Business Statistics Preliminary Edition Vol. II

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

• ISBN13: 9780321577733
• ISBN10: 0321577736
• 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 16-24 of the main text

Exploring Relationships Among Variables

Inference for Regression

The Population and the Sample

Assumptions and Conditions

The Standard Error of the Slope

A Test for the Regression Slope

A Hypothesis Test for Correlation

Standard Errors for Predicted Values

Using Confidence and Prediction Intervals

Understanding Residuals

Examining Residuals for Groups

Extrapolation and Prediction

Unusual and Extraordinary Observations

Working with Summary Values

Autocorrelation

Linearity

Transforming (Re-expressing) Data

The Ladder of Powers

Multiple Regression

The Multiple Regression Model

Interpreting Multiple Regression Coefficients

Assumptions and Conditions for the Multiple Regression Model

Guided Example: Housing Prices

Testing the Multiple Regression Model

Relationship between F and R2

The Logistic Regression Model

Building Multiple Regression Models

Indicator Variables

Adjusting for Different Slopes - Interaction Terms

Multiple Regression Diagnostics

Building Regression Models

Guided Example: Roller Coaster Speeds

Colinearity

Time Series Analysis

What is a Time-Series?

Components of a Time Series

Forecasting

Smoothing Models

Measuring Forecast Error

Seasonal Regression Models

Building Models For Decision Making

Probability Models

Expected Value of a Random Variable

Standard Deviation of a Random Variable

Properties of Expected Values and Variances

Continuous Random Variables

Probability Models

Decision Making and Risk

Alternative Decisions

Measuring Risk

Decision Trees

Reversing the Conditioning

Bayes's Rule

Design and Analysis of Experiments and Observational Studies

Observational Studies

Randomized, Comparative Experiments

The Four Principles of Experimental Design

Types of Designs

Blinding and Placebos

Confounding and Lurking Variables

Analyzing a Design in One Factor - The Analysis of Variance

Assumptions and Conditions for ANOVA

Multiple Comparisons

Analysis of Multi Factor Designs

Introduction to Data Mining

Direct Marketing

Data

Goals of Data Mining

Data Mining Myths

Challenges of Data Mining

Data Mining Algorithms

Building a Predictive Model

The Data Mining Process

Indicates an optional topic

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