##################

# Exercise #4

##################

##################

sink(“C:/UMUC/week5exercise.txt”)

# install.packages(‘dplyr’)

library(dplyr)

# Step 2: Read your data

# Pl change the location of file

hosp <- read.csv("C:/UMUC/HMGT400HOSPITAL.csv", header=T, sep = ',')
#Step 3: See the variables' names
names (hosp)
#Step 4: Generate new variables
hosp$benefit <- (hosp$total_hosp_revenue-hosp$total_hosp_cost)
hosp$medicare_discharge_ratio <- (hosp$total_hospital_medicare_discharg/hosp$total_hospital_discharges)*100
hosp$medicaid_discharge_ratio <- (hosp$total_hospital_medicaid_discharg/hosp$total_hospital_discharges)*100
# Step 5; Mean selected variable
summarize (hosp, bed=mean(hospital_beds, na.rm=T),
member=mean(system_member, na.rm=T),
cost=mean(total_hosp_cost, na.rm=T),
revenue=mean(log_hosp_revenue, na.rm=T),
benefit=mean(benefit, na.rm=T),
medicare_ratio=mean(medicare_discharge_ratio, na.rm=T),
medicaid_ratio=mean(medicaid_discharge_ratio, na.rm=T))
mean(hosp$hospital_beds)
mean(hosp$system_member)
mean(hosp$total_hosp_cost)
mean(hosp$log_hosp_revenue)
mean(hosp$benefit)
mean(hosp$medicare_discharge_ratio)
mean(hosp$medicaid_discharge_ratio)
sd(hosp$hospital_beds)
sd(hosp$system_member)
sd(hosp$total_hosp_cost)
sd(hosp$log_hosp_revenue)
sd(hosp$benefit)
sd(hosp$medicare_discharge_ratio)
sd(hosp$medicaid_discharge_ratio)
# Step 6; SD selected variable
summarize (hosp, bed=sd(hospital_beds, na.rm=T),
member=sd(system_member, na.rm=T),
cost=sd(total_hosp_cost, na.rm=T),
revenue=sd(log_hosp_revenue, na.rm=T),
benefit=sd(benefit, na.rm=T),
medicare_ratio=sd(medicare_discharge_ratio, na.rm=T),
medicaid_ratio=sd(medicaid_discharge_ratio, na.rm=T))
# Step 7; N for categorical variable
# Step 7a
## Bed Size
##1) <50
##2) 51-150
##3) 151-250
##4) 251-350
##5) 351-450
##6) 451-550
##7) 551-650
##8) >651

table(hosp$bedsize_cat)

# Step 7b

## Ownership

## 0) non-for-profit

## 1) for profit

## 2) Public

## 3) Other

table(hosp$own)

# Cost

mytable <- table(hosp$total_hosp_cost)
summary(mytable)
# Revenue
mytable <- table(hosp$total_hosp_revenue)
summary(mytable)
# system_member
mytable <- table(hosp$system_member)
summary(mytable)
# benefit
mytable <- table(hosp$benefit)
summary(mytable)
# total_hospital_medicare_discharg
mytable <- table(hosp$total_hospital_medicare_discharg)
summary(mytable)
# total_hospital_medicaid_discharg
mytable <- table(hosp$total_hospital_medicaid_discharg)
summary(mytable)
# Step 8: Generate new variables
hosp$benefit <- (hosp$total_hosp_revenue-hosp$total_hosp_cost)
hosp$medicare_discharge_ratio <- (hosp$total_hospital_medicare_discharg/hosp$total_hospital_discharges)*100
hosp$medicaid_discharge_ratio <- (hosp$medicaid_discharge_ratio/hosp$total_hospital_discharges)*100
# Step 9: Generate Factor variables
own1 <- factor(hosp$own, levels = c(0, 1, 2, 3))
bed_cat1 <- factor(hosp$bedsize_cat, levels = c(1, 2, 3, 4, 5, 6, 7, 8))
# Step 10: run regression models
# 1st Model:
## 0) non-for-profit
## 1) for profit
## 2) Public
## 3) Other
# Model 1a: Using bed as a continuous variable
Benefit=function(beds, ownership)
y=B0+B1beds+B2FP+B3Pbl+B4Ot+e
model1a <- lm(benefit ~ hospital_beds + own1, data=hosp)
summary(model1a)
# Model 1b: Using bed as a categorical variable
model1b <- lm(benefit ~ bed_cat1 + own1, data=hosp)
summary(model1b)
# Model 2:
model2 <- lm(benefit ~ hospital_beds + own1 + system_member, data=hosp)
summary(model2)
# Model 3:
model3 <- lm(total_hosp_revenue ~ hospital_beds + own1 + system_member + medicare_discharge_ratio + medicaid_discharge_ratio , data=hosp)
summary(model3)
# You may like to look at the plot to have better understaning.
plot(hosp$benefit , hosp$hospital_beds, pch = 1,
cex =.5, col = "blue", main = "Figure 1. Hospital Revenues and Hospital Beds", cex.main =.8,
xlab = "Hospital Revenue ($)", ylab = "Hospital Beds(#)")
abline (hosp$benefit , hosp$hospital_beds)
hosp_sub <- subset(hosp, hosp$benefit>0 & hosp$benefit<200000000)
plot(hosp_sub$benefit , hosp_sub$hospital_beds, pch = 1,
cex =.5, col = "blue", main = "Figure 2. Hospital Benfit and Hospital Beds", cex.main =.8,
xlab = "Hospital Revenue ($)", ylab = "Hospital Beds(#)")
abline (hosp_sub$benefit , hosp_sub$hospital_beds)
# Thank you,
# Dr. Zare
sink()

