Thursday, December 18, 2008

The Supply Management Handbook or A Second Course in Statistics

The Supply Management Handbook

Author: Joseph L Cavinato

Turn to the collective wisdom of the field's top experts to understand and solve even the most complex supply management issue


For more than three decades, The Supply Management Handbook (formerly The Purchasing Handbook) has been vital for purchasing and supply professionals in every field and industry. This latest edition comprehensively updates and revises this classic to encompass the ongoing shift from simple purchasing to a new, more technology-based imperative--identifying and managing supply chain sources and strategies.


Addressing every essential issue from outsourcing to total cost of ownership to negotiations and contract management, an international team of supply management experts offers the authoritative, practical coverage you need to survive and thrive in today's ever-changing supply management environment. Topics include:

  • What key organizations are doing now to develop and implement next-generation supply methodologies
  • An organization's duty to and interaction with society, and insights for addressing the evolving concept of social responsibility in the supply arena
  • A five-step best practices framework for implementing total cost of ownership in supply management
  • Logistics considerations for the supply management professional
  • Supply management in a risk-sensitive environment
  • Sharpening your supply management skills

    Dramatic social and technological changes have brought new roles, responsibilities, and challenges to supply managers - along with exciting new opportunities. This definitive reference is the most trusted and efficient way to prosper in this ever-changing field.

    Joseph L.Cavinato is a professor at The AmericanSchool of International Management.

    Ralph G. Kauffman is a professor at the Universityof Houston-Downtown.

    Anna E. Flynn is vice president and associate professorat the Institute for Supply Management. The Institute for Supply Managementproduces the influential Manufacturing Report, a monthlyeconomic indicator.



    New interesting book: Local Breads or Crepes

    A Second Course in Statistics: Regression Analysis

    Author: William Mendenhall

    This reader-friendly book focuses on building linear statistical models and developing skills for implementing regression analysis in real-life situations. It includes applications for a range of fields including engineering, sociology, and psychology, as well as traditional business applications. The authors use the latest material available from news articles, magazines, professional journals, the Internet, and actual consulting problems to illustrate real business situations and how to solve them using the tools of regression analysis. In addition, this book emphasizes model building and multiple regression models and pays special attention to model validation and spline regression. For professionals in any number of fields, including engineering, sociology, and psychology, who would benefit from learning how to use regression analysis to solve problems.



    Table of Contents:

