Business Statistics in Practice 7th Edition by Bruce Bowerman, Richard O’Connell, Emilly Murphree – Ebook PDF Instant Download/Delivery: 0073521493, 978-0073521497
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ISBN 10: 0073521493
ISBN 13: 978-0073521497
Author: Bruce Bowerman, Richard O’Connell, Emilly Murphree
Business Statistics in Practice, Seventh Edition provides a modern, practical and unique framework for teaching an introductory course in Business Statistics. The textbook employs realistic examples, continuing case studies and a business improvement theme to teach the material. The Seventh Edition features more concise and lucid explanations, an improved topic flow and a sensible use of the best and most compelling examples.
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Business Statistics in Practice 7th Table of contents:
Chapter 1. An Introduction to Business Statistics
1.1. Data
1.2. Data Sources
1.3. Populations and Samples
1.4. Three Case Studies That Illustrate Sampling and Statistical Inference
1.5. Ratio, Interval, Ordinal, and Nominative Scales of Measurement (Optional)
Appendix 1.1. Getting Started with Excel
Appendix 1.2. Getting Started with MegaStat
Appendix 1.3. Getting Started with MINITAB
Chapter 2. Descriptive Statistics: Tabular and Graphical Methods
2.1. Graphically Summarizing Qualitative Data
2.2. Graphically Summarizing Quantitative Data
2.3. Dot Plots
2.4. Stem-and-Leaf Displays
2.5. Contingency Tables (Optional)
2.6. Scatter Plots (Optional)
2.7. Misleading Graphs and Charts (Optional)
Appendix 2.1. Tabular and Graphical Methods Using Excel
Appendix 2.2. Tabular and Graphical Methods Using MegaStat
Appendix 2.3. Tabular and Graphical Methods Using MINITAB
Chapter 3. Descriptive Statistics: Numerical Methods
3.1. Describing Central Tendency
3.2. Measures of Variation
3.3. Percentiles, Quartiles, and Box-and-Whiskers Displays
3.4. Covariance, Correlation, and the Least Squares Line (Optional)
3.5. Weighted Means and Grouped Data (Optional)
3.6. The Geometric Mean (Optional)
Appendix 3.1. Numerical Descriptive Statistics Using Excel
Appendix 3.2. Numerical Descriptive Statistics Using MegaStat
Appendix 3.3. Numerical Descriptive Statistics Using MINITAB
Chapter 4. Probability
4.1. Probability and Sample Spaces
4.2. Probability and Events
4.3. Some Elementary Probability Rules
4.4. Conditional Probability and Independence
4.5. Bayes’ Theorem (Optional)
4.6. Counting Rules (Optional)
Chapter 5. Discrete Random Variables
5.1. Two Types of Random Variables
5.2. Discrete Probability Distributions
5.3. The Binomial Distribution
5.4. The Poisson Distribution (Optional)
5.5. The Hypergeometric Distribution (Optional)
5.6. Joint Distributions and the Covariance (Optional)
Appendix 5.1. Binomial, Poisson, and Hypergeometric Probabilities Using Excel
Appendix 5.2. Binomial, Poisson, and Hypergeometric Probabilities Using MegaStat
Appendix 5.3. Binomial, Poisson, and Hypergeometric Probabilities Using MINITAB
Chapter 6. Continuous Random Variables
6.1. Continuous Probability Distributions
6.2. The Uniform Distribution
6.3. The Normal Probability Distribution
6.4. Approximating the Binomial Distribution by Using the Normal Distribution (Optional)
6.5. The Exponential Distribution (Optional)
6.6. The Normal Probability Plot (Optional)
Appendix 6.1. Normal Distribution Using Excel
Appendix 6.2. Normal Distribution Using MegaStat
Appendix 6.3. Normal Distribution Using MINITAB
Chapter 7. Sampling and Sampling Distributions
7.1. Random Sampling
7.2. The Sampling Distribution of the Sample Mean
7.3. The Sampling Distribution of the Sample Proportion
7.4. Stratified Random, Cluster, and Systematic Sampling (Optional)
7.5. More about Surveys and Errors in Survey Sampling (Optional)
7.6. Derivation of the Mean and the Variance of the Sample Mean (Optional)
Appendix 7.1. Generating Random Numbers Using Excel
Appendix 7.2. Generating Random Numbers Using MegaStat
