Test Bank for Business Statistics in Practice, 6th Edition: Bowerman

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Product details:

• ISBN-10 ‏ : ‎ 0073401838
• ISBN-13 ‏ : ‎ 978-0073401836
• Author: Bruce L. Bowerman; Richard T. O’Connell

The new edition of Business Statistics in Practice provides a modern, practical, and unique framework for teaching the first course in business statistics. This framework features case study and example-driven discussions of all basic business statistics topics. In addition, the authors have rewritten many of the discussions in this edition and have explained concepts more simply from first principles. The only prerequisite for this text is high school algebra.

Table of contents:

An Introduction to Business Statistics

Data

3(2)

Data Sources

5(2)

Populations and Samples

7(1)

Three Case Studies That Illustrate Sampling and Statistical Inference

8(6)

Ratio, Interval, Ordinal, and Nominative Scales of Measurement (Optional)

14(4)

Getting Started with Excel

18(5)

Getting Started with MegaStat

23(4)

Getting Started with MINITAB

27(8)

Descriptive Statistics: Tabular and Graphical Methods

Graphically Summarizing Qualitative Data

35(7)

Graphically Summarizing Quantitative Data

42(12)

Dot Plots

54(2)

Stem-and-Leaf Displays

56(5)

Cross-tabulation Tables (Optional)

61(6)

Scatter Plots (Optional)

67(3)

Misleading Graphs and Charts (Optional)

70(10)

Tabular and Graphical Methods Using Excel

80(8)

Tabular and Graphical Methods Using MegaStat

88(4)

Tabular and Graphical Methods Using MINITAB

92(9)

Descriptive Statistics: Numerical Methods

Describing Central Tendency

101(9)

Measures of Variation

110(10)

Percentiles, Quartiles, and Box-and-Whiskers Displays

120(9)

Covariance, Correlation, and the Least Squares Line (Optional)

129(5)

Weighted Means and Grouped Data (Optional)

134(5)

The Geometric Mean (Optional)

139(7)

Numerical Descriptive Statistics Using Excel

146(3)

Numerical Descriptive Statistics Using MegaStat

149(2)

Numerical Descriptive Statistics Using MINITAB

151(4)

Probability

The Concept of Probability

155(2)

Sample Spaces and Events

157(7)

Some Elementary Probability Rules

164(7)

Conditional Probability and Independence

171(11)

Bayes’ Theorem (Optional)

182(3)

Counting Rules (Optional)

185(10)

Discrete Random Variables

Two Types of Random Variables

195(1)

Discrete Probability Distributions

196(11)

The Binomial Distribution

207(10)

The Poisson Distribution (Optional)

217(6)

The Hypergeometric Distribution (Optional)

223(5)

Binomial and Poisson Probabilities Using Excel

228(2)

Binomial and Poisson Probabilities Using MegaStat

230(1)

Binomial and Poisson Probabilities Using MINITAB

231(2)

Continuous Random Variables

Continuous Probability Distributions

233(2)

The Uniform Distribution

235(3)

The Normal Probability Distribution

238(18)

Approximating the Binomial Distribution by Using the Normal Distribution (Optional)

256(4)

The Exponential Distribution (Optional)

260(3)

The Normal Probability Plot (Optional)

263(7)

Normal Distribution Using Excel

270(1)

Normal Distribution Using MegaStat

271(1)

Normal Distribution Using MINITAB

272(3)

Sampling and Sampling Distributions

Random Sampling

275(4)

The Sampling Distribution of the Sample Mean

279(13)

The Sampling Distribution of the Sample Proportion

292(3)

Stratified Random, Cluster, and Systematic Sampling (Optional)

295(2)

More about Surveys and Errors in Survey Sampling (Optional)

297(8)

Generating Random Numbers Using Excel

305(1)

Generating Random Numbers Using MegaStat

306(1)

Generating Random Numbers and Simulating Sampling Distributions Using MINITAB

306(3)

Confidence Intervals

z-Based Confidence Intervals for a Population Mean: & Known

309(9)

t-Based Confidence Intervals for a Population Mean: & Unknown

318(7)

Sample Size Determination

325(4)

Confidence Intervals for a Population Proportion

329(7)

Confidence Intervals for Parameters of Finite Populations (Optional)

336(5)

A Comparison of Confidence Intervals and Tolerance Intervals (Optional)

341(5)

Confidence Intervals Using Excel

346(1)

Confidence Intervals Using MegaStat

347(1)

Confidence Intervals Using MINITAB

348(3)

Hypothesis Testing

The Null and Alternative Hypotheses and Errors in Hypothesis Testing

351(6)

z Tests about a Population Mean: & Known

357(11)

t Tests about a Population Mean: & Unknown

368(5)

z Tests about a Population Proportion

373(5)

Type II Error Probabilities and Sample Size Determination (Optional)

378(6)

The Chi-Square Distribution (Optional)

384(1)

Statistical Inference for a Population Variance (Optional)

385(7)

One-Sample Hypothesis Testing Using Excel

392(1)

One-Sample Hypothesis Testing Using MegaStat

393(1)

One-Sample Hypothesis Testing Using MINITAB

394(3)

Statistical Inferences Based on Two Samples

Comparing Two Population Means by Using Independent Samples: Variances Known

397(6)

Comparing Two Population Means by Using Independent Samples: Variances Unknown

403(8)

