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Basic Business Statistics : Concepts and Applications,9780131536869
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Basic Business Statistics : Concepts and Applications

by ; ;
Edition:
10th
ISBN13:

9780131536869

ISBN10:
0131536869
Format:
Hardcover
Pub. Date:
1/1/2005
Publisher(s):
Prentice Hall
List Price: $185.33
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Summary

For courses in Business Statistics. This comprehensive text focuses on the underlying statistical concepts that are important to students majoring in business. This Tenth edition has been substantially revised and rewritten to improve readability and comprehension, now featuring a more active, conversational writing style and a streamlined design. The authors take an applied approach and relate the concepts and applications of statistics to the functional areas of business-accounting, marketing, management, and economics and finance. This text also emphasizes the proper use of statistics to analyze data and assumes that computer software is an integral part of this analysis. Excel and Minitab, and SPSSģ are featured.

Table of Contents

Introduction and Data Collection
Using Statistics: Good Tunes
Basic Concepts of Statistics
The Growth of Statistics and Information Technology
How This Text Is Organized
The Importance of Collecting Data
Identifying Sources of Data
Types of Data
Levels of Measurement and Types of Measurement Scales
Summary
Introduction to Using Software
Presenting Data in Tables and Charts
Using Statistics: Comparing the Performance of Mutual Funds
Tables and Charts for Categorical Data
The Summary Table
The Bar Chart
The Pie Chart
The Pareto Diagram
Organizing Numerical Data
The Ordered Array
The Stem-and-Leaf Display
Tables and Charts for Numerical Data
The Frequency Distribution
The Relative Frequency Distribution and the Percentage Distribution
The Cumulative Distribution
The Histogram
The Polygon
The Cumulative Percentage Polygon (Ogive)
Cross Tabulations
The Contingency Table
The Side-by-Side Bar Chart
Scatter Diagrams and Time Series Plots
The Scatter diagram
The Time series plot
Misusing Graphs and Ethical Issues
Summary
Using Software for Tables and Charts
Numerical Descriptive Measures
Using Statistics: Comparing the Performance of Mutual Funds
Measures of Central Tendency, Variation, and Shape
The Mean
The Median
The Mode
Quartiles
The Geometric Mean
The Range
The Interquartile Range
The Variance and Standard Deviation
The Coefficient of Variation
Shape
Visual Explorations: Exploring Descriptive Statistics
Microsoft Excel Descriptive Statistics Output
Minitab Descriptive Statistics Output
Descriptive Numerical Measures for a Population
The Population Mean
The Population Variance and Standard Deviation
The Empirical Rule
The Chebychev Rule
Computing Descriptive Numerical Measures from a Frequency Distribution
Exploratory Data Analysis
The Five-Number Summary
The Box-and-Whisker Plot
The Covariance and the Coefficient of Correlation
The Covariance
The Coefficient of Correlation
Pitfalls in Numerical Descriptive Measures and Ethical Issues
Summary
Using Software for Descriptive Statistics
Basic Probability
Using Statistics: The Consumer Electronics Company
Basic Probability Concepts
Sample Spaces and Events
Contingincy Tables and Venn Diagrams
Simple (Marginal) Probability
Joint Probability
General Addition Rule
Conditional Probability
Computing Conditional Probabilities
Decision Trees
Statistical Independence
Multiplication Rule
Bayes' Theorem
Counting Rules
Ethical Issues and Probability
Summary
Using Software for Basic Probability
Some Important Discrete Probability Distributions
Using Statistics: The Accounting Information System of the Saxon Plumbing Company
The Probability Distribution for a Discrete Random Variable
Expected Value of a Discrete Random Variable
Variance and Standard Deviation of a Discrete Random Variable
Covariance and Its Application in Finance
The Covariance
The Expected Value, Variance, and Standard Deviation of the Sum of Two Random Variables
Portfolio Expected Return and Portfolio Risk
Binomial Distribution
Poisson Distribution
Hypergeometric Distribution
CD ROM Topic : Using the Poisson Distribution to Approximate the Binomial Distribution
Summary
Using Software for the Covariance and for Discrete Probability Distributions
The Normal Distribution and Other Continuous Distributions
Using Statistics: Download Time for a Web Site Home Page
Continuous Probability Distributions
The Normal Distribution
Evaluating Normality
Evaluating the Properties
Constructing the Normal Probability Plot
The Uniform Distribution
The Exponential Distribution
