Statistics for Managers Using Microsoft Excel, 9th edition

  • David M. Levine, 
  • David F. Stephan, 
  • Kathryn A. Szabat

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Statistics for Managers Using Microsoft® Excel® presents statistics in the context of specific business fields to help you develop the Excel knowledge needed in your career.

Published by Pearson (March 15th 2021) - Copyright © 2021

ISBN-13: 9780136880981

Subject: Advanced Statistics

Category: Introduction to Business Statistics

Table of contents

F. First Things First

  • FTF.1 Think Differently About Statistics
  • FTF.2 Business Analytics: The Changing Face of Statistics
  • FTF.3 Starting Point for Learning Statistics
  • FTF.4 Starting Point for Using Software
  • FTF.5 Starting Point for Using Microsoft Excel

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


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