BUSINESS STATISTICS - BUSINESS STATISTICS LINDA DAWSON BUSINESS STATI...

Complete SOLUTIONS MANUAL FOR

BUSINESS STATISTICS

LINDA DAWSON

BUSINESS STATISTICS

4 TH

EDITION

NOREAN R. SHARPE

RICHARD D. DE VEAUX

PAUL F. VELLEMAN 1 / 4

Copyright © 2019 Pearson Education, Inc.

Table of Contents

Part I Exploring and Collecting Data Chapter 1 Data and Decisions 1-1 Chapter 2 Displaying and Describing Categorical Data 2-1 Chapter 3 Displaying and Describing Quantitative Data 3-1 Chapter 4 Correlation and Linear Regression 4-1

Case Study: Paralyzed Veterans of America 4-49

Part II Modeling with Probability Chapter 5 Randomness and Probability 5-1 Chapter 6 Random Variables and Probability Models 6-1 Chapter 7 The Normal and Other Continuous Distributions 7-1

Part III Gathering Data

Chapter 8 Data Sources: Observational Studies and Surveys 8-1

Chapter 9 Data Sources:Experiments 9-1

Part IV Inference for Decision Making Chapter 10 Sampling Distributions and Confidence Intervals for Proportions 10-1

Case Study: Real Estate Simulation

Chapter 11 Confidence Intervals for Means 11-1 Chapter 12 Testing Hypotheses 12-1 Chapter 13 More about Tests and Intervals 13-1 Chapter 14 Comparing Two Means 14-1

Chapter 15 Inference for Counts: Chi-Square tests 15-1

Brief Case: Loyalty Program 15-27

Part V Models for Decision Making

Chapter 16 Inference for Regression 16-1 Chapter 17 Understanding Residuals 17-1 Chapter 18 Multiple Regression 18-1 2 / 4

Copyright © 2019 Pearson Education, Inc.

Chapter 19 Buidling Multiple Regression Models 19-1 Chapter 20 Time Series Analysis 20-1

Case Study: Health Care Costs 20-33

Part VI Analytics Chapter 21 Introduction to Big Data and Data Mining 21-1 Part VII Online Topics Chapter 22 Quality Control 22-1 Chapter 23 Nonparametric Methods 23-1 Chapter 24 Decision Making and Risk 24-1 Chapter 25 Analysis of Experiments and Observational Studies 25-1

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1-1 Copyright © 2019 Pearson Education, Inc.

Chapter 1 – Data and Decisions

SECTION EXERCISES

SECTION 1.1

• Each row represents a different house that was recently sold. It can be described as a case.

b) There are six quantitative variables in each row plus a house identifier for a total of seven variables.

• Each row represents a different transaction (not customer or book). It can be described as a case.

b) There are six quantitative variables plus two identifiers in each row for a total of eight variables.

SECTION 1.2

• House_ID is an identifier (categorical, not ordinal); Neighborhood is categorical (nominal); Mail_ZIP is

categorical (nominal – ordinal in a sense, but only on a national level); Acres is quantitative (units – acres); Yr_Built is quantitative (units – year); Full_Market_Value is quantitative (units – dollars); Size is quantitative (units – square feet).

b) These data are cross-sectional. Each row corresponds to a house that recently sold so at approximately

the same fixed point in time.

• Transaction ID is an identifier (categorical, nominal, not ordinal); Customer ID is an identifier

(categorical, nominal); Date can be treated as quantitative (how many days since the transaction took place, days since Jan. 1 2009, for example) or categorical (as month, for example); ISBN is an identifier (categorical, nominal); Price is quantitative (units – dollars); Coupon is categorical (nominal); Gift is categorical (nominal); Quantity is quantitative (unit – counts).

b) These data are cross-sectional. Each row corresponds to a transaction at a fixed point in time. However,

the date of the transaction has been recorded so the data could be reconfigured as a time series. It is likely that the store had more sales in that time period so a time series is not appropriate.

SECTION 1.3

• It is not specified whether or not the real estate data of Exercise 1 are obtained from a survey. The data

would not be from an experiment, a data gathering method with specific requirements. Rather, the real estate major’s data set was derived from transactional data (on local home sales). The major concern with drawing conclusions from this data set is that we cannot be sure that the sample is representative of the population of interest (e.g., all recent local home sales or even all recent national home sales). Therefore, we should be cautious about drawing conclusions from these data about the housing market in general.

• The student is using a secondary data source (from the Internet). No information is given about how, when,

where and why these data were collected or if it was the result of a designed experiment. It is also not stated that the sample is representative of companies. There are concerns about using these data for generalizing and drawing conclusions because the data could have been collected for a different purpose (not necessarily for developing a stock investment strategy). Therefore, the student should be cautious about using this type of data to predict performance in the future.

CHAPTER EXERCISES

• The news. Answers will vary.

• The Internet. Answers will vary.

• Survey. The description of the study has to be broken down into its components in order to understand the

study. Who– who or what was actually sampled–college students; What–what is being measured–opinion of electric vehicles: whether there will more electric or gasoline powered vehicles in 2025 and the likelihood of whether they would purchase an electric vehicle in the next 10 years; When–current; Where–your location; Why–automobile manufacturer wants college student opinions; How–how was the study

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