This repository includes the simulated data for Assignment #2. Fork this repository and add your analysis as described in the canvas assignment.
The csv file for cohort in the raw-data folder includes 5,000 observations with variables smoke, female, age, cardiac, and cost.
#############1: Table Describing Variables############# smoke female age cardiac cost
Min. :0.0000 Min. :0.0000 Min. :18.00 Min. :0.000 Min. : 8012
1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:31.00 1st Qu.:0.000 1st Qu.: 9090
Median :0.0000 Median :1.0000 Median :45.00 Median :0.000 Median : 9376
Mean :0.1292 Mean :0.5732 Mean :44.94 Mean :0.055 Mean : 9398
3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:59.00 3rd Qu.:0.000 3rd Qu.: 9676
Max. :1.0000 Max. :1.0000 Max. :72.00 Max. :1.000 Max. :11370#############2: Linear Regression#############
my_model <- glm(cost ~., smoke + age + female + cardiac,
-
family = gaussian, -
data = cohort)
###############3: Plot###############
par(mfrow = c(1, 2))
####Smoke#### plot(cohort$smoke, cohort$cost,
-
main = "Assignment 2 Regression Plot", -
xlab = "Smoke", -
ylab = "Cost")
abline(my_model, col = "red", lwd = 2) Warning message: In abline(my_model, col = "red", lwd = 2) : only using the first two of 5 regression coefficients
####Age####
plot(cohort$age, cohort$cost,
-
main = "Assignment 2 Regression Plot", -
xlab = "Age", -
ylab = "Cost")
abline(my_model, col = "red", lwd = 2) Warning message: In abline(my_model, col = "red", lwd = 2) : only using the first two of 5 regression coefficients
####Female####
plot(cohort$female, cohort$cost,
-
main = "Assignment 2 Regression Plot", -
xlab = "Female", -
ylab = "Cost")
abline(my_model, col = "red", lwd = 2) Warning message: In abline(my_model, col = "red", lwd = 2) : only using the first two of 5 regression coefficients
####Cardiac####
plot(cohort$cardiac, cohort$cost,
-
main = "Assignment 2 Regression Plot", -
xlab = "Cardiac", -
ylab = "Cost")
abline(my_model, col = "red", lwd = 2) Warning message: In abline(my_model, col = "red", lwd = 2) : only using the first two of 5 regression coefficients
geom_smooth(method = "lm") geom_smooth: na.rm = FALSE, orientation = NA, se = TRUE stat_smooth: na.rm = FALSE, orientation = NA, se = TRUE, method = lm position_identity
#############4: Summary of Findings############# summary(my_model)
Call: glm(formula = cost ~ ., family = gaussian, data = cohort, weights = smoke + age + female + cardiac)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8708.2984 12.2953 708.26 <2e-16 ***
smoke 508.6336 10.0461 50.63 <2e-16 ***
female -269.3246 6.7310 -40.01 <2e-16 ***
age 16.6895 0.2194 76.08 <2e-16 ***
cardiac 522.6343 14.7812 35.36 <2e-16 ***
Signif. codes: 0 ‘’ 0.001 ‘’ 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for gaussian family taken to be 2397895)
Null deviance: 4.4128e+10 on 4999 degrees of freedom
Residual deviance: 1.1977e+10 on 4995 degrees of freedom AIC: 68867
Number of Fisher Scoring iterations: 2
###############5: I did not use AI to complete this assignment###############