Lecture 3 Notes, Numerical Data
Sample Mean:
x
n
i =1 i
x :=
.
n
Sample median: order the data values x(1) x(2) x(n), so then
x( )
n odd
n+1
2
median := x := 1
2
n
n
[x( )
+ x( +1)]
2
n even
.
2
M
SAS
1.A raw data file is listed below.
1-+-10-+-20-+-son Frank 01/31/89
daughter June
12-25-87
brother Samuel 01/17/51
The following program is submitted using this file as input:
data work.family;
i
ISE 305 Engineering Statistics I
Course:
Winter 2005
ISE 305 Engineering Statistics I - 4 credit hours
Call number: 03978
ISE 505 Engineering Statistics I - 3 credit hours
Call number: 03998
Catalog d
Lecture 11 Notes, Nonparametric Statistics
Does not depend on the population fitting any particular type of distribution
(e.g, normal). Make fewer assumptions and apply more broadly at the
expense of
Lecture 10 Notes, Regression
Regression analysis allows us to estimate the relationship of a response
variable to a set of predictor variables
Let
x1, x2, xn
be settings of x chosen by the investigato
Lecture 2 Notes, Data
A population is a collection of objects, items, humans/animals (units) about
which information is sought.
A sample is a part of the population that is ob
Lecture 9 Notes, Two-Sample Inference
Independent Samples Design:
There are a few dierent ways we can do an experiment. In an independent samples design,
we have an independent sample from each popul
Lecture 6 Notes, Inference
Statistical Inference is the process of making conclusions using data that is subject to random variation.
Bias() := E() , where is the true parameter value and is an estima
Lecture 4 Notes, Central Limit
Let X1, X2, . . . , Xn be a random sample drawn from any distribution with a finite mean and
2
variance . As n , the distribution of:
X
/
n
converges to the distributio
Lecture 5 Notes, Confidence Intevals
Instead of reporting a point estimator, that is, a single value, we want to report a
confidence interval [L, U] where:
P cfw_L U = 1 ,
the probability of the true
Lecture 1 Notes, Probability
A probability space, defined by Kolmogorov (1903-1987) consists of:
A set of outcomes S, e.g.,
for the roll of a die, S = cfw_1, 2, 3, 4, 5, 6,
for the roll of two dice,
Lecture 8 Notes, Single Sample Inference
You know already for a large sample, you can invoke the CLT so:
2
X N(, ).
Also for a large sample, you can replace an unknown by s.
know how to do a hypothe