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# Doing statistics for Business with Excel

Data, Inferences, and Decision Making

896 pages, parution le 15/07/1999

## Résumé

This text teaches readers the basic techniques for analyzing data to make business decisions. Each chapter of the book begins with a "story". The story tells of a business problem that needs to be solved and describes the data that were collected to help solve the problem. The exercises which are presented in the chapter are tied to that story and are threaded throughout the chapter. The reader is then left to finish the analysis for the story problem. Sample output from Excel, as well as keystroke and menu instructions are featured throughout the book.

GETTING STARTED
CHAPTER 1 INTRODUCTION: THE ROLE OF
STATISTICAL THINKING IN MANAGEMENT
1.1 Chapter Objectives
1.2 Dispelling the Myths about Statistics
1.3 What Managers Should Know about Statistics
for Management
1.5 Situations That Call for Statistical Thinking
Discovery Exercise 1.1: Starting to Think Statistically
1.6 Key Components of Statistical Thinking
1.7 Organization of this Book
Chapter 1 Summary
Chapter 1 Exercises
CHAPTER 2 THE LANGUAGE OF STATISTICS
2.1 Chapter Objectives
2.2 The Difference Between the Population and a Sample of the Population
Discovery Exercise 2.1: Introducation to
Sampling and Variability
2.3 The Difference Between a Parameter and a Statistic
2.4 Factors That Influence Sample Size:
Some Sampling and Sample Size Considerations
2.5 Selecting the Sample
Discovery Exercise 2.2: Introduction to Sampling
2.6 Types of Data
2.7 The Difference Between Descriptive
Statistics and Inferential Statistics
2.8 Basic Summation Notation
2.9 Selecting a Sample in Excel
Chapter 2 Summary
Chapter 2 Exercises
EXPLORATORY DATA ANALYSIS
CHAPTER 3 GRAPHICAL DISPLAYS OF DATA
3.1 Chapter Objectives
3.2 Organizing Data
3.3 Graphical Displays of Data
3.4 Describing and Comparing Data
Variability
3.5 Creating Graphical Displays Using
Excel
Chapter 3 Summary
Chapter 3 Exercises
CHAPTER 4 NUMERICAL DESCRIPTORS OF DATA
4.1 Chapter Objectives
4.2 Describing Data Numerically
4.3 Measures of Central Tendency
Discovery Exercise 4.1: The Trimmed Mean
Discovery Exercise 4.2: Investigating
Variability
4.4 Measures of Dispersion or Spread
4.5 Measures of Relative Standing
4.6 Numerical Descriptors in Excel
Chapter 4 Summary
Chapter 4 Exercises
CHAPTER 5 ANALYZING BIVARIATE DATA
5.1 Chapter Objectives
5.2 Qualitative Bivariate Data
5.3 Quantitative Bivariate Data
Discovery Exercise 5.1: Discovering Relationships
5.4 Bivariate Data in Excel
Chapter 5 Summary
Chapter 5 Exercises
President--Exploratory Data Analysis
Really Doing It: What's on the Road?
Who's Driving It?: Exploratory Data Analysis
THE KEYS THAT UNLOCK THE DOOR TO INFERENTIAL STATISTICS
CHAPTER 6 PROBABILITY
6.1 Chapter Objectives
6.2 Basic Rules of Probability
6.3 Random Variables
6.4 The Binomial Probability Distribution
Discovery Exercise 6.1: Exploring the
Binomial Distribution
6.5 Continuous Random Variables
6.6 The Normal Distribution
6.7 Using Excel to Generate Probability
Distributions
Chapter 6 Summary
Chapter 6 Exercises
CHAPTER 7 SAMPLING DISTRIBUTIONS AND
CONFIDENCE INTERVALS
7.1 Chapter Objectives
7.2 Motivation for Point Estimators
7.3 Common Point Estimators
7.4 Desirable Properties of Point Estimators
7.5 Distribution of the Sample Mean, X
7.6 The Central Limit Theorem--A More
Detailed Look
Discovery Exercise 7.1: The Central
Limit Theorem in Action
7.7 Drawing Inferences by Using theCentral
Limit Theorem
7.8 Large-Sample Confidence Intervals for
the Mean
Discovery Exercise 7.2: Exploring
Confidence Intervals for Mu
7.9 Distribution of the Sample Mean: Small Sample and Unknown Sigma
7.10 Small-Sample Confidence Intervals for
the Mean
7.11 Confidence Intervals for Qualitative
Data
7.12 Sample Size Calculations
7.13 Using Excel to Find Confidence
Intervals
Chapter 7 Summary
Chapter 7 Exercises
INFERENTIAL STATISTICS
CHAPTER 8 HYPOTHESIS TESTING: AN INTRODUCTION
8.1 Chapter Objectives
8.2 What Is a Hypothesis Test?
8.3 Designing Hypotheses to Be Tested--An Overview
8.4 The Pieces of a Hypothesis Test
Discovery Exercise 8.1: Formulating Hypotheses
8.5 Two-Tail Tests of the Mean: Large Sample
Discovery Exercise 8.2: Exploring the
Impact of Varying the Value of Alpha
8.6 Which Theory Should Go into the Null Hypothesis?
8.7 One-Tail Tests of the Mean: Large Sample
8.8 What Error Could You Be Making?
8.9 Hypothesis Testing in Excel
Chapter 8 Summary
Chapter 8 Exercises
CHAPTER 9 INFERENCES: ONE POPULATION
9.1 Chapter Objectives
9.2 Hypothesis Test of the Mean: Small Sample
9.3 (X)^(2) Test of a Single Variance
9.4 Test of a Single Proportion
Discovery Exercise 9.1: Exploring the
Connection Between Confidence Intervals and Hypothesis Testing
9.5 Connection Between Hypothesis Testing
and Confidence Intervals
9.6 Hypothesis Testing in Excel
Chapter 9 Summary
Chapter 9 Exercises
CHAPTER 10 COMPARING TWO POPULATIONS
10.1 Chapter Objectives
10.2 Collecting Data from Two Populations
10.3 Hypothesis Test of the Difference in
Two Population Means--Overview
10.4 Large-Sample Tests of the Difference
in Two Population Means
10.5 Small-Sample Tests of the Difference
in Two Population Means
10.6 Summary of Tests of the Difference in
Two Population Means--Independent Samples
Discovery Exercise 10.1: Introduction to
Experimental Design
10.7 Test of Two Population
Means--Dependent Samples
10.8 Hypothesis Test for the Difference in Two Population Proportions
10.9 Hypothesis Test of the Difference in
Two Population Variances
10.10 Two Population Hypothesis Tests in Excel
Chapter 10 Summary
Chapter 10 Exercises
Really Doing It: Who Spends Money--What
Inferential Statistics
MODEL BUILDING
CHAPTER 11 REGRESSION ANALYSIS
11.1 Chapter Objectives
11.2 The Simple Linear Regression Model
Regression Model
11.4 Prediction and Confidence Intervals
11.5 Correlation Analysis
11.6 Regression Assumptions and Residual Analysis
11.7 Simple Linear Regression in Excel
Chapter 11 Summary
Chapter 11 Exercises
CHAPTER 12 MULTIPLE REGRESSION MODELS
12.1 Chapter Objectives
12.2 The Multiple Regression Model
12.3 Assessing the Multiple Regression
Model
Discovery Exercise 12.1: Finding the
Best Model
12.4 Building a Multiple Regression Model
Chapter 12 Summary
Chapter 12 Exercises
CHAPTER 13 TIME SERIES AND FORECASTING
13.1 Chapter Objectives
13.2 GettingStarted with Time Series Data
Discovery Exercise 13.1: Looking for Patterns and Trends
13.3 Simple Moving Average Models
13.4 Weighted Moving Averages
13.5 Exponential Smoothing Models
13.6 Regression Models
13.7 Time Series Analysis in Excel
Chapter 13 Summary
Chapter 13 Exercises
CHAPTER 14 EXPERIMENTAL DESIGN AND ANOVA
14.1 Chapter Objectives
14.2 Motivation for Usinga Designed Experiment
14.3 Analysis of Data from One-Way Designs
14.4 Assumptions of ANOVA
14.5 Analysis of Data from Blocked Designs
Discovery Exercise 14.1: The Benefits of Blocking
14.6 Analysis of Data from Two-Way Designs
14.7 Other Types of Experimental Designs
14.8 ANOVA in Excel
Chapter 14 Summary
Chapter 14 Exercises
CHAPTER 15 THE ANALYSIS OF QUALITATIVE DATA
15.1 Chapter Objectives
15.2 Test for Goodness of Fit