Exercise #4

Don't use plagiarized sources. Get Your Custom Essay on

EXERCISE 4 HMGT 400

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Linear Regression Model

If you have chosen to work with RStudio, please run the following model and complete the following tables.

1st Model:

Run a linear model and predict the difference between hospital beds (use the bed-tot) and hospital’s ownership on hospital net-benefit? Discuss your finding, do you think having higher beds has positive impact on the hospital net benefit? What about the ownership?

Model 1a

Hospital Characteristics

Coef.

St. Err

Hospital beds

Ownership

For Profit

Non-for profit

Other

N

R-Squared

2nd Model:

Now, estimate the impact of being a member of a system on hospital net benefit? And discuss your finding (nor more than 2 lines)? Is it significant?

Model 2

Hospital Characteristics

Coef.

St. Err

Hospital beds

Ownership

For Profit

Non-for profit

Other

Membership

System Membership

N

R-Squared

3nd Model:

Now, include the ratio of ratio-Medicare-discharge and ratio-Medicaid-discharge in your model? How do you evaluate the impact of having higher Medicare and Medicaid patients on hospital revenues?

Model 3

Hospital Characteristics

Coef.

St. Err

Hospital beds

Ownership

For Profit

Non-for profit

Other

Membership

System Membership

Socio-Economic Characteristics

Medicare discharge ratio

Medicaid discharge ratio

N

R-Squared

Based on your finding please recommend 3 policies to improve hospital performance, please make sure to use the final model for your recommendation.

Discuss your findings.

##################

# Exercise #4

##################

##################

sink(“C:/UMUC/week4exercise.txt”)

