Statistics for Managers Using Microsoft Excel, 9th edition

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

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Overview

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

Table of Contents

  • Preface
  • 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 73
    • 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

Appendices

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