Statistics for Applied Problem Solving and Decision Making

ISBN-10: 0534930840
ISBN-13: 9780534930844
Edition: 1997
List price: $229.95
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Description: Intended for the applied calculus-based, introductory statistics course for business and economics undergraduates and MBAs, this book presents business statistics as a problem-solving process, rather than as a means to achieve a numerical solution.  More...

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Book details

List price: $229.95
Copyright year: 1997
Publisher: Cengage South-Western
Publication date: 3/10/1997
Binding: Hardcover
Pages: 898
Size: 7.75" wide x 9.75" long x 1.25" tall
Weight: 2.794
Language: English

Intended for the applied calculus-based, introductory statistics course for business and economics undergraduates and MBAs, this book presents business statistics as a problem-solving process, rather than as a means to achieve a numerical solution. Applied, data-driven, and copmuter-oriented, it emphasizes the principles of statistics as a foundation for application of statistical methodologies and real-life problems. Curve fitting and regression are introduced early, allowing students to use the computer from the start of the course. Statistical and spreadsheet software help students make the connections between probability models and actual data. The authors include sparing and careful use of calculus at critical points where a simple calculus reference or computation can totally eliminate the perplexing arbitrariness that is seen in 'cookbook' approaches.

Preface
Introduction
Introduction
The Tools of Statistics
A Brief Course Outline
Statistical Relationships: A First Look
Introduction
Fitting the Model y = b0 + b1x
The Sample Correlation Coefficient
Fitting Curvilinear Relationships
Summary
Samples and Sample Variation
Introduction
Methods of Sampling
Summarizing a Sample
Measuring Location and Dispersion (An Introduction)
Summary
Probability Models: An Introduction
Introduction
Discrete Probability Models
Continuous Probability Models
Summary
The Normal Curve
Introduction
The Standard Normal Curve
The Z Transformation
The Central Limit The orem
Summary
Normal Plots
The Binomial Distribution
Introduction
The Binomial Model
Using Normal Curves to Approximate Binomial Probabilities
Summary
The Hypergeometric, Poisson, and Exponential Distributions
Introduction
The Hypergeometric Distribution
The Poisson and Exponential Distributions
Summary
Proof of The orem 7.3.4
Means and Standard Deviations
Introduction
Expected Value
The Population Standard Deviation
Summary
A Proof of Chebyshev?s Inequality (Theorem 8.3.1)
Estimating Parameters
Introduction
The Method of Maximum Likelihood
Estimators
Margin of Error and Sample Size Determination
Summary
Principles of Inference
Introduction
Testing Hypotheses About u (Normal Data)
Confidence Intervals for u
Drawing Inferences About o2
Binomial Data: Inferences About p
Poisson Data: Inferences About l
Summary
Some Common Types of Data
Introduction
Seven Common Experimental Designs
Summary
Comparing Means
Introduction
The Two-sample t Test
Testing H0: u1 = u2 =
= uk: the One-Way Analysis of Variance
The Paired t Test
Analyzing Randomized Block Data
Summary
Statistical Relationships: A Second Look
Introduction
The Simple Linear Model
Analyzing Categorical Data: the X2 Test for Independence
Summary
Multiple Regression
Introduction
Least Squares Estimation
Hypothesis Testing
Model Building
Prediction Intervals and Influential Observations
Summary
Time Series and Index Numbers
Introduction
Fitting a Time Series Model
Smoothing a Time Series
Simple and Composite Indexes
Weighted Composite Index Numbers
Summary
Quality Control
Introduction
X-charts
R-charts and S-charts
Other Control Charts
Specification Limits Versus Control Limits
Acceptance Sampling
Summary
Appendix A Tables
Bibliography
Answers to Odd-Numbered Exercises
Index

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