Optional) Applications In Finance: Market Model
Comparing Graphical and Numerical Techniques
General Guidelines for Exploring Data
Review Of chapters 2 to 4
Data Collection and Sampling
Methods of Collecting Data
Sampling Plans
Sampling and Nonsampling Errors
Assigning Probability to Events
Joint, Marginal, and Conditional Probability
Probability Rules and Trees
Bayes' Law
Identifying the Correct Method
Random Variables and Discrete Probability Distributions
Random Variables and Probability Distributions
Bivariate Distributions
(Optional) Applications In Finance: Investment Portfolio Diversification and Asset Allocation
Binomial Distribution
Poisson Distribution
Continuous Probability Distributions
Probability Density Functions
Normal Distribution
(Optional) Exponential Distribution
Other Continuous Distributions
Sampling Distributions
Sampling Distribution of the Mean
Sampling Distribution of a Proportion
Sampling Distribution of the Difference between Two Means
From Here to Inference
Introduction to Estimation
Concepts of Estimation
Estimating the Population Mean when the Population Standard Deviation is Known
Selecting the Sample Size
Introduction to Hypothesis Testing
Concepts of Hypothesis Testing
Testing the Population Mean when the Population Standard Deviation is Known
Calculating the Probability of a Type II Error
The Road Ahead
Inference about One Population
Inference about a population Mean when the Standard Deviation is Unknown
Inference about a Population Variance
Inference about a Population Proportion
(Optional) Applications In Marketing: Market Segmentation
Inference about Two Populations
Inference about the Difference between Two Means: Independent samples
Observational and Experimental Data
Inference about the Difference between Two Means: Matched Pairs Experiment
Inference about the Ratio of Two Variances
Inference about the Difference between Two Population Proportions
Review of Chapters 12 and 13
Analysis of Variance
One Way Analysis of Variance
Multiple Comparisons
Analysis of Variance Experimental Designs
Randomized Blocks (Two Way) Analysis of Variance
Two-Factor Analysis of Variance
(Optional) Applications In Operations Management: Finding and Reducing Variation
Review of chapters 12 to 14
Chi-Squared Tests
Chi-Squared Goodness-of-Fit Test
Chi-Squared Test of a Contingency Table
Summary of Tests on Nominal Data
(Optional) Chi-Squared Test for Normality
Review of Chapters 12 to 15
Simple Linear Regression
Estimating the Coefficients
Error Variable: Required Conditions
Assessing the Model
Using the Regression Equation
Regression Diagnostics - I
Review of Chapters 12 to 16
Multiple Regression
Model and Required Conditions
Estimating the Coefficients and Assessing the Model
Regression Diagnostics - II
Regression Diagnostics- III (Time Series)
Review of Chapters 12 to 17
Model Building
Polynomial Models
Nominal Independent Variables
(Optional) Applications In Human Resources Management: Pay Equity
Logistic Regression
Stepwise Regression
Model Building
Nonparametric Statistical Techniques
Wilcoxon Rank Sum Test
Sign Test and Wilcoxon Signed Rank Sum Test
Kruskal-Wallis Test
Friedman Test
Spearman Rank Correlation
Review of Chapters 12 to 19
Time-Series Analysis and Forecasting
Time Series Components
Smoothing Techniques
Trend and Seasonal Effects
Introduction to Forecasting
Forecasting Models
Statistical Process Control. 21.1.Process Variation
Control Charts
Control Charts for Variables: and S Charts
Control Charts for Attributes: p Chart. 22:Decision Analysis
Decision Problem
Acquiring, Using and Evaluating Additional Information
Data File Sample Statistics
Binomial Probabilities
Poisson Probabilities
Normal Probabilities
Critical Values of t
Critical Values of
Critical Values of F
Critical Values of the Studentized Range
Critical Values for the Durbin-Watson Statistic
Critical Values for the Wilcoxon Rank Sum Test
Critical Values for the Wilcoxon Signed Rank Sum Test
Critical Values for the Spearman Rank Correlation Coefficient
Control Chart Constants
Answers to Selected Even-Numbered Exercises
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