1. Provide a written analysis and detailed description of your results.
Be sure to discuss any assumptions associated with each statistical test as well
as whether the data set contains violations. If the data violates any assumption,
continue with the test and discuss potential outcome concerns in regards to
those violations.
1. (Correlation) Betsy is interested in relating quality of teaching to quality of
research by college professors. She has access to a sample of 50 civil engineering
professors who were teaching at the same university for a 10-year period. Over this
10-year period, the professors were evaluated on a 5-point scale on quality as
instructors and on quality of their courses. Betsy has averaged these ratings to
obtain an overall quality rating as an instructor (rating_1) and the overall quality of
the course (rating_2) for each professor. In addition, Betsy also has the number of
articles that each professor published during this time period (num_pubs) and the
number of times these articles were cited by other authors (cites).
o Conduct a correlational analysis to investigate the relations among the variables.
What are your conclusions?
o Evaluate Betsy’s hypothesis by computing correlations between the two teaching
variables and the two research variables controlling for the work ethic variable.
What effect does partialling out the effects of work ethic have on the relationships?
2. (Bivariate Regression) Peter was interested in determining if the number of hits by
a hammer affected the depth of bury of sheet piling. He conducted an experiment
with 10 sheet piles and drove them into the ground with a hammer for five
minutes. The number of times each sheet pile was hit was recorded (hits). Next,
Peter measured the depth the sheet piles were driven into the ground (depth).
Conduct a linear regression to predict the number of hits required to drive the sheet
pile into the ground. What are your conclusions?
3. (Multiple Regression) Mary conducts a non-experimental study to evaluate what
she refers to as the strength-injury hypothesis. It states that overall body strength in
elderly women determines the number and severity of accidents that cause bodily
injury. If the results of her prediction study support her strength-injury hypothesis,
she plans to conduct an experimental study to assess whether weight training
reduces injuries in elderly women. In the prediction study, Mary collects data from
100 women who range in age from 60 to 75 years old at the time the study begins.
The women initially undergo a number of measures that assess lower- and upperbody
strength. Over the next five years, the women record each time they have an
accident that results in a bodily injury and describe fully the context of the injury.
The data file has 100 cases and scores on six variables, five individual strength
measures (quads, gluts, abdoms, grip, and arms) and the dependent variable.
Conduct a regression analysis to evaluate the importance of the predictors in
predicting bodily injury. What are your conclusions?
4. (Logistic Regression) A traffic engineer interested in safety research wanted to
know the factors that influence road rage. The outcome measure was whether road
rage was present (1 = yes, 2 = no). The predictor variables were as follows: gender,
safety (likelihood of an automobile crash measured out of a 5), experience (degree
of experience measured out of a 10), previous presence of road rage (1 = yes, 2 =
no, 3 = don’t know), self-control (the degree of self-reported self-control measured
out of a 9), and perceived risk (measured out of a 6). Previous research has shown
that gender, safety, and perceived risk predict road rage. Conduct a logistic
regression analysis to determine the probability of road rage occurring given the
predictor variables.