The Essentials of Statistics: A Tool for Social Research
The Essentials of Statistics: A Tool for Social Research
4th Edition
ISBN: 9781305802698
Author: Joseph F. Healey
Publisher: Cengage Learning US
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Chapter 1, Problem 1.5P

Following are brief descriptions of how researchers measured a variable. For each situation, determine the level of measurement of the variable.

a. Race or ethnicity. Check all that apply:

__________ Black

__________ White

__________ Hispanic

__________ Asian or Pacific Islander

__________ Native American

__________ Other (Please specify: __________)

b. Honesty. Subjects were observed as they passed by a spot where an apparently lost wallet was lying. The wallet contained money and complete identification. Subjects were classified into one of the following categories:

__________ Returned the wallet with money

__________ Returned the wallet but kept the money

__________ Did not return the wallet

c. Social class. What was your family situation when you were 16 years old?

__________ Very well off compared to other families

__________ About average

__________ Not so well off

d. Education. How many years of schooling have

you completed? __________

e. Racial integration on campus. Students were observed during lunchtime at the cafeteria for a month. The number of students sitting with students of other races was counted for each meal period.

f. Number of children. How many children have

you ever had? __________

g. Student seating patterns in classrooms. On the first day of class, instructors noted where each student sat. Seating patterns were remeasured every two weeks until the end of the semester. Each student was classified as:

__________ Same seat as at last measurement

__________ Adjacent seat

__________ Different seat, not adjacent

__________ Absent

h. Physicians per capita. The number of physicians was counted in each of 50 cities. The researchers used population data to compute the number of physicians per capita.

i. Physical attractiveness. A panel of ten judges rated each of 50 photos of a mixed-race sample of males and females for physical attractiveness on a scale from 0 to 20, with 20 being the highest score.

j. Number of accidents. The number of traffic accidents per year for each of 20 intersections was recorded. Also, each accident was rated as:

__________ Minor damage, no injuries

__________ Moderate damage, personal injury requiring hospitalization

__________ Severe damage and injury

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(c) Because logistic regression predicts probabilities of outcomes, observations used to build a logistic regression model need not be independent. A. false: all observations must be independent B. true C. false: only observations with the same outcome need to be independent I ANSWERED: A. false: all observations must be independent.  (This was marked wrong but I have no idea why. Isn't this a basic assumption of logistic regression)
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Spam filters are built on principles similar to those used in logistic regression. We fit a probability that each message is spam or not spam. We have several variables for each email. Here are a few: to_multiple=1 if there are multiple recipients, winner=1 if the word 'winner' appears in the subject line, format=1 if the email is poorly formatted, re_subj=1 if "re" appears in the subject line. A logistic model was fit to a dataset with the following output:   Estimate SE Z Pr(>|Z|) (Intercept) -0.8161 0.086 -9.4895 0 to_multiple -2.5651 0.3052 -8.4047 0 winner 1.5801 0.3156 5.0067 0 format -0.1528 0.1136 -1.3451 0.1786 re_subj -2.8401 0.363 -7.824 0 (a) Write down the model using the coefficients from the model fit.log_odds(spam) = -0.8161 + -2.5651 + to_multiple  + 1.5801 winner + -0.1528 format + -2.8401 re_subj(b) Suppose we have an observation where to_multiple=0, winner=1, format=0, and re_subj=0. What is the predicted probability that this message is spam?…
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