RCode4
Kat Taylor
2/24/2020
R Markdown
download.file("http://www.openintro.org/stat/data/bdims.RData", destfile = "bdims.RData")
load("bdims.RData")First Few Rows of Data
head(bdims)## bia.di bii.di bit.di che.de che.di elb.di wri.di kne.di ank.di sho.gi che.gi
## 1 42.9 26.0 31.5 17.7 28.0 13.1 10.4 18.8 14.1 106.2 89.5
## 2 43.7 28.5 33.5 16.9 30.8 14.0 11.8 20.6 15.1 110.5 97.0
## 3 40.1 28.2 33.3 20.9 31.7 13.9 10.9 19.7 14.1 115.1 97.5
## 4 44.3 29.9 34.0 18.4 28.2 13.9 11.2 20.9 15.0 104.5 97.0
## 5 42.5 29.9 34.0 21.5 29.4 15.2 11.6 20.7 14.9 107.5 97.5
## 6 43.3 27.0 31.5 19.6 31.3 14.0 11.5 18.8 13.9 119.8 99.9
## wai.gi nav.gi hip.gi thi.gi bic.gi for.gi kne.gi cal.gi ank.gi wri.gi age
## 1 71.5 74.5 93.5 51.5 32.5 26.0 34.5 36.5 23.5 16.5 21
## 2 79.0 86.5 94.8 51.5 34.4 28.0 36.5 37.5 24.5 17.0 23
## 3 83.2 82.9 95.0 57.3 33.4 28.8 37.0 37.3 21.9 16.9 28
## 4 77.8 78.8 94.0 53.0 31.0 26.2 37.0 34.8 23.0 16.6 23
## 5 80.0 82.5 98.5 55.4 32.0 28.4 37.7 38.6 24.4 18.0 22
## 6 82.5 80.1 95.3 57.5 33.0 28.0 36.6 36.1 23.5 16.9 21
## wgt hgt sex
## 1 65.6 174.0 1
## 2 71.8 175.3 1
## 3 80.7 193.5 1
## 4 72.6 186.5 1
## 5 78.8 187.2 1
## 6 74.8 181.5 1mdims <- subset(bdims, sex == 1)
fdims <- subset(bdims, sex == 0)Histogram of Men’s Heights
hist(mdims$hgt)
## Histogram of Womens’s Heights
hist(fdims$hgt)
### Normal Distabution ##normal curve plotted on top
fhgtmean <- mean(fdims$hgt)
fhgtsd <- sd(fdims$hgt)hist(fdims$hgt, probability = TRUE)
x <- 140:190
y <- dnorm(x = x, mean = fhgtmean, sd = fhgtsd)
lines(x = x, y = y, col = "blue")
## is it normally distributaded? ## I would say it appear to follow the normal curve pretty well.
Evaluating Normal Distrabution
qqnorm(fdims$hgt)
qqline(fdims$hgt)
P Plot of Sim_norm
sim_norm <- rnorm(n = length(fdims$hgt), mean = fhgtmean, sd = fhgtsd)
qqnorm(sim_norm)
qqline(sim_norm)
more data
my r thinks it’s too much data
qqnormsim(fdims$hgt)
##Does the normal probability plot for fdims$hgt look similar to the plots created for the simulated data? That is, do plots provide evidence that the female heights are nearly normal? ## all pieces of data would concure that the female’s heights. All the plot’s look diffent, but that is because they are desined to analyze normalness in diffrent ways.
women’s weight ND]
##Histogram
fwgtmean <- mean(fdims$wgt)
fwgtsd <- sd(fdims$wgt)hist(fdims$wgt, probability = TRUE)
x <- 140:190
y <- dnorm(x = x, mean = fwgtmean, sd = fwgtsd)
lines(x = x, y = y, col = "blue")
## Q norm
qqnorm(fdims$hgt)
qqline(fdims$hgt) ## weight would appear to be normal as well
Normal Probablities
1 - pnorm(q = 182, mean = fhgtmean, sd = fhgtsd)## [1] 0.004434387sum(fdims$hgt > 182) / length(fdims$hgt)## [1] 0.003846154#what are the odds you will randomly select a women who weighs more then 100plbs
pnorm(q = 100, mean = fwgtmean, sd = fwgtsd)## [1] 0.9999791#what are the odds you will randomly select a women who’s shoter than 100cm
1 - pnorm(q = 100, mean = fhgtmean, sd = fhgtsd)## [1] 1on your own
##1. match # a. c # b. a # c. d # d. b ##2. why C and D slant upwards. #Most litley this indicates the graph is scewed right (or in the positive direction). ##3. Normal curve # error