Siegel - Practical Business Statistics, Student Solutions Manual (e-only)

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Front Matter

Practical Business Statistics

Sixth Edition

Andrew F. Siegel

Department of Information Systems and Operations Management

Department of Finance and Business Economics

Department of Statistics

Michael G. Foster School of Business

University of Washington

AMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK OXFORD PARIS SAN DIEGO SAN FRANCISCO SINGAPORE SYDNEY TOKYO

Academic Press is an imprint of Elsevier

Copyright

Academic Press is an imprint of Elsevier

225 Wyman Street, Waltham, MA 02451, USA

525 B Street, Suite 1800, San Diego, California 92101-4495, USA

84 Theobald's Road, London WC1X 8RR, UK

2012 Andrew F. Siegel. Published by Elsevier, Inc. All rights reserved.

No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher's permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions.

This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein).

Notices

Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary.

Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility.

To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein.

ISBN: 978-0-12-387722-2

For information on all Academic Press publications visit our Web site at www.elsevierdirect.com

Chapter 1. Introduction

Defining the Role of Statistics in Business

Odd Problem Solutions

1. Exercise for student.

3. Exercise for student.

5. Exercise for student.

7. Designing the study (to produce the needed numbers).

9. Exploring the data (looking through the accounting information).

11. Designing the study (while you would like, ultimately, to estimate, the initial phase involves design because no data are yet available).

13. Modeling the data (identifying the model and its parameters before the model is used for estimation and hypothesis testing).

15. Exploring the data (looking at the data).

17. Hypothesis testing (deciding whether or not the defect rate has increased).

19. Modeling the data (developing a system of equations with model parameters).

Chapter 2. Data Structures

Classifying the Various Types of Data Sets

Odd Problem Solutions

1. Exercise for the student.

3. Exercise for the student.

5.

a. Exercise for the student.

b. Exercise for the student.

7. Multivariate analysis could be used to predict one variable (your profit) based on several others (competitors' performance, state of the economy, and time of year).

9. This is a multivariate cross-sectional data set consisting of secondary data.

11.

a. The individual employee is the elementary unit for this data set.

b. Multivariate.

c. Salary and years of experience are quantitative. Gender and education are qualitative.

d. Education is ordinal qualitative because the categories can be put in a meaningful order from least to most education.

e. Cross-sectional.

13.

a. Months are the elementary units.

b. Bivariate.

c. Both of these variables are quantitative.

d. Time-series.

15. Multivariate cross-sectional data. All variables are quantitative. Variables are (a) last year's spending for TV advertising, (b) last year's spending for radio advertising, and (c) last year's spending for newspaper advertising. Elementary units are competitors.

17.

a. Bivariate.

b. Cross-sectional.

19.

a. Qualitative.

b. Ordinal.

21.

a. Models (of cell phones) are elementary units.

b. Multivariate.

c. Cross-sectional.

d. Nominal.

e. Quantitative.

f. Ordinal.

23. Ordinal.

25.

a. Vacuum cleaners are the elementary units.

b. Multivariate.

c. Quantitative: price and weight. Qualitative: quality and type.

d. Quality is ordinal. Type is nominal.

e. Cross-sectional.

27.

a. The elementary units are days.

b. Multivariate.

c. All variables are quantitative.

d. No variables are qualitative.

e. Time-series.

Chapter 3. Histograms

Looking at the Distribution of the Data

Odd Problem Solutions

1. Approximately normal.

3. Skewed (with a long tail towards higher values).

5.

a. 2 contracts.

b. 3 contracts.

c. No, you can only tell that it lost more than 50% of its value but not more than 100%.

d. 24 contracts.

e. Skewed (with a long tail towards high values) with 3 outliers.

7.

a.

b.

c. Shares have appreciated typically by about 10% to 20%.

9.

a.

b. Typically, CREF has invested $0 to $200 million in each firm, with one exceptions around $350 million.

c. The distribution is skewed (with a long tail toward high values).

d. The following table gives the base 10 logarithm of the dollar amounts, so that the first number is log10(2,035,000) = 6.309. Other correct answers are possible, either using base

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