Data Analysis for Managers With Microsoft Excel With Infotrac,9780534383664

Data Analysis for Managers With Microsoft Excel With Infotrac

by ; ;
Edition: 2nd
ISBN13: 9780534383664
ISBN10: 0534383661
Format: Paperback
Pub. Date: 2003-02-19
Publisher(s): Duxbury Press
List Price: $244.95

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Summary

This text presents statistical concepts and methods in a unified, modern, spreadsheet-oriented approach. Featuring a wealth of businessapplications, this examples-based text illustrates a variety of statistical methods to help students analyze data sets and uncoverimportant information to aid decision-making. DATA ANALYSIS FOR MANAGERS contains professional StatPro add-ins for Microsoft Excelfrom Palisade, valued at one hundred fifty dollars packaged at no additional cost with every new text.

Table of Contents

Preface xv
Introduction to Managerial Statistics
1(28)
Introduction
2(2)
An Overview of the Book
4(7)
Excel versus Stand-alone Statistical Software
11(1)
A Sampling of Examples
12(14)
Conclusion
26(3)
Entertainment on a Cruise Ship
27(2)
Part 1 Getting, Describing, and Summarizing Data
Describing Data: Graphs and Tables
29(46)
Introduction
31(1)
Basic Concepts
32(4)
Frequency Tables and Histograms
36(11)
Analyzing Relationships with Scatterplots
47(4)
Time Series Plots
51(4)
Exploring Data with Pivot Tables
55(12)
Conclusion
67(8)
Customer Arrivals at Bank98
73(1)
Automobile Production and Purchases
73(1)
Saving, Spending, and Social Climbing
74(1)
Describing Data: Summary Measures
75(54)
Introduction
76(2)
Measures of Central Location
78(2)
Quartiles and Percentiles
80(1)
Minimum, Maximum, and Range
81(1)
Measures of Variability: Variance and Standard Deviation
82(5)
Obtaining Summary Measures with Add-Ins
87(4)
Measures of Association: Covariance and Correlation
91(4)
Describing Data Sets with Boxplots
95(5)
Applying the Tools
100(17)
Conclusion
117(12)
The Dow Jones Averages
125(2)
Other Market Indexes
127(1)
Correct Interpretation of Means
128(1)
Getting the Right Data
129(60)
Introduction
130(1)
Sources of Data
131(3)
Using Excel's AutoFilter
134(6)
Complex Queries with the Advanced Filter
140(6)
Importing External Data from Access
146(12)
Creating Pivot Tables from External Data
158(2)
Web Queries
160(10)
Other Data Sources on the Web
170(6)
Cleansing the Data
176(7)
Conclusion
183(6)
EduToys, Inc.
187(2)
Part 2 Probability, Uncertainty, and Decision Making
Probability and Probability Distributions
189(50)
Introduction
190(1)
Probability Essentials
191(8)
Distribution of a Single Random Variable
199(4)
An Introduction to Simulation
203(4)
Distribution of Two Random Variables: Scenario Approach
207(6)
Distribution of Two Random Variables: Joint Probability Approach
213(7)
Independent Random Variables
220(4)
Weighted Sums of Random Variables
224(7)
Conclusion
231(8)
Simpson's Paradox
238(1)
Normal, Binomial, Poisson, and Exponential Distributions
239(60)
Introduction
240(1)
The Normal Distribution
241(9)
Applications of the Normal Distribution
250(12)
The Binomial Distribution
262(4)
Applications of the Binomial Distribution
266(12)
The Poisson and Exponential Distributions
278(5)
Fitting a Probability Distribution to Data: BestFit
283(5)
Conclusion
288(11)
EuroWatch Company
296(1)
Cashing in on the Lottery
297(2)
Decision Making Under Uncertainty
299(66)
Introduction
300(2)
Elements of a Decision Analysis
302(9)
The PrecisionTree Add-In
311(9)
More Single-Stage Examples
320(9)
Multistage Decision Problems
329(8)
Bayes' Rule
337(8)
Incorporating Attitudes Toward Risk
345(9)
Conclusion
354(11)
Jogger Shoe Company
363(1)
Westhouser Paper Company
364(1)
Part 3 Statistical Inference
Sampling and Sampling Distributions
365(50)
Introduction
366(1)
Sampling Terminology
366(1)
Methods for Selecting Random Samples
367(17)
