Data Analysis and Decision Making with Microsoft Excel

Name: Data Analysis and Decision Making with Microsoft Excel
Price: 5.7 USD
Availability: InStock
ISBN: 9780324662443

ISBN-10: 0324662440

ISBN-13: 9780324662443

Edition: 3rd 2009 (Revised)

Authors: S. Christian Albright, Wayne Winston, Christopher Zappe

List price: $328.95

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Description:

Master data analysis, modeling, and spreadsheet use with DATA ANALYSIS AND DECISION MAKING WITH MICROSOFT EXCEL! With a teach-by-example approach, student-friendly writing style, and complete Excel integration, this quantitative methods text provides you with the tools you need to succeed. Margin notes, boxed-in definitions and formulas in the text, enhanced explanations in the text itself, and stated objectives for the examples found throughout the text make studying easy. Problem sets and cases provide realistic examples that enable you to see the relevance of the material to your future as a business leader. The CD-ROMs packaged with every new book include the following add-ins: the…

Book details

List price: $328.95
Edition: 3rd
Copyright year: 2009
Publisher: Cengage South-Western
Publication date: 6/24/2008
Binding: Hardcover
Pages: 1104
Size: 8.25" wide x 10.00" long x 1.75" tall
Weight: 4.686
Language: English

S. Christian Albright received his B.S. degree in mathematics from Stanford in 1968 and his Ph.D. in operations research from Stanford in 1972. Since then, he has been teaching in the Operations and Decision Technologies Department in the Kelley School of Business at Indiana University. He has taught courses in management science, computer simulation, and statistics to all levels of business students: undergraduates, MBAs, and doctoral students. His current interest is in spreadsheet modeling, including development of VBA applications in Excel. Dr. Albright has published more than 20 articles in leading operations research journals in the area of applied probability as well as a number of…

Wayne L. Winston is a professor of Decision Sciences at Indiana University's Kelley School of Business and the recipient of more than 30 teaching awards. For the past 20 years, Wayne has also taught Fortune 500 companies how to use Excel to make smarter business decisions. He has written 15 books on Excel, management science, and mathematics in sports.




Getting, Describing, and Summarizing Data


Introduction to Data Analysis and Decision Making



Introduction


An Overview of the Book


The Methods


The Software


A Sampling of Examples


Modeling and Models


Conclusion



Describing Data: Graphs and Tables


Introduction


Basic Concepts


Frequency Tables and Histograms


Analyzing Relationships with Scatterplots


Time Series Graphs


Exploring Data with Pivot Tables


Conclusion



Describing Data: Summary Measures


Introduction


Measures of Central Location


Quartiles and Percentiles


Minimum, Maximum, and Range


Measures of Variability: Variance and Standard Deviation


Obtaining Summary Measures with StatTools


Measures of Association: Covariance and Correlation


Describing Data Sets with Boxplots


Applying the Tools


Conclusion



Getting the Right Data


Introduction


Sources of Data


Excel Tables for Filtering, Sorting, and Summarizing


Complex Queries with the Advanced Filter


Importing External Data from Access


Creating Pivot Tables from External Data


Web Queries


Other Data Sources on the Web


Cleansing the Data


Conclusion



Probability, Uncertainty, and Decision Making



Probability and Probability Distributions


Introduction


Probability Essentials


Distribution of a Single Random Variable


An Introduction to Simulation


Distribution of Two Random Variables: Scenario Approach


Distribution of Two Random Variables: Joint Probability Approach


Independent Random Variables


Weighted Sums of Random Variables


Conclusion



Normal, Binomial, Poisson, and Exponential Distributions


Introduction


The Normal Distribution


Applications of the Normal Distribution


The Binomial Distribution


Applications of the Binomial Distribution


The Poisson and Exponential Distributions


Fitting a Probability Distribution to Data: BestFit


Conclusion



Decision Making Under Uncertainty


Introduction


Elements of a Decision Analysis


The PrecisionTree Add-In


Bayes' Rule


Multistage Decision Problems


Incorporating Attitudes Toward Risk


Conclusion



Statistical Inference



Sampling and Sampling Distributions


Introduction


Sampling Terminology


Methods for Selecting Random Samples


An Introduction to Estimation


Conclusion



Confidence Interval Estimation


Introduction


Sampling Distributions


Confidence Interval for a Mean


Confidence Interval for a Total


Confidence Interval for a Proportion


Confidence Interval for a Standard Deviation


Confidence Interval for the Difference Between Means


Confidence Interval for the Difference Between Proportions Controlling Confidence Interval Length


Conclusion



Hypothesis Testing


Introduction


Concepts in Hypothesis Testing


Hypothesis Tests for a Population Mean


Hypothesis Tests for Other Parameters


Tests for Normality


Chi-Square Test for Independence


One-Way ANOVA


Conclusion



Regression, Forecasting, and Time Series



Regression Analysis: Estimating Relationships


Introduction


Scatterplots: Graphing Relationships


Correlations: Indicators of Linear Relationships Simple Linear Regression


Multiple Regression


Modeling Possibilities


Validation of the Fit


Conclusion



Regression Analysis: Statistical Inference Introduction


The Statistical Model


Inferences About the Regression Coefficients


Multicollinearity


Include/Exclude Decisions


Stepwise Regression.The Partial F Test


Outliers


Violations of Regression Assumptions


Prediction


Conclusion



Time Series Analysis and Forecasting


Introduction


Forecasting Methods: An Overview


Testing for Randomness


Regression-Based Trend Models


The Random Walk Model


Autoregression Models


Moving Averages


Exponential Smoothing


Seasonal Models


Winters' Exponential Smoothing Model


Conclusion



Decision Modeling



Introduction to Optimization Modeling


Introduction


Introduction to Optimization


A Two-Variable Model


Sensitivity Analysis Properties of Linear Models


Infeasibility and Unboundedness


A Product Mix Model


A Multiperio