ISE Applied Statistics in Business and Economics

✍ Scribed by David Doane, Lori . Seward

• Publisher: McGraw-Hill
• Year: 2021
• Tongue: English
• Leaves: 867
• Edition: 7
• Category: Library

✦ Synopsis

ISBN: 9781260597646 is the International edition of Applied Statistics in Business and Economics 7th Edition by David Doane, Lori . Seward This ISBN is student textbook only. It does not come with online access code. Access code, if required by an instructor, sold separately at another ISBN. The content of International edition is the same as other formats. Applied Statistics in Business and Economics, 7th edition, provides real meaning to the use of statistics in the real world by using real business situations and real data while appealing to students who want to know the why rather than just the how. The text emphasizes thinking about data, choosing appropriate analytic tools, using computers effectively, and recognizing the limitations of statistics. It motivates student learning through applied current exercises and cases that provide real-world relevance and includes analytics in action, careers, and applications of big data, Artificial Intelligence, and machine learning (including ethical issues). The Doane and Seward authors work as a team, integrating the digital and eBook assets seamlessly. In recognition of a growing interest in analytics training beyond Excel, the textbook now provides an optional introduction to R with illustrations of topics in each chapter. Support for R is further enhanced with Learning Stats modules, tables of R functions, and R-compatible Excel data sets.

