# Using Logistic Regression to handle a binary outcome.

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! Reference R Documentation (2016). Prostate cancer data. Retrieved from http://rafalab.github.io/pages/649/prostate.html

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Week 12 Assignment - 100 points 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 …

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 …

Week 8 Assignment - 100 points

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 …

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 …

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