9/29
Probability for a random variable X can be thought of as distributed along the number
line:
If X is discrete, there are bits of probability scattered along the line, like pebbles
of varying size.
The weight of each pebble is given by the probability
#REGRESSIONS
fish < read.table("http:/cs.slu.edu/~gong/Teaching/fish.txt", header=T)
attach(fish)
summary(out)
out$coef
out$coef[2] # gives you the second one (beta 1)
out$coef[3] #gives third (beta 2)
out = lm(MERCURY ~ LENGTH + WEIGHT + LENGTH*WEIGHT)
qnorm(0.025)
help(t.test)
x=rnorm(30,0,1)
y=dnorm(x,0,1) # density function
hist(x)
plot(y)
plot(x,y) # x and y shoud be the same length.
plot(x, y, type="p") # point
plot(x, y, type="l") # line
plot(x, y, type="b")
x
sortx=sort(x) # increasing order
sort
xbar= rep(0, 300)
for (i in 1:300) cfw_
xs = rexp( 400, 0.5) #rexp is to generate 400 values following exponential
distribution.
xbar[i] = mean(xs) # all the E(Xi) is equal to "u"
hist(xbar)
xs=rexp(400, 0.5)
hist(xs)
xbar= rep(0, 300)
for (i in 1:300) c
fish < read.table("fish.txt",header=T)
fish < read.table("http:/mathstat.slu.edu/~gong/Teaching/fish.txt",header=T)
head(fish)
attach(fish) # the variance of the 3rd column
var(LENGTH)
sd(WEIGHT)
cor(LENGTH,WEIGHT) # to find the correlation between "LEN
2/16/17
Informative Speech Outline
Name: Steffen Batista
Topic: Homelessness in New York City
General Purpose: To Inform
Thesis: The number of homeless people living in NYC is staggering and there should be more
done by the public and the government to co
Parks College of Engineering, Aviation and Technology
Saint Louis University
Module 5 Stage Check
Study Guide
Basic Attitude Instrument
Describe the various instrument scan patterns
Explain proper instrument scanning and interpretation techniques during
fish < read.table("fish.txt",header=T)
fish < read.table("http:/mathstat.slu.edu/~gong/Teaching/fish.txt",header=T)
head(fish)
attach(fish) #variance of the 3rd column
var(LENGTH)
sd(WEIGHT)
cor(LENGTH,WEIGHT) # to find the correlation between "LENGTH"
8/23
Rosencrantz and Guildenstern are dead (i.e., what do you make of 60 Heads in a row?)
Probability is the quantitative management of uncertainty.
8/25
If E is a possible outcome of a trial capable of being repeated in an independent series,
then Probab
Do 2.Rev problems and turn in appropriate ones Monday (9/27).
queen and three princes problem (2.135):
If the Queen has the gene, then the chances of princes getting it are
independent of one another.
Name the three princes!
If the Queen doesn't have the
10/4
expectation (mean) of random variables
discrete random variables:
Let X be a discrete random variable with values cfw_x_1, ., x_n and
probability mass function p_X.
We define the mean of X, written mu_Xalso called the expectation of X,
E(X)as follo
9/20
You go into a clinic and get tested for Condition X (a Bad Thing). You're told the test
results come back Positive for X; how worried should you be?
Need some information:
False Positive Rate = the rate at which
, in the clinic, patients who don't ha
10/1
why marginal density functions are as indicated:
we start with joint density function f_cfw_X,Y for two continuous random variables X
and Y
we know the density function f_Y should be the derivative of the distribution
function F_Y, i.e.,
f_Y(y) = dF_
10/20
Independence simplifies expressions; covariance measures how those expressions
aren't simplified for nonindependence.
If X, Y are independent:
E(XY) = E(X)E(Y)
proof: E(XY) = Integral Integral xy f_cfw_X,Y(x,y) dx dy
E(h(X,Y) = Integral Integral h(
9/13
Process for calculating probability of an event A (such as picking 2 white and 2 black
marbles when choosing 4 out of a bag of 8 white and 4 black):
describe U carefully (specify, for instance, that we mean unordered collections)
determine if U is eq
10/13
few key points on exam:
It helps to know what sort of number an answer should be, before doing any
calculations:
For example, "at least hundreds but not millions".
For example, "between 0 and 1, because it's a probability
For probabilities, try to g
10/22
Bernoulli trials:
a series of "experiments" such that
they each have only two outcomes, typically called "success" and "failure"
(although could be called anything else)
they are identical, all having the same probability p for "success"
typicall
sum(1:100) #1+2+3+.+100
# We use for loop
value = 0 #initial value
for ( i in 1:100 ) cfw_
value = value + i
value
# compute "1+3+5+.+99" by using "for loop"
value = 1
for ( i in 1:49 ) cfw_
value = value+2*i+1
value
factorial(100); factorial(10)
# comp
SAINT LOUIS UNIVERSITY
I FLIGHT
3
L

Part 141 TCO

PARKS COLLEGE OF ENGINEERING, AVIATION, AND TECHNOLOGY
REVISION NUMBER: Revision 3
23 February 2015
FLIGHT 3
Commercial BAI & Navigation I Instrument Navigation & Approach Operations
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