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Statistics, Data Analysis, and Decision Modeling

ISBN: 9780130205452 | 0130205451
Format: Paperback
Publisher: Prentice Hall
Pub. Date: 11/1/1999

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SummaryTable of Contents
For a brief or modular course covering business statistics and introductory topics in management science. Designed specifically for today's shorter courses, often found in MBA programs. This text covers the basic concepts of business statistics, data analysis, and management science in a contemporary spreadsheet environment. The authors emphasize practical applications of the approaches to business decision making.
Prefacexi
PART I
Data and Business Decisions
1(18)
The Importance of Data Analysis and Decision Modeling
2... MORE
Types and Sources of Business Data
3(7)
The Tracway database
6(4)
Measurement and Statistics
10(3)
Populations and samples
12(1)
Decision Models
13(1)
Using Microsoft Excel
14(3)
Excel add-ins: PHStat, Crystal Ball, and TreePlan
15(1)
Working with data
15(2)
Summary
17(1)
Questions and Problems
18(1)
Displaying and Summarizing Data
19(38)
Displaying Data with Charts and Graphs
20(10)
Column and bar charts
23(2)
Line charts
25(3)
Pie charts
28(1)
Area charts
29(1)
Scatter Diagrams
29(1)
Summary of graphical display methods
30(1)
Descriptive Statistics
30(13)
Frequency distributions and histograms
32(2)
Measures of central tendency
34(2)
Measures of dispersion
36(2)
Coefficient of variation
38(1)
Measures of shape
38(1)
Excel's descriptive statistics tool
38(3)
Data profiles and proportions
41(2)
Visual Display of Statistical Measures
43(5)
Box-and-whisker plots
43(3)
Stem-and-leaf displays
46(2)
Statistical Relationships
48(3)
Case Study: Using Descriptive Statistics for the Malcolm Baldrige National Quality Award
51(3)
Summary
54(1)
Questions and Problems
54(3)
Random Variables and Probability Distributions
57(40)
Basic Concepts
58(5)
Probability
58(1)
Random variables
59(1)
Probability distributions
59(3)
Expected value and variance of a random variable
62(1)
Discrete Probability Distributions
63(3)
Bernoulli and binomial distributions
63(2)
Poisson distribution
65(1)
Continuous Probability Distributions
66(12)
Uniform distribution
67(1)
Normal distribution
68(3)
Triangular distribution
71(1)
Exponential distribution
72(2)
Probability distributions in PHStat
74(1)
Other useful distributions
75(3)
Random Sampling from Probability Distributions
78(8)
Random numbers
78(1)
Sampling from probability distributions
79(1)
Sampling from probability distributions in Excel
80(3)
Sampling distributions and sampling error
83(3)
Summary
86(1)
Questions and Problems
87(2)
Appendix: Introduction to Crystal Ball
89(8)
Specifying input information
90(4)
Crystal Ball output
94(3)
Sampling and Statistical Analysis for Decision Making
97(43)
Statistical Sampling
98(5)
Sample design
99(1)
Sampling methods
100(2)
Error in sampling
102(1)
Sampling from finite populations
103(1)
Statistical Analysis of Sample Data
103(1)
Estimation
103(8)
Point estimates
103(2)
Interval estimates
105(1)
Confidence intervals for the mean
106(4)
Confidence intervals for proportions
110(1)
Confidence intervals and sample size
110(1)
Hypothesis Testing
111(14)
Hypothesis formulation
112(1)
Significance level
113(1)
Decision rules
114(2)
Using p-values
116(3)
Two-sample hypothesis tests for means
119(1)
F-test for testing variances
119(3)
Tests for proportions
122(2)
Other hypothesis tests
124(1)
Anova: Testing Differences of Several Means
125(2)
Chi-Square Test for Independence
127(2)
Summary
129(2)
Questions and Problems
131(2)
Appendix: Distribution Fitting
133(7)
