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Q1: In assessing the predictive power of categorical predictors of a binary outcome,should logistic regression be used? Q2: 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 tumor 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 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!ReferenceR Documentation (2016). Prostate cancer data. Retrieved fromhttp://rafalab.github.io/pages/649/prostate.html
prostate.xlsx

Unformatted Attachment Preview

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