The Normal Approximation to the Binomial Distribution
Need for a Correction for Continuity Adjustment
Approximating the Binomial Distribution
Computing a Probability Approximation for an Individual Value
Summary
Using Software with Continuous Probability Distributions
Sampling Distributions
Using Statistics: The Oxford Cereal Company Packaging Process
Sampling Distributions
Sampling Distribution of the Mean
The Unbiased Property of the Sample Mean
Standard Error of the Mean
Sampling from Normally Distributed Populations
Sampling from Nonnormally Distributed Populations The Central Limit Theorem
Sampling Distribution of the Proportion
Types of Survey Sampling Methods
Simple Random Sample
Systematic Sample
Stratified Sample
The Cluster Sample
Evaluating Survey Worthiness
Survey Errors
Ethical Issues
CD ROM Topic Sampling from Finite Populations
Summary
Using Software for Sampling Distributions
Confidence Interval Estimation
Using Statistics: Auditing Invoices at the Saxon Home Improvement Company
Confidence Interval Estimation of the Mean (√‚ Known)
Confidence Interval Estimation of the Mean (√‚ Unknown)
Student's t Distribution
The Concept of Degrees of Freedom
The Confidence Interval Statement
Confidence Interval Estimation for the Proportion
Determining Sample Size
Sample Size Determination for the Mean
Sample Size Determination for the Proportion
Applications of Confidence Interval Estimation in Auditing
Estimating the Population Total Amount
Difference Estimation
Confidence Interval Estimation and Ethical Issues
CD ROM Topic: Estimation and Sample Size Determination for Finite Populations
Summary
Using Software for Confidence Interval Estimation
Fundamentals of Hypothesis Testing
Using Statistics: The Oxford Cereal Company Packaging Process
Hypothesis-Testing Methodology
The Null and Alternative Hypotheses
The Critical Value of the Test Statistic
Regions of Rejection and Nonrejection
Risks in Decision Making using Hypothesis Testing Methodology
Z Test of Hypothesis for the Mean (√‚ Known)
The Critical Value Approach to Hypothesis Testing
The p-Value Approach to Hypothesis Testing
A Connection between Confidence Interval Estimation and Hypothesis Testing One-Tailed Tests
One-Tail Tests
The Critical Value Approach
The p-Value Approach
t Test of Hypothesis for the Mean (√‚ Unknown)
Z Test of Hypothesis for the Proportion
The Power of a Test
Potential Hypothesis-Testing Pitfalls and Ethical Issues
Summary
Using Software for One-Sample Tests of Hypothesis
Two-Sample Tests
Comparing The Means of Two Independent Samples
Z test for the Difference between Two Means
Pooled - Variance test for the Difference between Two Means
Confidence Interval Estimate for the Difference between the Means of two Independent Groups
Separate - Variance test for the Difference between Two Means
Comparing the Means of Two Related Populations
The Paired Test
Confidence Interval Estimate for the Mean Difference
Comparing Two Population Proportions
Z Test for the Difference between Two Proportions
Confidence Interval Estimate for the Difference between Two Proportions
F Test for the Difference between Two Variances
Finding Lower-Tail Critical Values
Summary
Using Software for Two-Sample Tests of Hypothesis for Numerical Data
Analysis of Variance
Using Statistics: The Perfect Parachute Company
The Completely Randomized Design: One-Way Analysis of Variance
F Test for Differences in More than Two Means
Multiple Comparisons: The Tukey-Kramer Procedure
ANOVA Assumptions
Levene's Test for Homogeneity of Variance
The Randomized Block Design
Tests for the Treatment and Block Effects
Multiple Comparisons: The Tukey Procedure
The Factorial Design: Two-Way Analysis of Variance
Testing for Factor and Interaction Effects
Interpreting Interaction Effects
Multiple Comparisons: The Tukey Procedure
Summary
Using Software for ANOVA
Chi-Square Tests and Nonparametric Tests
Using Statistics: Guest Satisfaction at T. C. Resort Properties
Chi-Square Test for Differences between Two Proportions (Independent Samples)
Chi-Square Test for Differences among More than Two Proportions
Chi-Square Test of Independence
McNemar Test for the Difference between Two Proportions (Related Samples)
Chi-Square Test for a Variance or Standard Deviation
Chi-Square Goodness of Fit Tests
Chi-Square Goodness of Fit Test for the Poisson Distribution
Chi-Square Goodness of Fit Test for the Normal Distribution
Wilcoxon Rank Sum Test: Nonparametric Analysis for Two Independent Populations
Wilcoxon Signed Ranks Test: Nonparametric Analysis for Two Related Populations
Kruskal-Wallis Rank Test: Nonparametric Analysis for the One-Way Design
Friedman Rank Test: Nonparametric Analysis for the Randomized Block Design
Summary
Using Software for Chi-Square Tests and Nonparametric Tests
