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Statistics for Business and Economics

ISBN-10: 032182623X

ISBN-13: 9780321826237

Edition: 12th 2014

Authors: P. George Benson, Terry Sincich, James T. McClave

List price: $254.40
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Statistics for Business and Economics, Twelfth Edition, meets today's business students with a balance of clarity and rigor, and applications incorporated from a diverse range of industries. This classic text covers a wide variety of data collection and analysis techniques with these goals in mind: developing statistical thinking, learning to assess the credibility and value of inferences made from data, and making informed business decisions. TheTwelfth Editionhas been updated withreal, current datain many of the exercises, examples, and applications. Exercises draw on actual business situations and recent economic events so that students can test their knowledge throughout the course.Statistics in Actioncase studies open each chapter with a recent, controversial, or high-profile business issue, motivating students to critically evaluate the findings and think through the statistical issues involved. A continued emphasis onethicshighlights the importance of ethical behavior in collecting, interpreting, and reporting on data.
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Book details

List price: $254.40
Edition: 12th
Copyright year: 2014
Publisher: Pearson Education
Publication date: 12/21/2012
Binding: Hardcover
Pages: 864
Size: 8.75" wide x 11.00" long x 1.50" tall
Weight: 4.4
Language: English

James T. McClave, Info Tech, Inc./ University of Florida P. Goerge Benson, Terry College of Business, University of Georgia Terry Sincich, University of South Florida

