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#load dataset
heart <- read.csv("ClevelandHeart.csv")
#count males
table(heart$Sex)

female   male 
    97    206 

sum(is.na(heart$Thal))

[1] 2

# Histogram
hist(heart$Chol, main="Histogram of Cholesterol", xlab="Cholesterol (mg/dl)", col="skyblue")

# Summary statistics
mean(heart$Chol, na.rm=TRUE)

[1] 246.6931

median(heart$Chol, na.rm=TRUE)

[1] 241

sd(heart$Chol, na.rm=TRUE)

[1] 51.77692

IQR(heart$Chol, na.rm=TRUE)

[1] 64

# 95% CI for mean
t.test(heart$Chol)$conf.int

[1] 240.8397 252.5465
attr(,"conf.level")
[1] 0.95

max(heart$Chol, na.rm=TRUE)

[1] 564

# Fractions
normal <- mean(heart$Chol < 200, na.rm=TRUE)
high <- mean(heart$Chol > 240, na.rm=TRUE)

normal

[1] 0.1617162

high

[1] 0.5016502

plot(heart$Age, heart$MaxHR,
     xlab="Age (years)", ylab="Max Heart Rate",
     main="Age vs MaxHR", pch=19, col="darkgreen")

table(heart$AHD)

 No Yes 
164 139 

# Split
hd_yes <- subset(heart, AHD=="Yes")
hd_no  <- subset(heart, AHD=="No")

boxplot(MaxHR ~ AHD, data=heart,
        xlab="Heart Disease", ylab="MaxHR",
        main="MaxHR by Heart Disease Status", col=c("orange","lightblue"))

qqnorm(hd_yes$MaxHR); qqline(hd_yes$MaxHR, col="red")

qqnorm(hd_no$MaxHR); qqline(hd_no$MaxHR, col="blue")

t.test(MaxHR ~ AHD, data=heart)

    Welch Two Sample t-test

data:  MaxHR by AHD
t = 7.8579, df = 272.27, p-value = 9.106e-14
alternative hypothesis: true difference in means between group No and group Yes is not equal to 0
95 percent confidence interval:
 14.32900 23.90912
sample estimates:
 mean in group No mean in group Yes 
          158.378           139.259 

wilcox.test(MaxHR ~ AHD, data=heart)

    Wilcoxon rank sum test with continuity correction

data:  MaxHR by AHD
W = 16990, p-value = 1.861e-13
alternative hypothesis: true location shift is not equal to 0

boxplot(Chol ~ AHD, data=heart,
        xlab="Heart Disease", ylab="Cholesterol",
        main="Cholesterol by Heart Disease Status", col=c("pink","lightgreen"))

qqnorm(hd_yes$Chol); qqline(hd_yes$Chol, col="red")

qqnorm(hd_no$Chol); qqline(hd_no$Chol, col="blue")

t.test(Chol ~ AHD, data=heart)

    Welch Two Sample t-test

data:  Chol by AHD
t = -1.4924, df = 298.64, p-value = 0.1366
alternative hypothesis: true difference in means between group No and group Yes is not equal to 0
95 percent confidence interval:
 -20.484170   2.815018
sample estimates:
 mean in group No mean in group Yes 
         242.6402          251.4748 

wilcox.test(Chol ~ AHD, data=heart)

    Wilcoxon rank sum test with continuity correction

data:  Chol by AHD
W = 9798.5, p-value = 0.03536
alternative hypothesis: true location shift is not equal to 0

Question 1

There are 206 male patients in the dataset and 2 NAs. ## Question 2 (mean, median, SD, IQR, CI) 246.6931, 241, 51,66, 64, (240.84, 252.44) ## Question 3 564, 0.1617, 0.501 ## Question 4 As age goes down max heart rate goes down slightly but the correlation is light and can barely see anything ##Question 7 There are a few lower outliers byut no larger ones ##Question 10 Both tests agree and say that the true difference in means are not equal to 0 ##Question 15 both tests do agree and say the true difference is not equal to 0

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