Description
Mini Project #
Name
Names of group members (if applicable)
Contribution of each group member
Section 1. Answers to the specific questions asked
Section 2: R code. Your code must be annotated. No points may be given if a brief
look at the code does not tell us what it is doing.
1. Consider the prostate cancer dataset available on eLearning as prostate cancer.csv.
It consists of data on 97 men with advanced prostate cancer. A description of the
variables is given in Figure 1. We would like to understand how PSA level is related
to the other predictors in the dataset. Note that vesinv is a qualitative variable.
You can treat gleason as a quantitative variable.
Build a “reasonably good” linear model for these data by taking PSA level as the
response variable. Carefully justify all the choices you make in building the model.
Be sure to verify the model assumptions. In case a transformation of response is
necessary, try the natural log transformation. Use the final model to predict the
PSA level for a patient whose quantitative predictors are at the sample means of the
variables and qualitative predictors are at the most frequent category.
1
Statistical Methods for Data Science
Mini Project 5
Consider the prostate cancer dataset available on eLearning. It consists of data on 97 men
with advanced prostate cancer. Following is a description of the variables:
Build a “reasonably good” linear model for these data by taking PSA level as the response variable. Carefully justify all the choices you make in building the model. Be
sure to verify the model assumptions and also to distinguish between quantitative and
qualitative variables. Use the final model to predict the PSA level for a patient whose
predictors are at the sample means of the variables.
Instructions:
• Due date: Thursday, April 20.
• Total points = 25.
• Submit a typed report.
• You can work on the project either individually or in a group of no more than two
students. In case of the latter, submit only one report for the group, and include a description of the contribution of each member.
• Do a good job.
• You must use the following template for your report:
header name description
subject ID 1 to 97
psa PSA level Serum prostate-specific antigen level (mg/ml)
cancervol Cancer Volume Estimate of prostate cancer volume (cc)
weight Weight prostate weight (gm)
age Age Age of patient (years)
benpros Benign prostatic
hyperplasia
Amount of benign prostatic hyperplasia (cm2
)
vesinv Seminal vesicle
invasion
Presence (1) or absence (0) of seminal vesicle
invasion
capspen Capsular
penetration
Degree of capsular penetration (cm)
gleason Gleason score Pathologically determined grade of disease (6, 7 or 8)
Figure 1: List of variables in the prostate cancer data
.
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