Statistics, Data, and Statistical Thinking
The Science of Statistics
Types of Statistical Applications in Business
Fundamental Elements of Statistics
Processes (Optional)
Types of Data
Collecting Data: Sampling and Related Issues
Critical Thinking with Statistics
Statistics in Action: A 20/20 View of Surveys: Fact or Fiction?
Keep the Change: Collecting Data
Identifying Misleading Statistics
Using Technology: Accessing and Listing Data; Random Sampling
Methods for Describing Sets of Data
Describing Qualitative Data
Graphical Methods for Describing Quantitative Data
Numerical Measures of Central Tendency
Numerical Measures of Variability
Using the Mean and Standard Deviation to Describe Data
Numerical Measures of Relative Standing
Methods for Detecting Outliers: Box Plots and z-Scores
Graphing Bivariate Relationships (Optional)
The Time Series Plot (Optional)
Distorting the Truth with Descriptive Techniques
Statistics in Action: Can Money Buy Love?
Real Estate Sales
Keep the Change: Measures of Central Tendency and Variability
Using Technology: Describing Data
Making Business Decisions: The Kentucky Milk Case Part 1 (Covers Chapters 1 and 2)
Events, Sample Spaces, and Probability
Unions and Intersections
Complementary Events
The Additive Rule and Mutually Exclusive Events
Conditional Probability
The Multiplicative Rule and Independent Events
Bayes's Rule
Statistics in Action: Lotto Buster!
Exit Polls: Conditional Probability
Keep the Change: Independent Events
Using Technology: Combinations and Permutations
Random Variables and Probability Distributions
Two Types of Random Variables
Discrete Random Variables
Probability Distributions for Discrete Random Variables
The Binomial Distribution
Other Discrete Distributions: Poisson and Hypergeometric
Continuous Random Variables
Probability Distributions for Continuous Random Variables
The Normal Distribution
Descriptive Methods for Assessing Normality
Other Continuous Distributions: Uniform and Exponential
Statistics in Action: Probability in a Reverse Cocaine Sting: Was Cocaine Really Sold?
Warehouse Club Memberships: Exploring a Binomial Random Variable
Identifying the Type of Probability Distribution
Using Technology: Discrete Probabilities, Continuous Probabilities, and Normal Probability Plots
Sampling Distributions
The Concept of a Sampling Distribution
Properties of Sampling Distributions: Unbiasedness and Minimum Variance
The Sampling Distribution of the Sample Mean and the Central Limit Theorem
The Sampling Distribution of the Sample Proportion
Statistics in Action: The Insomnia Pill: Is It Effective?
Simulating a Sampling Distribution Cell Phone Usage
Using Technology: Simulating a Sampling Distribution
Making Business Decisions: The Furniture Fire Case (Covers Chapters 3-5)
Inferences Based on a Single Sample: Estimation with Confidence Intervals
Identifying and Estimating the Target Parameter
Confidence Interval for a Population Mean: Normal (z) Statistic
Confidence Interval for a Population Mean: Student's t-Statistic
Large-Sample Confidence Interval for a Population Proportion
Determining the Sample Size
Finite Population Correction for Simple Random Sampling (Optional)
Confidence Interval for a Population Variance (Optional)
Inferences Based on a Single Sample: Estimation with Confidence Intervals
Statistics in Action: Medicare Fraud Investigations
Conducting a Pilot Study
Using Technology: Confidence Intervals
Inferences Based on a Single Sample: Tests of Hypotheses
The Elements of a Test of Hypothesis
Formulating Hypotheses and Setting Up the Rejection Region
Observed Significance Levels: p-Values
Test of Hypothesis about a Population Mean: Normal (z) Statistic
Test of Hypothesis about a Population Mean: Student's t-Statistic
Large-Sample Test of Hypothesis about a Population Proportion
Test of Hypothesis about a Population Variance
Calculating Type II Error Probabilities: More about b (Optional)
Statistics in Action: Diary of a Kleenex� User-How Many Tissues in a Box?
Challenging a Company's Claim: Tests of Hypotheses
Keep the Change: Tests of Hypotheses
Using Technology: Tests of Hypotheses
Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses
Identifying the Target Parameter
Comparing Two Population Means: Independent Sampling
Comparing Two Population Means: Paired Difference Experiments
Comparing Two Population Proportions: Independent Sampling
Determining the Required Sample Size
Comparing Two Population Variances: Independent Sampling
Statistics in Action: ZixIt Corp. v. Visa USA Inc.-A Libel Case
Box Office Receipts: Comparing Population Means
Keep the Change: Inferences Based on Two Samples
Using Technology: Two-Sample Inferences
Making Business Decisions: The Kentucky Milk Case-Part II (Covers Chapters 6-8)
Design of Experiments and Analysis of Variance
Elements of a Designed Experiment
The Completely Randomized Design: Single Factor
Multiple Comparisons of Means
The Randomized Block Design
Factorial Experiments: Two Factors
Statistics in Action: Pollutants at a Housing Development-A Case of Mishandling Small Samples