Paired Difference Experiments

411(8)

Comparing Two Population Proportions by Using Large, Independent Samples

419(6)

Comparing Two Population Variances by Using Independent Samples

425(11)

Two-Sample Hypothesis Testing Using Excel

436(1)

Two-Sample Hypothesis Testing Using MegaStat

437(2)

Two-Sample Hypothesis Testing Using MINITAB

439(4)

Experimental Design and Analysis of Variance

Basic Concepts of Experimental Design

443(3)

One-Way Analysis of Variance

446(11)

The Randomized Block Design

457(8)

Two-Way Analysis of Variance

465(16)

Experimental Design and Analysis of Variance Using Excel

481(1)

Experimental Design and Analysis of Variance Using MegaStat

482(2)

Experimental Design and Analysis of Variance Using MINITAB

484(5)

Chi-Square Tests

Chi-Square Goodness of Fit Tests

489(9)

A Chi-Square Test for Independence

498(11)

Chi-Square Tests Using Excel

509(2)

Chi-Square Tests Using MegaStat

511(2)

Chi-Square Tests Using MINITAB

513(4)

Simple Linear Regression Analysis

The Simple Linear Regression Model and the Least Squares Point Estimates

517(13)

Model Assumptions and the Standard Error

530(3)

Testing the Significance of the Slope and y-Intercept

533(7)

Confidence and Prediction Intervals

540(6)

Simple Coefficients of Determination and Correlation

546(5)

Testing the Significance of the Population Correlation Coefficient (Optional)

551(1)

An F Test for the Model

552(3)

The QHIC Case

555(2)

Residual Analysis

557(10)

Some Shortcut Formulas (Optional)

567(9)

Simple Linear Regression Analysis Using Excel

576(1)

Simple Linear Regression Analysis Using MegaStat

577(2)

Simple Linear Regression Analysis Using MINITAB

579(2)

Multiple Regression

The Multiple Regression Model and the Least Squares Point Estimates

581(10)

Model Assumptions and the Standard Error

591(2)

R2 and Adjusted R2

593(2)

The Overall F Test

595(2)

Testing the Significance of an Independent Variable

597(4)

Confidence and Prediction Intervals

601(3)

The Sales Territory Performance Case

604(2)

Using Dummy Variables to Model Qualitative Independent Variables

606(12)

The Partial F Test: Testing the Significance of a Portion of a Regression Model

618(3)

Residual Analysis in Multiple Regression

621(7)

Multiple Regression Analysis Using Excel

628(2)

Multiple Regression Analysis Using MegaStat

630(2)

Multiple Regression Analysis Using MINITAB

632(3)

Model Building and Model Diagnostics

The Quadratic Regression Model

635(7)

Interaction

642(6)

Logistic Regression

648(4)

Model Building and the Effects of Multicollinearity

652(13)

Improving the Regression Model I: Diagnosing and Using Information about Outlying and Influential Observations

665(6)

Improving the Regression Model II: Transforming the Dependent and Independent Variables

671(7)

Improving the Regression Model III: The Durbin-Watson Test and Dealing with Autocorrelation

678(10)

Model Building Using Excel

688(2)

Model Building Using MegaStat

690(2)

Model Building Using MINITAB

692(5)

Time Series Forecasting

Time Series Components and Models

697(1)

Time Series Regression: Basic Models

698(6)

Time Series Regression: More Advanced Models (Optional)

704(4)

Multiplicative Decomposition

708(7)

Simple Exponential Smoothing

715(5)

Holt-Winters’ Models

720(9)

Forecast Error Comparisons

729(1)

Index Numbers

730(9)

Time Series Analysis Using Excel

739(1)

Time Series Analysis Using MegaStat

740(2)

Time Series Analysis Using MINITAB

742(3)

Process Improvement Using Control Charts

Quality: Its Meaning and a Historical Perspective

745(4)

Statistical Process Control and Causes of Process Variation

749(2)

Sampling a Process, Rational Subgrouping, and Control Charts

751(5)

X and R Charts

756(16)

Pattern Analysis

772(5)

Comparison of a Process with Specifications: Capability Studies

777(8)

Charts for Fraction Nonconforming

785(6)

Cause-and-Effect and Defect Concentration Diagrams (Optional)

791(8)

Control Charts Using MegaStat

799(1)

Control Charts Using MINITAB

800(4)

Nonparametric Methods

The Sign Test: A Hypothesis Test about the Median

804(4)

The Wilcoxon Rank Sum Test

808(6)

The Wilcoxon Signed Ranks Test

814(4)

Comparing Several Populations Using the Kruskal-Wallis H Test

818(2)

Spearman’s Rank Correlation Coefficient

820(6)

Nonparametric Methods Using MegaStat

826(3)

Nonparametric Methods Using MINITAB

829(4)

Decision Theory

Introduction to Decision Theory

833(6)

Decision Making Using Posterior Probabilities

839(8)

Introduction to Utility Theory

847(5)

Appendix A Statistical Tables

852(25)

Appendix B Properties of the Mean and the Variance of a Random Variable, and the Covariance

877(3)

Appendix C Derivations of the Mean and Variance of x and p

880(2)

Appendix D Answers to Most Odd-Numbered Exercises

882(8)

Appendix E References

890(2)

Photo Credits

892(1)

Index

893

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