Basic Business Statistics : Concepts and Applications

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  • Edition: 9th
  • Format: Hardcover
  • Copyright: 1/1/2004
  • Publisher: PH
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Reflecting the latest trends and methodologies, this comprehensive and innovative study on business statistics uses a practical, data-analytic approach. Based on the development of a survey which integrates the various topics and provides a cohesive study of descriptive statistics, probability, statistical inference, and regression analysis, it now focuses on data analysis and interpretation of computer output with a reduced focus on hand calculations. Creates an Employee Satisfaction Survey yielding 400 sample responses which readers can use to integrate such topics as descriptive statistics, probability, statistical inference, and regression analysis. Offers over 1200 realistic applications problems, 170 Survey/Database Projects, and relevant case studies. Contains two distinct types of summary sections to facilitate understanding - Exploratory and Confirmatory Data Analysis sections (looking at the four components of good data analysis - plotting, observing, computing and describing), and Ethical Issue sections (helps readers learn to think critically about the ramifications of the ethical issues involved in data analysis). Provides thorough coverage of regression and multiple regression, and considers many popular methodologies, including exploratory data analysis (EDA) techniques and dot charts, Pareto diagrams and supertables. Now opens each chapter with a "Using Statistics" example that shows how statistics can be applied to accounting, finance, management or marketing - plus includes appendices on using Microsoft Excel 97 and Minitab; an additional chapter on multiple regression that focuses on model building; a new chapter on decision making; a running case study, and more.

Table of Contents

Introduction and Data Collection
Overview of Statistics
Applying Statistics to Business
How This Text Is Organized
The Importance of Collecting Data
Identifying Sources of Data
Design of Survey Research
Types of Survey Sampling Methods
Types of Data
Evaluating Survey Worthiness
Presenting Data in Tables and Charts
Organizing Numerical Data
Tables and Charts for Numerical Data
Graphing Bivariate Numerical Data
Tables and Charts for Categorical Data
Tabulating and Graphing Bivariate Categorical Data
Graphical Excellence
Numerical Descriptive Measures
Exploring Numerical Data and Their Properties
Measures of Central Tendency, Variation, and Shape
Obtaining Descriptive Summary Measures from a Population
Obtaining Descriptive Summary Measures from a Frequency Distribution
Exploratory Data Analysis
The Coefficient of Correlation
Pitfalls in Numerical Descriptive Measures and Ethical Issues
Basic Probability
Basic Probability Concepts
Conditional Probability
Bayes' Theorem
Counting Rules
Ethical Issues and Probability
Some Important Discrete Probability Distributions
The Probability Distribution for a Discrete Random Variable
Covariance and Its Application in Finance
Binomial Distribution
Hypergeometric Distribution
Poisson Distribution
CD ROM Topic Using the Poisson Distribution to Approximate the Binomial Distribution
The Normal Distribution and Other Continuous Distributions
The Normal Distribution
Evaluating the Normality Assumption
The Uniform Distribution
The Exponential Distribution
CD ROM Topic The Normal Approximation to the Binomial Distribution
Sampling Distributions
Sampling Distribution of the Mean
Sampling Distribution of the Proportion
CD ROM Topic Sampling from Finite Populations
Confidence Interval Estimation
Confidence Interval Estimation of the Mean (s Known)
Confidence Interval Estimation of the Mean (s Unknown)
Confidence Interval Estimation for the Proportion
Determining Sample Size
Applications of Confidence Interval Estimation in Auditing
Confidence Interval Estimation and Ethical Issues
CD ROM Topic Estimation and Sample Size Determination for Finite
Fundamentals of Hypothesis Testing: One-Sample Tests
Hypothesis-Testing Methodology
Z Test of Hypothesis for the Mean (s Known)
One-Tailed Tests
T Test of Hypothesis for the Mean (s Unknown)
Z Test of Hypothesis for the Proportion
X2 Test of Hypothesis for the Variance or Standard Deviation
Potential Hypothesis-Testing Pitfalls and Ethical Issues
CD ROM Topic The Power of a Test
Two Sample Tests with Numerical Data
Comparing Two Independent Samples: Tests for Differences in Two Means
F Test for Differences in Two Variances
Comparing Two Related Samples: Tests for the Mean Difference
Wilcoxon Rank Sum Test: Nonparametric Analysis for Two Independent Populations
Wilcoxon Signed-Ranks Test: Nonparametric Analysis for Two Related Populations
Analysis of Variance
The Completely Randomized Design: One-Way Analysis of Variance
The Randomized Block Design
The Factorial Design: Two-Way Analysis of Variance
Kruskal-Wallis Rank Test: Nonparametric Analysis for the One-Way Design
Friedman Rank Test: Nonparametric Analysis for the Randomized Block Design
Tests for Two or More Samples with Categorical Data
Z Test for the Difference between Two Proportions
Chi-Square Test for Differences between Two Proportions
Chi-Square Test for Differences in Among More than Two Proportions
Chi-Square Test of Independence
CD ROM Topic Chi-Square Goodness of Fit Tests
Simple Linear Regression
Types of Regression Models
Determining the Simple Linear Regression Equation
Measures of Variation
Residual Analysis
Measuring Autocorrelation: The Durbin-Watson Statistic
Inferences about the Slope and Correlation Coefficient
Estimation of Predicted Values
Pitfalls in Regression and Ethical Issues
Computations in Simple Linear Regression
Introduction to Multiple Regression
Developing the Multiple Regression Model
Residual Analysis for the Multiple Regression Model
Testing for the Significance of the Multiple Regression Model
Inferences Concerning the Population Regression Coefficients
Testing Portions of the Multiple Regression Model
Using Dummy-Variables and Interaction Terms in Regression Models
Introduction to Logistic Regression
Multiple Regression Model Building
The Quadratic Regression Model
Using Transformations in Regression Models
Influence Analysis
Model Building
Pitfalls in Multiple Regression and Ethical Issues
Time-Series Analysis
The Importance of Business Forecasting
Component Factors of the Classical Multiplicative Time-Series Model
Smoothing the Annual Time Series
Least-Squares Trend Fitting and Forecasting
The Holt-Winters Method for Trend Fitting and Forecasting
Autoregressive Modeling for Trend Fitting and Forecasting
Choosing an Appropriate Forecasting Model
Time-Series Forecasting of Monthly or Quarterly Data
Index Numbers
Pitfalls Concerning Time-Series Analysis
Decision Making
Payoff Tables and Decision Trees
Criteria for Decision Making
Decision Making with Sample Information
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
The c Chart
Control Charts for the Range and the Mean
Process Capability
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