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Data Analysis Using Microsoft Excel Updated for Office XP

ISBN-10: 0534402933
ISBN-13: 9780534402938
Edition: 3rd 2004 (Revised)
List price: $126.95 Buy it from $1.00 Rent it from $33.47
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Description: Spreadsheet skills are important for a first job, and DATA ANALYSIS USING MICROSOFT EXCEL prepares students to enter the world of work with stronger spreadsheet skills. Designed as a supplement to a main statistics text or as a reference for  More...

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Book details

List price: $126.95
Edition: 3rd
Copyright year: 2004
Publisher: Brooks/Cole
Publication date: 3/7/2003
Binding: Paperback
Pages: 296
Size: 7.25" wide x 9.00" long x 0.75" tall
Weight: 1.364
Language: English

Spreadsheet skills are important for a first job, and DATA ANALYSIS USING MICROSOFT EXCEL prepares students to enter the world of work with stronger spreadsheet skills. Designed as a supplement to a main statistics text or as a reference for professionals, this handbook helps students build their proficiency in Microsoft Excel and shows them how to use the built-in capabilities of Excel to analyze data and make decisions. Although many of the examples are business oriented, the step-by-step approach makes this book appropriate for statistical analysis in other courses and academic disciplines.

Michael R. Middleton is a Professor of Decision Sciences at the School of Business and Management, University of San Francisco, where he has taught since 1977. He is a member of the Decision Sciences Institute, the Institute for Operations Research and the Management Sciences, and the American Statistical Association. He has published three books with Duxbury, delivered a variety of seminars and workshops in decision science, and authored two decision science software programs.

Introduction to Excel
Managing Files and Printing
Basic Charts
Univariate Numerical Data
Categorical Data
Bivariate Numerical Data
Probability Distributions
Sampling and Simulation
One-Sample Inference for the Mean
Quality Control Charts
Two-Sample Inference for the Mean
Chi-Square Tests
Analysis of Variance
Simple Linear Regression
Simple Nonlinear Regression
Multiple Regression
Regression using Categorical Data
Autocorrelation and Autoregression
Time Series Smoothing
Time Series Seasonality
Appendix
References
Index

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