Test Bank – Statistics for Managers Using Microsoft Excel, 9th edition by David M. Levine, David F. Stephan, Kathryn A. Szabat

Description

Test Bank – Statistics for Managers Using Microsoft Excel, 9th edition by David M. Levine, David F. Stephan, Kathryn A. Szabat

Table of Contents

1. Defining and Collecting Data

• 1.1 Defining Variables
• 1.2 Collecting Data
• 1.3 Types of Sampling Methods
• 1.4 Data Cleaning
• 1.5 Other Data Preprocessing Tasks
• 1.6 Types of Survey Errors

2. Organizing and Visualizing Variables

• 2.1 Organizing Categorical Variables
• 2.2 Organizing Numerical Variables
• 2.3 Visualizing Categorical Variables
• 2.4 Visualizing Numerical Variables
• 2.5 Visualizing Two Numerical Variables
• 2.6 Organizing a Mix of Variables
• 2.7 Visualizing a Mix of Variables
• 2.8 Filtering and Querying Data
• 2.9 Pitfalls in Organizing and Visualizing Variables

3. Numerical Descriptive Measures

• 3.1 Measures of Central Tendency
• 3.2 Measures of Variation and Shape
• 3.3 Exploring Numerical Variables
• 3.4 Numerical Descriptive Measures for a Population
• 3.5 The Covariance and the Coefficient of Correlation
• 3.6 Descriptive Statistics: Pitfalls and Ethical Issues

4. Basic Probability

• 4.1 Basic Probability Concepts
• 4.2 Conditional Probability
• 4.3 Ethical Issues and Probability
• 4.4 Bayes’ Theorem
• 4.5 Counting Rules

5. Discrete Probability Distributions

• 5.1 The Probability Distribution for a Discrete Variable
• 5.2 Binomial Distribution
• 5.3 Poisson Distribution
• 5.4 Covariance of a Probability Distribution and Its Application in Finance
• 5.5 Hypergeometric Distribution

6. The Normal Distribution and Other Continuous Distributions

• 6.1 Continuous Probability Distributions
• 6.2 The Normal Distribution
• 6.3 Evaluating Normality
• 6.4 The Uniform Distribution
• 6.5 The Exponential Distribution
• 6.6 The Normal Approximation to the Binomial Distribution

7. Sampling Distributions

• 7.1 Sampling Distributions
• 7.2 Sampling Distribution of the Mean
• 7.3 Sampling Distribution of the Proportion
• 7.4 Sampling from Finite Populations

8. Confidence Interval Estimation

• 8.1 Confidence Interval Estimate for the Mean (σ Known)
• 8.2 Confidence Interval Estimate for the Mean (σ Unknown)
• 8.3 Confidence Interval Estimate for the Proportion
• 8.4 Determining Sample Size
• 8.5 Confidence Interval Estimation and Ethical Issues
• 8.6 Application of Confidence Interval Estimation in Auditing
• 8.7 Estimation and Sample Size Determination for Finite Populations
• 8.8 Bootstrapping

9. Fundamentals of Hypothesis Testing: One-Sample Tests

• 9.1 Fundamentals of Hypothesis Testing
• 9.2 t Test of Hypothesis for the Mean (σ Unknown)
• 9.3 One-Tail Tests
• 9.4 Z Test of Hypothesis for the Proportion
• 9.5 Potential Hypothesis-Testing Pitfalls and Ethical Issues
• 9.6 Power of the Test

10. Two-Sample Tests

• 10.1 Comparing the Means of Two Independent Populations
• 10.2 Comparing the Means of Two Related Populations
• 10.3 Comparing the Proportions of Two Independent Populations
• 10.4 F Test for the Ratio of Two Variances
• 10.5 Effect Size

11. Analysis of Variance

• 11.1 One-Way ANOVA
• 11.2 Two-Way ANOVA
• 11.3 The Randomized Block Design
• 11.4 Fixed Effects, Random Effects, and Mixed Effects Models

12. Chi-Square and Nonparametric Tests

• 12.1 Chi-Square Test for the Difference Between Two Proportions
• 12.2 Chi-Square Test for Differences Among More Than Two Proportions
• 12.3 Chi-Square Test of Independence
• 12.4 Wilcoxon Rank Sum Test for Two Independent Populations
• 12.5 Kruskal-Wallis Rank Test for the One-Way ANOVA
• 12.6 McNemar Test for the Difference Between Two Proportions (Related Samples)
• 12.7 Chi-Square Test for the Variance or Standard Deviation
• 12.8 Wilcoxon Signed Ranks Test for Two Related Populations

13. Simple Linear Regression

• 13.1 Simple Linear Regression Models
• 13.2 Determining the Simple Linear Regression Equation
• 13.3 Measures of Variation
• 13.4 Assumptions of Regression
• 13.5 Residual Analysis
• 13.6 Measuring Autocorrelation: The Durbin-Watson Statistic
• 13.7 Inferences About the Slope and Correlation Coefficient
• 13.8 Estimation of Mean Values and Prediction of Individual Values
• 13.9 Potential Pitfalls in Regression

14. Introduction to Multiple Regression

• 14.1 Developing a Multiple Regression Model
• 14.2 Evaluating Multiple Regression Models
• 14.3 Multiple Regression Residual Analysis
• 14.4 Inferences About the Population Regression Coefficients
• 14.5 Testing Portions of the Multiple Regression Model
• 14.6 Using Dummy Variables and Interaction Terms
• 14.7 Logistic Regression
• 14.8 Cross-Validation

15. Multiple Regression Model Building

• 15.1 The Quadratic Regression Model
• 15.2 Using Transformations in Regression Models
• 15.3 Collinearity
• 15.4 Model Building
• 15.5 Pitfalls in Multiple Regression and Ethical Issues

16. Time-Series Forecasting

• 16.1 Time-Series Component Factors
• 16.2 Smoothing an Annual Time Series
• 16.3 Least-Squares Trend Fitting and Forecasting
• 16.4 Autoregressive Modeling for Trend Fitting and Forecasting
• 16.5 Choosing an Appropriate Forecasting Model
• 16.6 Time-Series Forecasting of Seasonal Data
• 16.7 Index Numbers

17. Business Analytics

• 17.1 Business Analytics Overview
• 17.2 Descriptive Analytics
• 17.3 Decision Trees
• 17.4 Clustering
• 17.5 Association Analysis
• 17.6 Text Analytics
• 17.7 Prescriptive Analytics

18. Getting Ready to Analyze Data in the Future

• 18.1 Analyzing Numerical Variables
• 18.2 Analyzing Categorical Variables

19. Statistical Applications in Quality Management (online)

• 19.1 The Theory of Control Charts
• 19.2 Control Chart for the Proportion: The p Chart
• 19.3 The Red Bead Experiment: Understanding Process Variability
• 19.4 Control Chart for an Area of Opportunity: The c Chart
• 19.5 Control Charts for the Range and the Mean
• 19.6 Process Capability
• 19.7 Total Quality Management
• 19.8 Six Sigma

20. Decision Making

• 20.1 Payoff Tables and Decision Trees
• 20.2 Criteria for Decision Making
• 20.3 Decision Making with Sample Information
• 20.4 Utility