Richard De Veaux - Chapter 1 – Stats Starts Here Section 1.1 1. ...

INSTRUCTOR’S

S

OLUTIONS MANUAL

INTRO STATS

FIFTH EDITION

Richard De Veaux

Paul Velleman

David Bock

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Copyright © 2018 Pearson Education, Inc.Chapter 1 – Stats Starts Here Section 1.1

• Grocery shopping. Discount cards at grocery stores allow the stores to collect information

about the products that the customer purchases, what other products are purchased at the same time, whether or not the customer uses coupons, and the date and time that the products are purchased. This information can be linked to demographic information about the customer that was volunteered when applying for the card, such as the customer’s name, address, sex, age, income level, and other variables. The grocery store chain will use that information to better market their products. This includes everything from printing out coupons at the checkout that are targeted to specific customers to deciding what television, print, or Internet advertisements to use.

• Online shopping. Amazon hopes to gain all sorts of information about customer behavior,

such as how long they spend looking at a page, whether or not they read reviews by other customers, what items they ultimately buy, and what items are bought together. They can then use this information to determine which other products to suggest to customers who buy similar items, to determine which advertisements to run in the margins, and to determine which items are the most popular so these items come up first in a search.

• Parking lots. The owners of the parking garage can advertise about the availability of parking.

They can also communicate with businesses about hours when more spots are available and when they should encourage more business.

• Satellites and global climate change. This rise and fall of temperature and water levels can

help in planning for future problems and guide public policy to protect our safety.Section 1.2

• Super Bowl. When collecting data about the Super Bowl, the games themselves are the Who.
• Nobel laureates. Each year is a case, holding all of the information about that specific year.

Therefore, the year is the Who.

• Health records. The sample is about 5,000 people, and the population is all residents of the

United States of America. The Who is the selected subjects and the What includes medical, dental, and physiological measurements and laboratory test results.

• Facebook. The Who is the 350 million photos. The What might be information about the photos,

for example: file format, file size, time and date when uploaded, people and places tagged, and GPS information. 2 / 4

• Part I Exploring and Understanding Data

Copyright © 2018 Pearson Education, Inc.Section 1.3

• Grade level.

a) If we are, for example, comparing the percentage of first-graders who can tie their own

shoes to the percentage of second-graders who can tie their own shoes, grade-level is treated as categorical. It is just a way to group the students. We would use the same methods if we were comparing boys to girls or brown-eyed kids to blue-eyed kids.

b) If we were studying the relationship between grade-level and height, we would be treating

grade level as quantitative.

• ZIP codes.

a) ZIP codes are categorical in the sense that they correspond to a location. The ZIP code

14850 is a standardized way of referring to Ithaca, NY.

b) ZIP codes generally increase as the location gets further from the east coast of the United

States. For example, one of the ZIP codes for the city of Boston, MA is 02101. Kansas City, MO has a ZIP code of 64101, and Seattle, WA has a ZIP code of 98101.

• Voters. The response is a categorical variable.
• Job hunting. The answer is a categorical variable.
• Medicine. The company is studying a quantitative variable.
• Stress. The researcher is studying a quantitative variable.

Section 1.4

• Voting and elections. Pollsters might consider whether a person voted previously or whether

he or she could name the candidates. Voting previously and knowing the candidates may indicate a greater interest in the election.

• Weather. Meteorologists can use the models to predict the average temperature ten days in

advance and compare their predictions to the actual temperatures.

• The News. Answers will vary.
• The Internet. Answers will vary.
• Gaydar. Who – 40 undergraduate women. What – Whether or not the women could identify

the sexual orientation of men based on a picture. Population of interest – All women. 3 / 4

Chapter 1 Stats Starts Here 3 Copyright © 2018 Pearson Education, Inc.

• Hula-hoops. Who – An unknown number of participants. What – Heart rate, oxygen

consumption, and rating of perceived exertion. Population of interest – All people.

• Bicycle Safety. Who – 2,500 cars. What – Distance from the bicycle to the passing car (in

inches). Population of interest – All cars passing bicyclists.

• Investments. Who – 30 similar companies. What – 401(k) employee participation rates (in

percent). Population of interest – All similar companies.

• Honesty. Who – Workers who buy coffee in an office. What – amount of money contributed to

the collection tray. Population of interest – All people in honor system payment situations.

• Blindness. Who – 24 patients. What – Whether the patient had Stargardt’s disease or dry age-

related macular degeneration, and whether or not the stem cell therapy was effective in treating the condition. Population of interest – All people with these eye conditions.

• Not-so-diet soda. Who – 474 participants. What – whether or not the participant drank two or

more diet sodas per day, waist size at the beginning of the study, and waist size at the end of the study. Population of interest – All people.

• Molten iron. Who – 10 crankshafts at Cleveland Casting. What – The pouring temperature (in

degrees Fahrenheit) of molten iron. Population of interest – All crankshafts at Cleveland Casting.

• Weighing bears. Who – 54 bears. What – Weight, neck size, length (no specified units), and

sex. When – Not specified. Where – Not specified. Why - Since bears are difficult to weigh, the researchers hope to use the relationships between weight, neck size, length, and sex of bears to estimate the weight of bears, given the other, more observable features of the bear.How – Researchers collected data on 54 bears they were able to catch. Variables – There are 4 variables; weight, neck size, and length are quantitative variables, and sex is a categorical variable. No units are specified for the quantitative variables. Concerns – The researchers are (obviously!) only able to collect data from bears they were able to catch. This method is a good one, as long as the researchers believe the bears caught are representative of all bears, in regard to the relationships between weight, neck size, length, and sex.

• Schools. Who – Students. What – Age (probably in years, though perhaps in years and

months), race or ethnicity, number of absences, grade level, reading score, math score, and disabilities/special needs. When – This information must be kept current. Where – Not specified. Why – Keeping this information is a state requirement. How – The information is collected and stored as part of school records. Variables – There are seven variables. Race or ethnicity, grade level, and disabilities/special needs are categorical variables. Number of absences (days), age (years?), reading test score, and math test score are quantitative variables.Concerns – What tests are used to measure reading and math ability, and what are the units of measure for the tests?

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