Distribution fitting with Crystal Ball
137(3)
Statistical Quality Control
140(27)
The Role of Statistics and Data Analysis in Quality Control
141(1)
Statistical Process Control
142(10)
Control Charts
143(1)
x-and R-charts
144(4)
Analyzing control charts
148(4)
Control Charts for Attributes
152(2)
Statistical Issues in the Design of Control Charts
154(1)
Process Capability Analysis
155(2)
Summary
157(1)
Questions and Problems
158(9)
PART II
Regression
167(36)
Simple Linear Regression
170(3)
Least Squares estimation
170(3)
Measuring Variation about the Regression Line
173(5)
Coefficient of determination and correlation coefficient
176(1)
Standard error of the estimate and confidence bands
176(2)
Regression as Analysis of Variance
178(3)
Assumptions of Regression Analysis
181(1)
Application of Regression Analysis to Investment Risk
182(3)
Multiple Linear Regression
185(3)
Interpreting results from multiple linear regression
187(1)
Building Good Regression Models
188(3)
Using Adjusted R+2 to evaluate fit
188(1)
Correlation and multicollinearity
189(1)
Best subsets regression
190(1)
Regression with Ordinal and Nominal Independent Variables
191(3)
Regression Models with Nonlinear Terms
194(4)
Summary
198(1)
Questions and Problems
199(4)
Forecasting
203(31)
Qualitative and Judgmental Methods
204(5)
Historical analogy
205(1)
The Delphi method
205(1)
Applying the Delphi method
206(2)
Indicators and indexes
208(1)
Statistical Forecasting Models
209(11)
Moving average models
210(2)
Error metrics and forecast accuracy
212(4)
Exponential smoothing models
216(1)
Incorporating trend and seasonality into exponential smoothing models
217(3)
Regression Models
220(3)
Incorporating seasonality in regression models
222(1)
The Practice of Forecasting
223(3)
Summary
226(1)
Questions and Problems
227(2)
Appendix: CB Predictor
229(5)
Selection Models and Risk Analysis
234(42)
Decision Criteria and Selection
235(14)
Decisions involving a single alternative
236(1)
Sensitivity analysis
237(2)
Decisions involving mutually exclusive alternatives
239(2)
Decisions involving nonmutually exclusive alternatives
241(1)
Decisions involving uncertainty
242(7)
Monte Carlo Simulation for Risk Analysis
249(4)
Applications of Monte Carlo Simulation
253(6)
Project management
253(4)
Budget-Constrained product selection
257(2)
Case Study: Simulation and Risk Analysis in New Product Screening at Cinergy Corporation
259(4)
Summary
263(1)
Questions and Problems
263(8)
Appendix: Additional Crystal Ball Options
271(5)
Correlated assumptions
271(1)
Freezing assumptions
271(1)
Overlay charts
272(1)
Trend charts
272(1)
Sensitivity charts
272(4)
Introduction to Optimization
276(39)
Constrained Optimization
278(1)
Types of Optimization Problems
279(5)
Linear optimization example: transportation problem
280(2)
Integer optimization example: project selection
282(1)
Nonlinear optimization example: hotel pricing
283(1)
Spreadsheet Optimization
284(2)
Solving the optimal pricing problem
285(1)
Solving Linear Optimization Models
286(5)
Interpreting Solver reports
288(2)
Difficulties with Solver
290(1)
Solving Integer Optimization Models
291(1)
Solving Nonlinear Optimization Models
292(2)
Risk Analysis of Optimization Results
294(2)
Combining Optimization and Simulation
296(8)
A portfolio allocation model
296(1)
Using OptQuest
297(6)
Interpreting results
303(1)
Adding a requirement
303(1)
Summary
304(1)
Questions and Problems
304(11)
APPENDIX315(10)
Table A.1 The Standardized Normal Distribution
316(1)
Table A.2 The Cumulative Standard Normal Distribution
317(2)
Table A.3 Critical Values of t
319(2)
Table A.4 Critical Values of F
321(4)
Index325

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