An Introduction to Estimation
384(20)
Conclusion
404(11)
Sampling from Videocassette Renters
413(2)
Confidence Interval Estimation
415(64)
Introduction
416(1)
Sampling Distributions
417(6)
Confidence Interval for a Mean
423(6)
Confidence Interval for a Total
429(2)
Confidence Interval for a Proportion
431(6)
Confidence Interval for a Standard Deviation
437(3)
Confidence Interval for the Difference Between Means
440(13)
Confidence Interval for the Difference Between Proportions
453(5)
Controlling Confidence Interval Length
458(8)
Conclusion
466(13)
Harrigan University Admissions
474(1)
Employee Retention at D&Y
475(1)
Delivery Times at SnowPea Restaurant
476(1)
The Bodfish Lot Cruise
477(2)
Hypothesis Testing
479(68)
Introduction
480(1)
Concepts in Hypothesis Testing
481(7)
Hypothesis Tests for a Population Mean
488(7)
Hypothesis Tests for Other Parameters
495(21)
Tests for Normality
516(6)
Chi-Square Test for Independence
522(4)
One-Way ANOVA
526(8)
Conclusion
534(13)
Regression Toward the Mean
540(1)
Baseball Statistics
541(1)
The Wichita Anti-Drunk Driving Advertising Campaign
542(2)
Deciding Whether to Switch to a New Toothpaste Dispenser
544(3)
Part 4 Regression, Forecasting, and Time Series
Regression Analysis: Estimating Relationships
547(72)
Introduction
548(3)
Scatterplots: Graphing Relationships
551(9)
Correlations: Indicators of Linear Relationships
560(2)
Simple Linear Regression
562(11)
Multiple Regression
573(6)
Modeling Possibilities
579(27)
Validation of the Fit
606(2)
Conclusion
608(11)
Quantity Discounts at the FirmChair Company
616(1)
Housing Price Structure in MidCity
616(1)
Demand for French Bread at Howie's
617(1)
Investing for Retirement
618(1)
Regression Analysis: Statistical Inference
619(70)
Introduction
620(1)
The Statistical Model
621(4)
Inferences About the Regression Coefficients
625(10)
Multicollinearity
635(4)
Include/Exclude Decisions
639(5)
Stepwise Regression
644(4)
The Partial F Test
648(8)
Outliers
656(6)
Violations of Regression Assumptions
662(4)
Prediction
666(6)
Conclusion
672(17)
The Artsy Corporation
683(2)
Heating Oil at Dupree Fuels Company
685(1)
Developing a Flexible Budget at the Gunderson Plant
686(1)
Forecasting Overhead at Wagner Printers
687(2)
Time Series Analysis and Forecasting
689(72)
Introduction
690(1)
Forecasting Methods: An Overview
691(7)
Testing for Randomness
698(7)
Regression-Based Trend Models
705(9)
The Random Walk Model
714(4)
Autoregression Models
718(5)
Moving Averages
723(6)
Exponential Smoothing
729(10)
Seasonal Models
739(15)
Conclusion
754(7)
Arrivals at the Credit Union
759(1)
Forecasting Weekly Sales at Amanta
760(1)
Part 5 Design of Experiments, Quality Control, and Data Mining
Analysis of Variance and Experimental Design
761(52)
Introduction
763(3)
One-Way ANOVA
766(12)
Using Regression to Perform ANOVA
778(4)
The Multiple Comparison Problem
782(5)
Two-Way ANOVA
787(12)
More About Experimental Design
799(8)
Conclusion
807(6)
Krentz Appraisal Services
812(1)
Data Mining Techniques: Discriminant Analysis, Logistic Regression, and OLAP
813(52)
Introduction
815(2)
Discriminant Analysis
817(17)
Logistic Regression
834(14)
Online Analytical Processing (OLAP)
848(10)
Conclusion
858(7)
Understanding Cereal Brand Preferences
864(1)
Statistical Process Control
865(60)
Introduction
867(1)
Deming's 14 Points
868(4)
Basic Ideas Behind Control Charts
872(1)
Control Charts for Variables
873(19)
Control Charts for Attributes
892(8)
Process Capability
900(11)
Conclusion
911(14)
The Lamination Process at Intergalactica
917(2)
Paper Production for Fornax at the Pluto Mill
919(6)
Appendix A: Statistical Reporting
925(18)
A.1 Introduction
925(1)
A.2 Suggestions for Good Statistical Reporting
926(5)
A.3 Examples of Statistical Reports
931(11)
A.4 Conclusion
942(1)
References 943(4)
Index 947

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