Business Statistics Preliminary Edition, Vol. I

by Sharpe, Norean R.; De Veaux, Richard D.; Velleman, Paul F.

• ISBN13: 9780321577726
• ISBN10: 0321577728
• Edition: 1st
• Format: Paperback
• Copyright: 2009-01-01
• Publisher: Addison Wesley

Summary

Professors Norean Sharpe (Babson College), Dick De Veaux (Williams College), and Paul Velleman (Cornell University) have teamed up to provide an innovative new textbook for the undergraduate introductory business statistics course. These authors have taught at the finest business schools and draw on their consulting experience at leading companies to show students how statistical thinking is vital to modern decision making. Managers make better business decisions when they understand statistics, and Business Statistics gives students the statistical tools and understanding to take them from the classroom to the boardroom. Hundreds of examples are based on current events and timely business topics. Short, accessible chapters allow for flexible coverage of important topics, while the conversational writing style maintains student interest and improves understanding. Business Statistics includes Guided Examples that feature the authors' signature Plan/Do/Report problem-solving method. Each worked example shows students how to clearly define the business decision to be made and plan which method to use, do the calculations and make the graphical displays, and finally report their findings, often in the form of a business memo. Every chapter reminds students of What Can Go Wrong and teaches them how to avoid making common statistical mistakes. Volume II contains chapters 16 24 of the main text.

Table of Contents

Contains chapters 1-15 of the main text

Exploring and Collecting Data

Statistics and Variation

Data

What Are Data?

Variable Types

Where, How, and When

Surveys and Sampling

Three Ideas of Sampling

A Census-Does it Make Sense?

Populations and Parameters

Simple Random Sample (SRS)

Other Sample Designs

Defining the Population

The Valid Survey

Displaying and Describing Categorical Data

The Three Rules of Data Analysis

Frequency Tables

Charts

Contingency Tables

Randomness and Probability

Random Phenomena and Probability

The Non-existent Law of Averages

Different Types of Probability

Probability Rules

Joint Probability and Contingency Tables

Conditional Probability

Constructing Contingency Tables

Displaying and Describing Quantitative Data

Displaying Distributions

Shape

Center

Spread of the Distribution

Shape, Center, and Spread-A Summary

Five-Number Summary and Boxplots

Comparing Groups

Identifying Outliers

Standardizing

Time Series Plots

Transforming Skewed Data

Understanding Data and Distributions

Scatterplots, Association, and Correlation

Looking at Scatterplots

Assigning Roles to Variables in Scatterplots

Understanding Correlation

Straightening Scatterplots

Lurking Variables and Causation

Linear Regression

The Linear Model

Correlation and the Line

Regression to the Mean

Checking the Model

Learning More from the Residuals

Variation in the Model and R2

Reality Check: Is the Regression Reasonable?

Sampling Distributions and the Normal Model

Modeling the Distribution of Sample Proportions

Simulation

The Normal Distribution

Practice with Normal Distribution Calculations

The Sampling Distribution for Proportions

Assumptions and Conditions

The Central Limit Theorem-The Fundamental Theorem of Statistics

The Sampling Distribution of the Mean

Sample Size-Diminishing Returns

How Sampling Distribution Models Work

Confidence Intervals for Proportions

A Confidence Interval

Margin of Error: Certainty vs. Precision

Critical Values

Assumptions and Conditions

A Confidence Interval for Small Samples

Choosing Sample Size

Testing Hypotheses about Proportions

Hypotheses

A Trial as a Hypothesis Test

P-values

The Reasoning of Hypothesis Testing

Alternative Hypotheses

Alpha Levels and Significance

Critical Values

Confidence Intervals and Hypothesis Tests

Two Types of Errors

Power

Confidence Intervals and Hypothesis Tests for Means

The Sampling Distribution for the Mean

A Confidence Interval for Means

Assumptions and Conditions

Cautions About Interpreting Confidence Intervals

One-Sample t-Test

Sample Size

Degrees of Freedom-Why (n-1)?

Comparing Two Means

Testing Differences Between Two Means

The Two-Sample t-test

Assumptions and Conditions

A Confidence Interval for the Difference Between Two Means

The Pooled t-test

Tukey's Quick Test

Paired Samples and Blocks

Paired Data

Assumptions and Conditions

The Paired t-Test

How the Paired t-Test Works

Inference for Counts: Chi-Square Tests

Goodness of Fit Tests

Interpreting Chi-square Values

Examining the Residuals

The Chi-Square Test of Homogeneity

Comparing Two Proportions

Chi-Square Test of Independence *Indicates an optional topic

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