Normal Distribution
This is a continuous Symmetric, unimodal bell-shaped Distribution
- it has two parameters: mean $\mu$ and std $\sigma$, denoted as $N(\mu, \sigma)$
- Standard Normal Distribution is $N(\mu = 0, \sigma = 1)$
x = seq(from=-3, to=3, length=15)
normalDensity = dnorm(x, mean=0, sd=1)
r = round(normalDensity, 2)
bp = barplot(r)
xspline(x=bp, y=r, lwd=2, shape=1, border="blue")
text(x=bp, y=r+0.03, labels=as.character(r), xpd=TRUE, cex=0.7)
Code [1] [2]
Z-score
68-95-99.7 rule
Also referred as the "rule of 3 sigmas"
- most of the data lay within 3 $\sigma$s from $\mu$
-
$Z$-score
$Z$-score of an observation is the number of standard deviations for the mean
- 1 sdt above - $z = +1$
- 1.5 std below - $z = -1.5$
- $z = \cfrac{x - \mu}{\sigma}$
we can use $z$-scores to identify unusual observations
- $x_1$ is more unusual than $x_2$ if $| z_1 | > | z_2 |$
$Z$-standardization
- so $Z$-scores are used to standardize the observations
- in effect, it normalizes any normal distribution $N(\mu, \sigma$) to $N(0, 1)$
- see Normalization
Percentile
Example:
- Scores of SAT takers are distributed normally
- parameters: $\mu = 1500, \sigma = 300$
- Ann earned 1800 on SAT,
- so Ann's $z = 1$
Ann's percentile - percent of people who earned lower SAT score
-
- shaded - individuals who scored below Ann
- so knowing the $z$-score we can calculate the percentile
- Ann is the 84th percentile of SAT takers
- and vise-versa: we can also find $z$-score for given percentile
Example 2
- Shannon is a randomly selected SAT-taker.
- What's the probability that she'll score 1630 or more?
- Can find the $z$-score for that - it's $z = \cfrac{x - \mu}{\sigma} = 0.43$
- so we calculate the percentiles
- probability of getting below $z=0.43$ is 2/3
- so probability of getting above $z=0.43$ is 1 - 2/3 = 1/3
Always draw the bell shape first and then shade the area of interest
it may be useful for
Normal Approximation
Many processes can be approximated well by normal distribution
- e.g. SAT, height of USA males, etc
But need to check if it's reasonable to use the normal approximation
2 visual methods for checking the assumption of normality
- simple histogram + best fit of normal shape
- Q-Q Plot (or Normal Probability Plot)
Code to produce the first figure:
load(url("http://www.openintro.org/stat/data/bdims.RData"))
fdims = subset(bdims, bdims$sex == 0)
hist(fdims$hgt, probability=TRUE, ylim=c(0, 0.07))
x = 140:190
y = dnorm(x=x, mean=mean(fdims$hgt), sd=sd(fdims$hgt))
lines(x=x, y=y, col="blue")
Code to produce Q-Q Plots
qqnorm(fdims$hgt, col="orange", pch=19)
qqline(fdims$hgt, lwd=2)
Sources