Data Analysis in Plain English with Microsoft Excel

Name: Data Analysis in Plain English with Microsoft Excel
Price: 9.89 USD
Availability: InStock
ISBN: 9780534526504

ISBN-10: 0534526500

ISBN-13: 9780534526504

Edition: 1999

Authors: Harvey J. Brightman

List price: $186.95

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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.

Book details

List price: $186.95
Copyright year: 1999
Publisher: Brooks/Cole
Publication date: 9/10/1998
Binding: Paperback
Pages: 594
Size: 7.50" wide x 9.25" long x 1.00" tall
Weight: 1.980
Language: English




Univariate Data



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



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



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



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



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



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



Multivariate Data



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



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



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



Forecasting and Time Series Analysis


Data Patterns and Forecasting


Management Scenarios and Data Bases


Forecasting Meandering Patterns


Forecasting Seasonal Patterns


Summary


Exercises


Appendices



Quality Area



Quality Improvement and Statistical Process Contro