## L'auteur - Marilyn K. Pelosi

Dr. Marilyn Pelosi is a Professor of Quantitative Methods at Western New England College in Springfield, MA. Dr. Pelosi, a Rhode Island native graduated from Brown University, where she met her husband. She received her PhD. in Industrial Engineering from the University of Massachusetts, where she met co-author Terry Sandifer. Dr. Pelosi has worked as a statistician for the government in Washington DC., and taught Industrial Engineering at Western New England College before moving to her current position in the School of Business. Dr. Pelosi is known for her enthusiastic teaching style and for using real data from consulting projects in the classroom. Dr. Pelosi has two children and enjoys reading, traveling, and watching movies.

## L'auteur - Theresa Sandifer

Dr. Terry Sandifer is a Professor of Mathematics at Southern Connecticut State University in New Haven. She graduated from Iona College and received her PhD. in Industrial Engineering and Operations Research from the University of Massachusetts, where she met both her husband and Dr. Pelosi. She continued her career teaching Industrial Engineering at Western New England College with Dr. Pelosi. Dr. Sandifer then spend five years working as a statistician for the Kimberly Clark Corporation before returning to teaching. A Brooklyn, NY, native, Dr. Sandifer has two children and enjoys reading, shopping, and playing video games.

## Caractéristiques techniques

 PAPIER Éditeur(s) Wiley Auteur(s) Marilyn K. Pelosi, Theresa Sandifer Parution 15/07/1999 Nb. de pages 896 Format 21,5 x 27,5 Poids 1900g EAN13 9780471122081

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