Simple Linear Regression
Using Statistics: Forecasting Sales at the Sunflowers Clothing Stores
Types of Regression Models
The Least-Squares Method
Visual Explorations: Exploring Simple Linear Regression Coefficients
Predictions in Regression Analysis: Interpolation versus Extrapolation
Measures of Variation
Computing the Sum of Squares
The Coefficient of Determination
Standard Error of the Estimate
Assumptions
Residual Analysis
Evaluating the Assumptions
Measuring Autocorrelation: The Durbin-Watson Statistic
Residual Plots to Detect Autocorrelation
The Durbin-Watson Statistic
Inferences about the Slope and Correlation Coefficient Test for the Slope
F Test for the Slope
Confidence Interval Estimate for the Slope
t Test for the Correlation Coefficient
Estimation of Predicted Values
The Confidence Interval Estimate
The Prediction Interval
Pitfalls in Regression and Ethical Issues
Summary
Using Software for Simple Linear Regression
Introduction to Multiple Regression
Using Statistics: Predicting OmniPower Sales
Developing the Multiple Regression Model
Interpreting the Regression Coefficients
Predicting the Dependent Variable Y
R2, Adjusted R2, and the Overall F test 000
Coefficients of Multiple Determination
Test for the Significance of the overall Multiple Regression Model
Residual Analysis for the Multiple Regression Model
Inferences Concerning the Population Regression Coefficients
Test of Hypothesis
Confidence Interval Estimation
Testing Portions of the Multiple Regression Model
Coefficient of Partial Determination
Using Dummy-Variables and Interaction Terms in Regression Models
Interactions
Logistic Regression
Summary
Using Software for Multiple Regression
Multiple Regression Model Building
Using Statistics: Predicting Standby Hours for Unionized Artists
The Quadratic Regression Model
Finding the Regression Coefficients and Predicting Y
Testing for the Significance of the Quadratic Effect
Testing the Quadratic Effect
The Coefficient of Multiple Determination
Using Transformations in Regression Models
The Square Root Transformation
The Log Transformation
Influence Analysis
Collinearity
Model Building
The Stepwise Regression Approach to Model Building
The Best-Subsets Approach to Model Building
Model Validation
Pitfalls in Multiple Regression and Ethical Issues
Pitfalls in Multiple Regression
Ethical Issues
Summary
Using Software for Multiple Regression Model Building
Time-Series Forecasting and Index Numbers
Using Statistics: Forecasting Revenues for Three Companies
The Importance of Business Forecasting
Component Factors of the Classical Multiplicative Time-Series Model
Smoothing the Annual Time Series
Moving Averages
Exponential Smoothing
Least-Squares Trend Fitting and Forecasting
The Linear Trend Model
The Quadratic Trend Model
The Exponential Trend Model
The Holt-Winters Method for Trend-Fitting and Forecasting
Autoregressive Modeling for Trend Fitting and Forecasting
Choosing an Appropriate Forecasting Model
Performing a Residual Analysis
Measuring the Magnitude of the Residual Error through Squared or Absolute Differences
Principle of Parsimony
Time-Series Forecasting of Monthly or Quarterly Data
Least-Squares Forecasting with Monthly or Quarterly Data
Index Numbers
The Price Index
Aggregate Price Indexes
Weighted Aggregate Price Indexes
Paasche Price Index
Some Common Price Indexes
Pitfalls Concerning Time-Series Analysis
Summary
Using Software for Time-Series Forecasting and Index Numbers
Decision Making
Using Statistics: Selecting Stocks
Payoff Tables and Decision Trees
Criteria for Decision Making
Expected Monetary Value
Expected Opportunity Loss
Return-to-Risk Ratio
Decision Making with Sample Information
Utility
Summary
Using Software for Decision Making
Statistical Applications in Quality and Productivity Management
Total Quality Management
Six Sigma¬“ Management
The Theory of Control Charts
Control Chart for the Proportion of Nonconforming Items The p Chart
The Red Bead Experiment: Understanding Process Variability
Control Chart for an Area of Opportunity the c Chart
Control Charts for the Range and the Mean
The R Chart: A Control Chart for Dispersion
The Chart
Process Capability
Customer Satisfaction and Specification Limits
Capability Indices
CPL, CPU, Cpk
Summary
Using Software for Control Charts
Answers to Self-Test Problems
Answers to Even-Numbered Problems
Appendices
Review of Arithmetic and Algebra
Summation Notation
Statistical Symbols and Greek Alphabet
CD-ROM Contents
Tables
Configuring and Customizing Microsoft Excel For Use With This Text
PHStat2 User's Guide
Index
CD-ROM Topics
Table of Contents provided by Publisher. All Rights Reserved.


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