STAT-UB.0103
NOTES for Thursday 2012.MAR.29
Now ready for the extension to multiple regression. It looks like we just
expand the situation to cover multiple predictors (independent variables). The
dat
STAT-UB.0103
NOTES for Wednesday 2012.MAR.21
The regression problem does not treat the two variables in the same way.
One of the variables is called the independent variable, and its given the
horizon
STAT-UB.0103
NOTES for Monday 2012.MAR.19
This first example illustrates the normal random walk for equity prices. This was not
done in class. Most analyses of equity prices use the lognormal random w
STAT-UB.0103
NOTES for Thursday 2012.MAR.08
Some fun, easy math.
Lets say you get a 10% raise, then you get a 10% pay cut. Are you better off, worse off,
or the same?
Suppose that your base salary is
STAT-UB.0103
NOTES for Wednesday 2012 MAR 07
And now. back to stories about the sample average X .
Recall that in the sampling story we had E( X ) = and also SD( X ) =
.
n
Major use is law of averages
STAT-UB.0103
NOTES for Monday, 2012.MAR.05
Suppose that X1 , X2 , , Xn is a sample from a population with mean and standard
. Of course, the sample standard deviation s
deviation . We know that SD ( X
STAT-UB.0103
Notes for Thursday 2012.MAR.01
Heres a quick review about what weve done with random variables.
For discrete random variables, we studied the binomial, the Poisson, and the
hypergeometric
NOTES for Monday 2012.JAN.30
STAT-UB.0103
By the way, if the list x1, x2, , xn constitutes an entire population (rather than a
1
2
sample) we define the standard deviation as =
xi , where we use
n
1N
NOTES for Thursday 2012.JAN.26
STAT-UB.0103
Weve been dabbling in various numeric summaries for quantitative data. Crudely we
separate these (so far) in terms of measures of location (position) and me
STAT-UB.0103
NOTES for Wednesday 2012.JAN.25
Lets consider a number of devices for displaying quantitative, or measured, data.
These devices can also be used for ordered qualitative data.
We can alway
C22.0103
NOTES for 2012.JAN.23
Office hours for Gary Simon, Spring 2012
These are tentative, pending committee times.
Mondays:
3:30 p.m. 5:00 p.m.
Thursdays
noon 1:30 p.m.
The course syllabus has been
STAT-UB.0103
NOTES for 2012.FEB.29
Lets note some interesting things you can do in Minitab with regard to continuous
distributions.
The command Graph Probability Distribution Plot will allow you to se
STAT-UB.0103
NOTES for Monday, 2012.FEB.27
We need a way to describe continuous random variables. There is no probability at any
single value, so we cant simply use descriptions of the form P[ X = k ]
STAT-UB.0103
NOTES for Wednesday 2012.FEB.15
Suppose that a hospital has a cardiac care unit which handles heart attack victims
on the first day of their problems. The geographic area served by the ho
STAT-UB.0103
NOTES for Monday 2012.FEB.13
If X is a binomial random variable with parameters n and p (meaning that X is the
number of successes in n independent trials where each trial has success
pro
STAT-UB.0103
NOTES for Wednesday 2012.FEB.08
Here is an interesting misuse of independence. The website
http:/howmanyofme.com claims to give the number of people in the United
States with any name. Th
Notes for Monday 2012.FEB.06
STAT-UB.0103
Reprise on the taxi problem. Let
G
B
G
B
be the event that a green taxi caused the accident
be the event that a blue taxi caused the accident
be the event tha
STAT-UB.0103
Notes for Wednesday, 2012.FEB.01.
For many problems, we need to do a little counting. We try to construct a sample
space S for which the elements are equally likely. Then for any event E,
STAT-UB.0103
NOTES for Thursday 2012.APR.05
Advantages of best subsets regression and stepwise regression:
The procedures are automated, so that the user does not have to think about
correlations, VIF
STAT-UB.0103
NOTES for Wednesday 2012.APR.04
One of the clues on the library data comes through the VIF values. These VIFs tell you to
what extent a predictor is linearly dependent on other predictors
STAT-UB.0103
NOTES for Monday 2012.APR.02
Multiple regression creates comes some difficulties with the interpretation of
coefficients. Suppose that you do the regression of Y on (G, H, K). The
estimat