Objective: Using Logistic Regression to handle a binary outcome. Given the prostate cancer dataset, in which biopsy results are given for 97 men:

• You are to predict tumour spread in this dataset of 97 men who had undergone a biopsy.

• The measures to be used for prediction are age, lbph, lcp, gleason, and lpsa. This implies that the binary dependent variable of lcavol will be the outcome variable. We start by loading the appropriate libraries in R: ROCR, ggplot2, and aod packages as follows: > install.packages(“ROCR”) > install.packages(“ggplot2”) > install.packages(“aod”) > library(ROCR) > library(ggplot2) > library(aod) Next, we load the csv file and check the statistical properties of the csv File as follow: > setwd(“C:/RData”) # your working directory > tumor <- read.csv(“prostate.csv”) # loading the file > str(tumor) # check the properties of the file . . . continue from here! Reference R Documentation (2016). Prostate cancer data. Retrieved from http://rafalab.github.io/pages/649/prostate.html

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