✦ Table of Contents

Cover
Applied Statistics in Business and Economics
About the Authors
From the Authors
Acknowledgments
Brief Contents
Contents
Chapter 1: Overview of Statistics
1.1 What is Statistics?
1.2 Why Study Statistics?
1.3 Applying Statistics in Business
1.4 Statistical Challenges
1.5 Critical Thinking
Chapter Summary
Chapter Exercises
Chapter 2: Data Collection
2.1 Variables and Data
2.2 Level of Measurement
2.3 Sampling Concepts
2.4 Sampling Methods
2.5 Data Sources
2.6 Surveys
Chapter Summary
Chapter Exercises
Chapter 3: Describing Data Visually
3.1 Stem-and-Leaf Displays and Dot Plots
3.2 Frequency Distributions and Histograms
3.3 Effective Excel Charts
3.4 Line Charts
3.5 Column and Bar Charts
3.6 Pie Charts
3.7 Scatter Plots
3.8 Tables
3.9 Deceptive Graphs
Chapter Summary
Chapter Exercises
Chapter 4: Descriptive Statistics
4.1 Numerical Description
4.2 Measures of Center
4.3 Measures of Variability
4.4 Standardized Data
4.5 Percentiles, Quartiles, and Box Plots
4.6 Covariance and Correlation
4.7 Grouped Data
4.8 Skewness and Kurtosis
Chapter Summary
Chapter Exercises
Chapter 5: Probability
5.1 Random Experiments
5.2 Probability
5.3 Rules of Probability
5.4 Independent Events
5.5 Contingency Tables
5.6 Tree Diagrams
5.7 Bayes’ Theorem
5.8 Counting Rules
Chapter Summary
Chapter Exercises
Chapter 6: Discrete Probability Distributions
6.1 Discrete Probability Distributions
6.2 Expected Value and Variance
6.3 Uniform Distribution
6.4 Binomial Distribution
6.5 Poisson Distribution
6.6 Hypergeometric Distribution
6.7 Geometric Distribution (Optional)
6.8 Transformations of Random Variables (Optional)
Chapter Summary
Chapter Exercises
Chapter 7: Continuous Probability Distributions
7.1 Continuous Probability Distributions
7.2 Uniform Continuous Distribution
7.3 Normal Distribution
7.4 Standard Normal Distribution
7.5 Normal Approximations
7.6 Exponential Distribution
7.7 Triangular Distribution (Optional)
Chapter Summary
Chapter Exercises
Chapter 8: Sampling Distributions and Estimation
8.1 Sampling and Estimation
8.2 Central Limit Theorem
8.3 Confidence Interval for a Mean (μ) With Known σ
8.4 Confidence Interval for a Mean (μ) With Unknown σ
8.5 Confidence Interval for a Proportion (π)
8.6 Estimating from Finite Populations
8.7 Sample Size Determination for a Mean
8.8 Sample Size Determination for a Proportion
8.9 Confidence Interval for a Population Variance, σ2 (Optional)
Chapter Summary
Chapter Exercises
Chapter 9: One-Sample Hypothesis Tests
9.1 Logic of Hypothesis Testing
9.2 Type I and Type II Errors
9.3 Decision Rules and Critical Values
9.4 Testing a Mean: Known Population Variance
9.5 Testing a Mean: Unknown Population Variance
9.6 Testing a Proportion
9.7 Power Curves and OC Curves (Optional)
9.8 Tests for One Variance (Optional)
Chapter Summary
Chapter Exercises
Chapter 10: Two-Sample Hypothesis Tests
10.1 Two-Sample Tests
10.2 Comparing Two Means: Independent Samples
10.3 Confidence Interval for the Difference of Two Means,
10.4 Comparing Two Means: Paired Samples
10.5 Comparing Two Proportions
10.6 Confidence Interval for the Difference of Two Proportions,
10.7 Comparing Two Variances
Chapter Summary
Chapter Exercises
Chapter 11: Analysis of Variance
11.1 Overview of Anova
11.2 One-Factor Anova (Completely Randomized Model)
11.3 Multiple Comparisons
11.4 Tests for Homogeneity Of Variances
11.5 Two-Factor Anova Without Replication (Randomized Block Model)
11.6 Two-Factor Anova with Replication (Full Factorial Model)
11.7 Higher-Order Anova Models (Optional)
Chapter Summary
Chapter Exercises
Chapter 12: Simple Regression
12.1 Visual Displays and Correlation Analysis
12.2 Simple Regression
12.3 Regression Models
12.4 Ordinary Least Squares Formulas
12.5 Tests for Significance
12.6 Analysis of Variance: Overall Fit
12.7 Confidence and Prediction Intervals for Y
12.8 Residual Tests
12.9 Unusual Observations
12.10 Other Regression Problems (Optional)
12.11 Logistic Regression (Optional)
Chapter Summary
Chapter Exercises
Chapter 13: Multiple Regression
13.1 Multiple Regression
13.2 Assessing Overall Fit
13.3 Predictor Significance
13.4 Confidence Intervals for Y
13.5 Categorical Variables
13.6 Tests for Nonlinearity and Interaction
13.7 Multicollinearity
13.8 Regression Diagnostics
13.9 Other Regression Topics
13.10 Logistic Regression (Optional)
Chapter Summary
Chapter Exercises
Chapter 14: Time-Series Analysis
14.1 Time-Series Components
14.2 Trend Forecasting
14.3 Assessing Fit
14.4 Moving Averages
14.5 Exponential Smoothing
14.6 Seasonality
14.7 Index Numbers
14.8 Forecasting: Final Thoughts
Chapter Summary
Chapter Exercises
Chapter 15: Chi-Square Tests
15.1 Chi-Square Test for Independence
15.2 Chi-Square Tests for Goodness of Fit
15.3 Uniform Goodness-of-Fit Test
15.4 Poisson Goodness-of-Fit Test
15.5 Normal Chi-Square Goodness-of-Fit Test
15.6 ECDF Tests (Optional)
Chapter Summary
Chapter Exercises
Chapter 16: Nonparametric Tests
16.1 Why Use Nonparametric Tests?
16.2 One-Sample Runs Test
16.3 Wilcoxon Signed-Rank Test
16.4 Wilcoxon Rank Sum Test
16.5 Kruskal-Wallis Test for Independent Samples
16.6 Friedman Test for Related Samples
16.7 Spearman Rank Correlation Test
Chapter Summary
Chapter Exercises
Chapter 17: Quality Management
17.1 Quality and Variation
17.2 Pioneers in Quality Management
17.3 Quality Improvement
17.4 Control Charts: Overview
17.5 Control Charts for a Mean
17.6 Control Charts for a Range
17.7 Other Control Charts
17.8 Patterns in Control Charts
17.9 Process Capability
17.10 Additional Quality Topics (Optional)
Chapter Summary
Chapter Exercises
Chapter 18: Simulation
Appendixes
A: Binomial Probabilities
B: Poisson Probabilities
C-1: Standard Normal Areas
C-2: Cumulative Standard Normal Distribution
D: Student’s t Critical Values
E: Chi-Square Critical Values
F: Critical Values of F.10
G: Solutions to Odd-Numbered Exercises
H: Answers to Exam Review Questions
I: Writing and Presenting Reports
J: Statistics in Excel and R
K: Using R and RStudio
Index
Standard Normal Areas
Cumulative Standard Normal Distribution
Student's t Critical Values