Data analysis in plain English with Microsoft Excel

著者

書誌事項

Data analysis in plain English with Microsoft Excel

Harvey J. Brightman

Duxbury Press, c1999

  • : pbk

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注記

Includes bibliographical references and index

内容説明・目次

内容説明

Harvey Brightman's accessible, easy-to-understand new book focuses on helping readers learn essential statistical concepts and data analysis. In an intuitive and non-mathematical writing style, Brightman uses actual business applications and covers practical insights in business problem solving using Microsoft Excel as the primary computational tool. His clear, to-the-point presentation gives students a 'map' for learning what data analysis techniques to use and when to use them. Brightman presents descriptive and inferential methods in sequential chapters, and introduces probability only as needed and then only on a very limited basis.

目次

PART I: UNIVARIATE DATA 1. DATA ANALYSIS FOR IMPROVED DECISION MAKING Introduction / Types of Problems / Mental Models and Effective Problem Solving / Types of Variation / Types of Data Business Professionals Use / Data Measurement Scales / Data Sources for Improved Decision Making / Data Collection through Surveys / Summary / Exercises / Appendices 2. DESCRIBING UNIVARIATE DATA Introduction / Management Scenarios and Data Sets / Displaying Cross-Sectional Data for Quantitative Variables / Summarizing Cross-Sectional Data for Quantitative Variables / Assessing Assignable Cause Variation: Cross-Sectional Data for Quantitative Variables / Cross-Sectional Data for Qualitative Variables / Displaying Time-Ordered Data / Summarizing Time-Ordered Data / Assessing Assignable-Cause Variation for Time-Ordered Data / Guide to Data-Analysis Methods / Exercises / Appendices 3. BASIC PROBABILITY CONCEPTS AND PROBLEMS IN ASSESSING PROBABILITIES Introduction / Types of Probability / Computing Conditional Probabilities and Statistical Independence / Using Probability Trees to Minimize Managerial Judgment Errors / Key Ideas / Exercises 4. SAMPLING AND SAMPLING DISTRIBUTIONS The Need for Statistical Inference Methods / Exploring the Distribution of the Sample Mean / The Normal Distribution / Exploring the Distribution of the Sample Proportion / Exploring the Distribution of the Sample Variance / Key Ideas and Overview / Exercises / Appendices 5. STATISTICAL INFERENCE I: CONFIDENCE INTERVALS The Statistical Inference Process / Management Scenarios and Data Sets / General Principles of Confidence Intervals / Confidence Intervals on an Unknown Population Mean / Confidence Interval on an Unknown Population Proportion / Determining the Sample Size / Confidence Interval on an Unknown Population Variance / Key Ideas and Overview / Exercises / Appendices 6. STATISTICAL INFERENCE II: HYPOTHESIS TESTING ON ONE POPULATION PARAMETER Introduction / Management Scenarios and Data Sets / Hypothesis Testing on One Population Mean / Hypothesis Testing on One Population Proportion / Key Ideas and Summary / Exercises / Appendices PART II: MULTIVARIATE DATA 7. DESCRIBING MULTIVARIATE DATA Introduction / Management Scenarios and Data Sets / Analyzing Mixed Cross-Sectional Data / Analyzing Qualitative Cross-Sectional Data / Analyzing Quantitative Cross-Sectional Data / Analyzing Time-Ordered Quantitative Data / Analyzing Time-Ordered Quantitative Data: Autoregressive Equations / Correlation and Cross-Correlation / Guidelines for Using Chapter's Descriptive Methods / Exercises / Appendices 8. HYPOTHESIS TESTING ON TWO POPULATION PARAMETERS Statistical Inference Process / Management Scenarios and Data Sets / Hypothesis Testing on the Difference in the Means of Two Independent Populations Having Equal Variances / Hypothesis Testing on the Difference in the Means of Two Independent Populations Having Unequal Variances / Testing for the Difference between the Means of Two Related Populations: The Paired Sample t-Test for the Matched Pair Design / Testing for the Difference between the Proportions of Two Independent Populations / Testing for the Equality of Variances from Two Independent Populations: The F-test / Roadmap for the Chapter / Exercises / Appendices 9. REGRESSION ANALYSIS AND CHI-SQUARE TEST OF INDEPENDENCE Introduction / Management Scenarios and Data Sets / Introduction to Regression Analysis / Scatter Diagramming and the Analysis of Variance / Evaluating the Regression Model Assumptions: Graphical Analysis of Residuals / Using the Estimated Regression Models for Making Predictions / Multicollinearity / Chi-Square Test of Independence / Chapter Overview / Exercises / Appendices 10. FORECASTING AND TIME SERIES ANALYSIS Data Patterns and Forecasting / Management Scenarios and Data Bases / Forecasting Meandering Patterns / Forecasting Seasonal Patterns / Summary / Exercises / Appendices PART III: QUALITY AREA 11. QUALITY IMPROVEMENT AND STATISTICAL PROCESS CONTROL Quality Improvement, Productivity, and Business Success / Types of Quality / The "Big 8" Quality Improvement Tools / Management Scenarios and Data Sets / Control Charts for Variables: x [overbar] and Standard Deviation / Control Charts for Proportion Nonconforming / Control Charts for the Number of Nonconformities: The Demerit Chart / Ideas and Overview / Exercises / Appendices 12. DESIGN OF EXPERIMENTS The Role of Experimentation in Process and Product Improvement / Management Scenarios and Data Sets / Designing Experiments / One Factor Completely Random Design / The Two-Factor Completely Random Design / Exercises / Appendices / INDEX

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