# install.packages(‘dplyr’)

# library(dplyr)

# Step 2: Read your data

# Pl change the location of file

hosp <- read.csv("C:/UMUC/HMGT400HOSPITAL.csv", header=T, sep = ',')
#Step 3: See the variables' names
names (hosp)
#Step 4: Generate new variables
hosp$benefit <- (hosp$total_hosp_revenue-hosp$total_hosp_cost)
hosp$medicare_discharge_ratio <- (hosp$total_hospital_medicare_discharg/hosp$total_hospital_discharges)*100
hosp$medicaid_discharge_ratio <- (hosp$total_hospital_medicaid_discharg/hosp$total_hospital_discharges)*100
# Step 5; Mean selected variable
# summarize (hosp, bed=mean(hospital_beds, na.rm=T),
#member=mean(system_member, na.rm=T),
# cost=mean(total_hosp_cost, na.rm=T),
# revenue=mean(log_hosp_revenue, na.rm=T),
# benefit=mean(benefit, na.rm=T),
# medicare_ratio=mean(medicare_discharge_ratio, na.rm=T),
# medicaid_ratio=mean(medicaid_discharge_ratio, na.rm=T))
mean(hosp$hospital_beds, na.rm=T)
mean(hosp$system_member, na.rm=T)
mean(hosp$total_hosp_cost, na.rm=T)
mean(hosp$log_hosp_revenue, na.rm=T)
mean(hosp$benefit, na.rm=T)
mean(hosp$medicare_discharge_ratio, na.rm=T)
mean(hosp$medicaid_discharge_ratio, na.rm=T)
sd(hosp$hospital_beds, na.rm=T)
sd(hosp$system_member, na.rm=T)
sd(hosp$total_hosp_cost, na.rm=T)
sd(hosp$log_hosp_revenue, na.rm=T)
sd(hosp$benefit, na.rm=T)
sd(hosp$medicare_discharge_ratio, na.rm=T)
sd(hosp$medicaid_discharge_ratio, na.rm=T)
# Step 6; SD selected variable
#summarize (hosp, bed=sd(hospital_beds, na.rm=T),
# member=sd(system_member, na.rm=T),
# cost=sd(total_hosp_cost, na.rm=T),
# revenue=sd(log_hosp_revenue, na.rm=T),
# benefit=sd(benefit, na.rm=T),
# medicare_ratio=sd(medicare_discharge_ratio, na.rm=T),
# medicaid_ratio=sd(medicaid_discharge_ratio, na.rm=T))
# Step 7: Generate new variables
#hosp$benefit <- (hosp$total_hosp_revenue-hosp$total_hosp_cost)
#hosp$medicare_discharge_ratio <- (hosp$total_hospital_medicare_discharg/hosp$total_hospital_discharges)*100
#hosp$medicaid_discharge_ratio <- (hosp$medicaid_discharge_ratio/hosp$total_hospital_discharges)*100
# Step 8; N for categorical variable
# Step 8a
## Bed Size
##1) <50
##2) 51-150
##3) 151-250
##4) 251-350
##5) 351-450
##6) 451-550
##7) 551-650
##8) >651

table(hosp$bedsize_cat)

# Step 8b

## Ownership

## 0) non-for-profit

## 1) for profit

## 2) Public

## 3) Other

table(hosp$own)

mytable <- table(hosp$total_hosp_cost)
summary(mytable)
# 10-7
mytable <- table(hosp$total_hosp_revenue)
summary(mytable)
# 10-12
mytable <- table(hosp$benefit)
summary(mytable)
# 10-13
mytable <- table(hosp$medicare_discharge_ratio)
summary(mytable)
mytable <- table(hosp$medicaid_discharge_ratio)
summary(mytable)
# Step 9: Generate Factor variables
own1 <- factor(hosp$own, levels = c(0, 1, 2, 3))
bed_cat1 <- factor(hosp$bedsize_cat, levels = c(1, 2, 3, 4, 5, 6, 7, 8))
# Step 10: run regression models
# 1st Model:
# Model 1a: Using bed as a continuous variable
model1a <- lm(benefit ~ hospital_beds + own1, data=hosp)
summary(model1a)
# Model 1b: Using bed as a categorical variable
model1b <- lm(benefit ~ bed_cat1 + own1, data=hosp)
summary(model1b)
# Model 2:
model2 <- lm(benefit ~ hospital_beds + own1 + system_member, data=hosp)
summary(model2)
# Model 3:
model3 <- lm(total_hosp_revenue ~ hospital_beds + own1 + system_member + medicare_discharge_ratio + medicaid_discharge_ratio , data=hosp)
summary(model3)
# You may like to look at the plot to have better understaning.
plot(hosp$benefit , hosp$hospital_beds, pch = 1,
cex =.5, col = "blue", main = "Figure 1. Hospital Revenues and Hospital Beds", cex.main =.8,
xlab = "Hospital Revenue ($)", ylab = "Hospital Beds(#)")
abline (hosp$benefit , hosp$hospital_beds)
hosp_sub <- subset(hosp, hosp$benefit>0 & hosp$benefit<200000000)
plot(hosp_sub$benefit , hosp_sub$hospital_beds, pch = 1,
cex =.5, col = "blue", main = "Figure 2. Hospital Benfit and Hospital Beds", cex.main =.8,
xlab = "Hospital Revenue ($)", ylab = "Hospital Beds(#)")
abline (hosp_sub$benefit , hosp_sub$hospital_beds)
# Thank you,
# Dr. Zare
sink()

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