Designed vs. Observational Experiments
Using Technology: Analysis of Variance
Categorical Data Analysis
Categorical Data and the Multinomial Experiment
Testing Category Probabilities: One-Way Table
Testing Category Probabilities: Two-Way (Contingency) Table
A Word of Caution about Chi-Square Tests
Statistics in Action: The Case of the Ghoulish Transplant Tissue-Who Is Responsible for Paying Damages?
Binomial vs. Multinomial Experiments
Contingency Tables
Using Technology: Chi-Square Analyses
Making Business Decisions: Discrimination in the Workplace (Covers Chapters 9 and 10)
Simple Linear Regression
Probabilistic Models
Fitting the Model: The Least Squares Approach
Model Assumptions
Assessing the Utility of the Model: Making Inferences about the Slope b <Sub>1</Sub>
The Coefficients of Correlation and Determination
Using the Model for Estimation and Prediction
A Complete Example
Statistics in Action: Legal Advertising-Does It Pay?
Apply Simple Linear Regression to Your Favorite Data
Using Technology: Simple Linear Regression
Multiple Regression and Model Building
Multiple Regression Models
First-Order Models with Quantitative Independent Variables
Estimating and Making Inferences about the b Parameters
Evaluating Overall Model Utility
Using the Model for Estimation and Prediction
Model Building in Multiple Regression
Interaction Models
Quadratic and Other Higher-Order Models
Qualitative (Dummy) Variable Models
Models with Both Quantitative and Qualitative Variables
Comparing Nested Models
Stepwise Regression
Multiple Regression Diagnostics
Residual Analysis: Checking the Regression Assumptions
Some Pitfalls: Estimability, Multicollinearity, and Extrapolation
Statistics in Action: Bid Rigging in the Highway Construction Industry
Insurance Premiums: Collecting Data for Several Variables
Collecting Data and Fitting a Multiple Regression Model
Using Technology: Multiple Regression
Making Business Decisions: The Condo Sales Case (Covers Chapters 11 and 12)
Methods for Quality Improvement: Statistical Process Control (Available on CD)
Quality, Processes, and Systems
Statistical Control
The Logic of Control Charts
A Control Chart for Monitoring the Mean of a Process: The [x-bar]-Chart
A Control Chart for Monitoring the Variation of a Process: The R-Chart
A Control Chart for Monitoring the Proportion of Defectives Generated by a Process: The p-Chart
Diagnosing the Causes of Variation
Capability Analysis
Statistics in Action: Testing Jet Fuel Additive for Safety
Quality Control: Consistency
Using Technology: Control Charts
MAKING BUSINESS DECISIONS: The Gasket Manufacturing Case (Covers Chapter 13)
Time Series: Descriptive Analyses, Models, and Forecasting (Available on CD)
Descriptive Analysis: Index Numbers
Descriptive Analysis: Exponential Smoothing
Time Series Components
Forecasting: Exponential Smoothing
Forecasting Trends: Holt's Method
Measuring Forecast Accuracy: MAD and RMSE
Forecasting Trends: Simple Linear Regression
Seasonal Regression Models
Autocorrelation and the Durbin-Watson Test
Statistics in Action: Forecasting the Monthly Sales of a New Cold Medicine
Time Series
Using Technology: Forecasting
Nonparametric Statistics (Available on CD)
Introduction: Distribution-Free Tests
Single Population Inferences
Comparing Two Populations: Independent Samples
Comparing Two Populations: Paired Difference Experiment
Comparing Three or More Populations: Completely Randomized Design
Comparing Three or More Populations: Randomized Block Design
Rank Correlation
Statistics in Action: How Vulnerable Are New Hampshire Wells to Groundwater Contamination?
Keep the Change: Nonparametric Statistics
Using Technology: Nonparametric Tests
Making Business Decisions: Detecting "Sales Chasing" (Covers Chapters 10 and 15)
Summation Notation
Basic Counting Rules
Calculation Formulas for Analysis of Variance
Formulas for the Calculations in the Completely Randomized Design
Formulas for the Calculations in the Randomized Block Design
Formulas for the Calculations for a Two-Factor Factorial Experiment
Tukey's Multiple Comparisons Procedure (Equal Sample Sizes)
Bonferroni Multiple Comparisons Procedure (Pairwise Comparisons)
Scheff�'s Multiple Comparisons Procedure (Pairwise Comparisons)
Binomial Probabilities
Normal Curve Areas
Critical Values of t
Critical Values of x <Sup>2</Sup>
Percentage Points of the F-Distribution, � = .10
Percentage Points of the F-Distribution, � = .05
Percentage Points of the F-Distribution, � = .025
Percentage Points of the F-Distribution, � = .01
Control Chart Constants
Critical Values for the Durbin-Watson d-Statistic, � = .05
Critical Values for the Durbin-Watson d-Statistic, � = .01
Critical Values of T<Sub>L</Sub> and T<Sub>u</Sub> for the Wilcoxon Rank Sum Test: Independent Samples
Critical Values of T0 in the Wilcoxon Paired Difference Signed Rank Test
Critical Values of Spearman's Rank Correlation Coefficient
Critical Values of the Studentized Range, � = .05
Answers to Selected Exercises
Index Credits