    1.      A Review of Basic Concepts (Optional)

    1.1  Statistics and Data

    1.2  Populations, Samples and Random Sampling

    1.3  Describing Qualitative Data

    1.4  Describing Quantitative Data Graphically

    1.5  Describing Quantitative Data Numerically

    1.6  The Normal Probability Distribution

    1.7  Sampling Distributions and the Central Limit Theorem

    1.8  Estimating a Population Mean

    1.9  Testing a Hypothesis about a Population mean

    1.10          Inferences about the Difference Between Two Population Means

    1.11          Comparing Two Population Variances

    2.      Introduction to Regression Analysis

    2.1 Modeling a Response

    2.2 overview of Regression Analysis

    2.3 Regression Applications

    2.4 Collecting the Data for Regression

    3.      Simple Linear Regression

    3.1 Introduction

    3.2 TheStraight-Line Probabilistic Model

    3.3 Fitting the Model: The Method of Least-Squares

    3.4 Model Assumptions

    3.5 An Estimator of σ2

    3.6 Assessing the Utility of the Model: Making Inferences About the Slope ß1

    3.7 The Coefficient of Correlation

    3.8 The Coefficient of Determination

    3.9 Using the Model for Estimation and Prediction

    3.10 A Complete Example

    3.11 Regression Through the Origin (Optional)

    3.12 A Summary of the Steps to Follow in a Simple Linear Regression Analysis

    4. Multiple Regression Models

                4.1 General Form of a Multiple Regression Model

                4.2 Model Assumptions

                4.3 A First-Order Model with Quantitative Predictors

                4.4 Fitting the Model: The Method of Least Squares

                4.5 Estimation of σ2 , the variance of ε

                 4.6 Inferences about the ß parameters

                4.7 The Multiple Coefficient of Determination, R2

                    4.8 Testing the Utility of a Model: The Analysis of Variance F test

                4.9 An Interaction Model with Quantitative Predictors

                4.10 A Quadratic (Second-Order) Model with a Quantitative Predictor

                4.11 Using the model for Estimation and Prediction

                4.12 More Complex Multiple Regression Models (Optional)

                4.13 A Test for Comparing Nested Models

                4.14 A Complete Example

                4.15 A Summary of the Steps to Follow in a Multiple Regression Analysis

    5. Model Building

                5.1 Introduction: Why Model Building is Important

                5.2 The Two Types of independent Variables: Quantitative and Qualitative

                5.3 Models with a Single Quantitative Independent Variable

                5.4 First-Order Models with Two or More Quantitative Independent Variables

                5.5. Second-Order Models with Two or More Quantitative Independent Variables

                5.6 Coding Quantitative Independent Variables (Optional)

                5.7 Models with One Qualitative Independent Variable

                5.8 Models with Two Qualitative Independent Variables

                5.9 Models with Three or more Qualitative Independent Variables

                5.10 Models with Both Quantitative and Qualitative Independent Variables

                5.11 External Model Validation (Optional)

                5.12 Model Building: An Example

    6. Variable Screening Methods

                6.1 Introduction: Why Use a Variable Screening Method?

                6.2 Stepwise Regression

                6.3 All-Posssible-Regressions Selection Procedure

                6.4 Caveats

    7. Some Regression Pitfalls

                7.1 Introduction

                7.2 Observational DataVersus Designed Experiments

                7.3 Deviating from the Assumptions

                7.4 Parameter Estimability and Interpretation

                7.5 Multicollinearity

                7.6 Extrapolation: Predicting Outside the Experimental Region

                7.7 Data Transformations

    8. Residual Analysis

                8.1 Introduction

                8.2 Plotting Residuals and Detecting Lack of Fit

                8.3 Detecting Unequal Variances

                8.4 Checking the Normality Assumption

                8.5 Detecting Outliers and Identifying Influential Observations

                8.6 Detecting Residual Correlation: The Durbin-Watson Test

    9. Special Topics in Regression (Optional)

                9.1 Introduction

                9.2 Piecewise Linear Regression

                9.3 Inverse Prediction

                9.4 Weighted Least Squares

                9.5 Modeling Qualitative Dependent Variable

                9.6 Logistic Regression

                9.7 Ridge Regression

                9.8 Robust Regression

                9.9 Nonparametric Regression Models

    10. Introduction to Time Series Modeling and Forecasting

                10.1 What is a Time Series?

                10.2 Time Series Components

                10.3 Forecasting using Smoothing Techniques (Optional)

                10.4 Forecasting: The Regression Approach

                10.5 Autocorrelation and Autoregressive Error Models

                10.6 Other Models for Autocorrelated Errors (Optional)

                10.7 Constructing Time Series Models

                10.8 Fitting Time Series Models With Autoregressive Errors

                10.9 Forecasting with Time Series Autoregressive Models

                10.10 Seasonal Time Series Models: An Example

                10.11 Forecasting Using Lagged Values of the Dependent Variable (Optional)

    11. Principles of Experimental Design

                11.1 Introduction

                11.2 Experimental Design Terminology

                11.3 Controlling the Information in an Experiment

                11.4 Noise-Reducing Designs

                11.5 Volume-Increasing Designs

                11.6 Selecting the Sample Size

                11.7 The Importance of Randomization

    12. The Analysis of Variance for Designed Experiments

                12.1 Introduction

                12.2 The Logic Behind Analysis of Variance

                12.3. One-Factor Completely Randomized Designs

                12.4 Randomized Block Designs

                12.5 Two-Factor Factorial Experiments

                12.6 More Complex Factorial Designs (Optional)

                12.7 Follow up Analysis: Tukey’s Multiple Comparisons of Means

                12.8 Other Multiple Comparisons Methods (Optional)

                12.9 Checking ANOVA Assumptions

    13. CASE STUDY: Modeling the Sale Prices of Residential Properties in Four Neighborhoods

                13.1 The Problem

                13.2 The Data

                13.3 The Theoretical Model

                13.4 The Hypothesized Regression Models

                13.5 Model Comparisons

                13.6 Interpreting the Prediction Equation

                13.7 Predicting the Sale Price of a Property

                13.8 Conclusions

    14. CASE STUDY: An Analysis of Rain Levels in California

                14.1 The Problem

                14.2 The Data

                14.3 A Model for Average Annual Precipitation

                14.4 A Residual Analysis of the Model

                14.5 Adjustments to the Model

                14.6 Conclusions

    15. CASE STUDY: Reluctance to Transmit Bad News: the MUM Effect

                15.1 The Problem

                15.2 The Design

                15.3 Analysis of Variance Models and Results

                15.4 Follow up Analysis

                15.5 Conclusions

    16. CASE STUDY: An Investigation of Factors Affecting the Sale Price of Condominium Units Sold at Public Auction

                16.1 The Problem

                16.2 The Data

                16.3 The Models

                16.4 The Regression Analyses

                16.5 An Analysis of the Residuals form Model 3

                16.6 What the Model 3 Regression Analysis Tells Us

                16.7 Comparing the Mean Sale Price for Two Types of Units (Optional)

                16.8 Conclusions

    17. CASE STUDY: Modeling Daily Peak Electricity Demands

                17.1 The Problem

                17.2 The Data

                17.3 The Models

                17.4 The Regression and Autoregression Analyses

                17.5 Forecasting Daily Peak Electricity Demand

                17.6 Conclusions

    Appendix A: The Mechanics of a Multiple Regression Analysis.


    Appendix B: A Procedure for Inverting a Matrix.


    Appendix C: Statistical Tables.


    Appendix D: SAS for Windows Tutorial.


    Appendix E: SPSS for Windows Tutorial.


    Appendix F: MINITAB for Windows Tutorial.


    Appendix G: Sealed Bid Data for Fixed and Competitive Highway Construction Contracts.


    Appendix H: Real Estate Appraisals and Sales Data for Six Neighborhoods in Tampa, Florida.


    Appendix I: Condominium Sales Data.


    Answers to Odd-Numbered Exercises.


    Index.

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