Description
This highly accessible text gives students a solid foundation in traditional inferential statistics with the concepts of quality, variability, and process unifying themes. It covers three important features of data-center, distribution shape, and variation-for processes as well as for populations. The book also explores whether a process is statistically stable and show how analysis of an unstable process can easily lead to incorrect, misleading, and costly decisions. It develops control charts and uses them as a statistical tool to track processes. It also covers the techniques for measuring and understanding variation, and details its effects on costs and quality.
Features
- From Data to Decision. Open-ended case studies designed to develop students' problem-solving skills while using statistics to make real- world decisions.
- Marginal Essays. Ninety-eight essays motivate students by presenting them with applications of statistics in business and industry
- Flow Charts. These figures simplify and clearly illustrate the more complicated procedures.
- Chapter-Opening Problems. Show how statistics is used in real situations.
- Flexible Computer Coverage. Includes Minitab displays throughout, plus the authors provide computer projects that use MINITAB® or some other statistical package to show how businesses use computers for problem-solving and analysis.
Table of Contents
1. Introduction To Populations And Processes.
Overview: Statistics, Quality, Variability.
Uses and Abuses of Statistics.
The Nature of Data.
Statistical Experiments and Sampling.
Statistics and Computers.
2. Statistics From Populations And Processes.
Overview.
Summarizing Data.
Pictures of Population Data.
Pictures of Process Data: Run Charts.
Averages.
Variation in Populations and Processes.
Measures of Position (z Scores and Percentiles).
Exploratory Data Analysis.
3. Probability.
Overview.
Fundamentals.
Addition Rule.
Multiplication Rule.
Probability Applied to Runs Charts.
Counting.
4. Probability Distributions.
Overview.
Random Variables.
Mean, Variance, and Expectation.
Binomial Experiments.
Mean and Standard Deviation for the Binomial Distribution.
5. Normal Probability.
Distributions.
Overview.
The Standard Normal Distribution.
Nonstandard Normal Distributions.
Determining Normality of Populations and Processes.
Finding Scores When Given Probabilities.
Normal as Approximation to Binomial.
The Central Limit Theorem.
An Application of the Central Limit Theorem (x-chart when s is known).
6. Estimates And Sample Sizes.
Overview.
Estimates and Sample Sizes of Means.
Estimates and Sample Sizes of Proportions.
Estimates and Sample Sizes of Variances.
7. Testing Hypotheses.
Overview.
Testing a Claim about a Mean.
P-values.
t-test.
Tests of Proportions.
Tests of Variances.
8. Quality Control.
Overview.
Control Chart for P.
Control Chart for R.
Control Chart for X.
Equivalency of Hypothesis Test and Control Chart.
9. Inferences From Two Samples.
Overview.
Comparing Two Variances.
Inferences from Two Means.
Inferences from Two Proportions.
10. Multinomial Experiments And Contingency Tables.
Overview.
Multinomial Experiments.
Contingency Tables.
11. Analysis Of Variance.
Overview.
ANOVA with Equal Sample Sizes.
ANOVA with Unequal Sample Sizes.
Randomized Block ANOVA.
12. Simple Linear Regression And Correlation.
Overview.
Simple Linear Regression and Least Squares.
Variation and Model Assumptions.
Hypothesis Testing and Confidence Intervals for the Slope and Intercept.
A Simulation: Putting It All Together.
Confidence Intervals for the Mean and Prediction Intervals for Estimated Values.
Correlation and Scatter Diagrams.
13. Multiple Regression.
Overview.
Multiple Regression and Least Square.
Variation and Model Assumptions.
Hypothesis Testing and Confidence Intervals for Coefficients.
Two Illustrations of Multiple Regression.
Confidence Intervals for the Mean and Prediction Intervals for Estimated Values.
Multicollinearity, Extrapolation, and Estimability.
14. Time Series.
Overview.
Index Numbers.
Time Series Components.
Decomposition: Trend and Cyclical Components Combined.
Separating the Trend and Cyclical Components.
Separating the Seasonal and Irregular Components.
Seasonal Adjustments and Forecasting.
15. Nonparametric Statistics.
Overview.
Sign Test.
Wilcoxon Signed-Ranks Test for Two Dependent Samples.
Wilcoxon Rank Sum Test for Two Independent Samples.
Kruskal Wallis Test.
Rank Correlation.
Runs Test for Randomness.
16. Decision Analysis.
Overview.
Components of the Decision Problem.
Payoff Tables, Opportunity Loss Tables, Decision Trees.
Decision Making with Probabilities.
Decision Making Without Probabilities.
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