Appendix 7.3. Generating Random Numbers Using MINITAB
Chapter 8. Confidence Intervals
8.1. z-Based Confidence Intervals for a Population Mean: σ Known
8.2. t-Based Confidence Intervals for a Population Mean: σ Unknown
8.3. Sample Size Determination
8.4. Confidence Intervals for a Population Proportion
8.5. Confidence Intervals for Parameters of Finite Populations (Optional)
Appendix 8.1. Confidence Intervals Using Excel
Appendix 8.2. Confidence Intervals Using MegaStat
Appendix 8.3. Confidence Intervals Using MINITAB
Chapter 9. Hypothesis Testing
9.1. The Null and Alternative Hypotheses and Errors in Hypothesis Testing
9.2. z Tests about a Population Mean: σ Known
9.3. t Tests about a Population Mean: σ Unknown
9.4. z Tests about a Population Proportion
9.5. Type II Error Probabilities and Sample Size Determination (Optional)
9.6. The Chi-Square Distribution
9.7. Statistical Inference for a Population Variance (Optional)
Appendix 9.1. One-Sample Hypothesis Testing Using Excel
Appendix 9.2. One-Sample Hypothesis Testing Using MegaStat
Appendix 9.3. One-Sample Hypothesis Testing Using MINITAB
Chapter 10. Statistical Inferences Based on Two Samples
10.1. Comparing Two Population Means by Using Independent Samples
10.2. Paired Difference Experiments
10.3. Comparing Two Population Proportions by Using Large, Independent Samples
10.4. The F Distribution
10.5. Comparing Two Population Variances by Using Independent Samples
Appendix 10.1. Two-Sample Hypothesis Testing Using Excel
Appendix 10.2. Two-Sample Hypothesis Testing Using MegaStat
Appendix 10.3. Two-Sample Hypothesis Testing Using MINITAB
Chapter 11. Experimental Design and Analysis of Variance
11.1. Basic Concepts of Experimental Design
11.2. One-Way Analysis of Variance
11.3. The Randomized Block Design
11.4. Two-Way Analysis of Variance
Appendix 11.1. Experimental Design and Analysis of Variance Using Excel
Appendix 11.2. Experimental Design and Analysis of Variance Using MegaStat
Appendix 11.3. Experimental Design and Analysis of Variance Using MINITAB
Chapter 12. Chi-Square Tests
12.1. Chi-Square Goodness-of-Fit Tests
12.2. A Chi-Square Test for Independence
Appendix 12.1. Chi-Square Tests Using Excel
Appendix 12.2. Chi-Square Tests Using MegaStat
Appendix 12.3. Chi-Square Tests Using MINITAB
Chapter 13. Simple Linear Regression Analysis
13.1. The Simple Linear Regression Model and the Least Squares Point Estimates
13.2. Model Assumptions and the Standard Error
13.3. Testing the Significance of the Slope and y-Intercept
13.4. Confidence and Prediction Intervals
13.5. Simple Coefficients of Determination and Correlation
13.6. Testing the Significance of the Population Correlation Coefficient (Optional)
13.7. An F-Test for the Model
13.8. Residual Analysis
Appendix 13.1. Simple Linear Regression Analysis Using Excel
Appendix 13.2. Simple Linear Regression Analysis Using MegaStat
Appendix 13.3. Simple Linear Regression Analysis Using MINITAB
Chapter 14. Multiple Regression and Model Building
14.1. The Multiple Regression Model and the Least Squares Point Estimates
14.2. Model Assumptions and the Standard Error
14.3. R² and Adjusted R²
14.4. The Overall F-Test
14.5. Testing the Significance of an Independent Variable
14.6. Confidence and Prediction Intervals
14.7. The Sales Representative Case: Evaluating Employee Performance
14.8. Using Dummy Variables to Model Qualitative Independent Variables
14.9. Using Squared and Interaction Variables
14.10. Model Building and the Effects of Multicollinearity
14.11. Residual Analysis in Multiple Regression
14.12. Logistic Regression
Appendix 14.1. Multiple Regression Analysis Using Excel
Appendix 14.2. Multiple Regression Analysis Using MegaStat
Appendix 14.3. Multiple Regression Analysis Using MINITAB
Appendix A. Statistical Tables
Answers to Most Odd-Numbered Exercises
References
Photo Credits
Subject Index
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Tags: Bruce Bowerman, Richard O’Connell, Emilly Murphree, Business Statistics