Solution Manual For Essentials of Modern Business Statistics with Microsoft Office Excel 7th Edition by David Anderson, Dennis Sweeney, Thomas Williams, Jeffrey Camm, James Cochran ISBN 1337298298 9798214343013

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Product details:

ISBN 10: 1337298298

ISBN 13: 979-8214343013

Author: David Anderson, Dennis Sweeney, Thomas Williams, Jeffrey Camm, James Cochran

Essentials of Modern Business Statistics with Microsoft Office Excel 7th Edition: Discover an accessible introduction to business statistics as ESSENTIALS OF MODERN BUSINESS STATISTICS, 7E balances a conceptual understanding of statistics with real-world applications of statistical methodology. The book integrates Microsoft Excel 2016, providing step-by-step instructions and screen captures to help you master the latest Excel tools. Extremely reader-friendly, this edition includes numerous tools to maximize your course success, including Self-Test Exercises, margin annotations, insightful Notes and Comments, and real-world Methods and Applications exercises. Eleven new Case Problems, as well as new Statistics in Practice applications and real data examples and exercises, give you opportunities to put what you learn into practice. Additional learning resources, including MindTap and CengageNOW for online homework assistance and a complete support website, provide everything to acquire Excel 2016 skills and an understanding of business statistics.

 

Essentials of Modern Business Statistics with Microsoft Office Excel 7th Edition Table of contents:

Chapter 1. Data and Statistics
1.1. Applications in Business and Economics
1.2. Data
1.3. Data Sources
1.4. Descriptive Statistics
1.5. Statistical Inference
1.6. Statistical Analysis Using Microsoft Excel
1.7. Analytics
1.8. Big Data and Data Mining
1.9. Ethical Guidelines for Statistical Practice

Chapter 2. Descriptive Statistics: Tabular and Graphical Displays
2.1. Summarizing Data for a Categorical Variable
2.2. Summarizing Data for a Quantitative Variable
2.3. Summarizing Data for Two Variables Using Tables
2.4. Summarizing Data for Two Variables Using Graphical Displays
2.5. Data Visualization: Best Practices

Chapter 3. Descriptive Statistics: Numerical Measures
3.1. Measures of Location
3.2. Measures of Variability
3.3. Measures of Distribution Shape, Relative Location, and Detecting Outliers
3.4. Five-Number Summaries and Box Plots
3.5. Measures of Association between Two Variables
3.6. Data Dashboards: Adding Numerical Measures

Chapter 4. Introduction to Probability
4.1. Experiments, Counting Rules, and Assigning Probabilities
4.2. Events and Their Probabilities
4.3. Some Basic Relationships of Probability
4.4. Conditional Probability
4.5. Bayes’ Theorem

Chapter 5. Discrete Probability Distributions
5.1. Random Variables
5.2. Developing Discrete Probability Distributions
5.3. Expected Value and Variance
5.4. Bivariate Distributions, Covariance, and Financial Portfolios
5.5. Binomial Probability Distribution
5.6. Poisson Probability Distribution
5.7. Hypergeometric Probability Distribution

Chapter 6. Continuous Probability Distributions
6.1. Uniform Probability Distribution
6.2. Normal Probability Distribution
6.3. Exponential Probability Distribution

Chapter 7. Sampling and Sampling Distributions
7.1. The Electronics Associates Sampling Problem
7.2. Selecting a Sample
7.3. Point Estimation
7.4. Introduction to Sampling Distributions
7.5. Sampling Distribution of x̄
7.6. Sampling Distribution of p̄
7.7. Other Sampling Methods
7.8. Practical Advice: Big Data and Errors in Sampling

Chapter 8. Interval Estimation
8.1. Population Mean: σ Known
8.2. Population Mean: σ Unknown
8.3. Determining the Sample Size
8.4. Population Proportion
8.5. Practical Advice: Big Data and Interval Estimation

Chapter 9. Hypothesis Tests
9.1. Developing Null and Alternative Hypotheses
9.2. Type I and Type II Errors
9.3. Population Mean: σ Known
9.4. Population Mean: σ Unknown
9.5. Population Proportion
9.6. Practical Advice: Big Data and Hypothesis Testing

Chapter 10. Inference About Means and Proportions with Two Populations
10.1. Inferences About the Difference Between Two Population Means: σ₁ and σ₂ Known
10.2. Inferences About the Difference Between Two Population Means: σ₁ and σ₂ Unknown
10.3. Inferences About the Difference Between Two Population Means: Matched Samples
10.4. Inferences About the Difference Between Two Population Proportions

Chapter 11. Inferences About Population Variances
11.1. Inferences About a Population Variance
11.2. Inferences About Two Population Variances

Chapter 12. Tests of Goodness of Fit, Independence, and Multiple Proportions
12.1. Goodness of Fit Test
12.2. Test of Independence
12.3. Testing for Equality of Three or More Population Proportions

Chapter 13. Experimental Design and Analysis of Variance
13.1. An Introduction to Experimental Design
13.2. Analysis of Variance and the Completely Randomized Design
13.3. Multiple Comparison Procedures
13.4. Randomized Block Design
13.5. Factorial Experiment

Chapter 14. Simple Linear Regression
14.1. Simple Linear Regression Model
14.2. Least Squares Method
14.3. Coefficient of Determination
14.4. Model Assumptions
14.5. Testing for Significance
14.6. Using the Estimated Regression Equation for Estimation and Prediction

Chapter 15. Multiple Regression
15.1. Multiple Regression Model
15.2. Least Squares Method
15.3. Multiple Coefficient of Determination
15.4. Model Assumptions
15.5. Testing for Significance
15.6. Using the Estimated Regression Equation for Estimation and Prediction
15.7. Categorical Independent Variables
15.8. Residual Analysis

 

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