Sample Test One
Instructions:
1. Space will be provided for you to work out your answers on the actual
midterm.
2. You should be able to complete this midterm within 45 minutes. If
you can not, this m
R Assignment 1
Due Date: The assignment will be accepted for marking at the beginning of class, January
19th, 2018. Assignments which are submitted after this deadline will not be accepted for
marking
Stat 359 Assignment 1
1. Reading: Chapters 1, 2, 3, 4
2. Suppose the following data comes from a study on plant growth (mm) where 2 plants are in
each pot, 3 pots are within each plot and 2 plots are
Stat 359 Assignment 4
1. Beef consumption (in pounds per capita) in the United States between 1922 and 1941 are
given in the data set beef.txt. Other variables of interest are beef price (in cents per
Stat 359 Assignment 3
1. In a study examining smoking and lung cancer, a random sample of men between the ages
of 55 and 60 was obtained. The smoking and disease status of each sampled subject was
asc
Stat 359 / 563 Assignment 2
1. Use R to take 10, 100, 1000 samples of size 10, 20, 50, 100 from 3 distributions.
1) Uniform(a=0, b=1)
2) Poisson( = 5)
3) Bernoulli(p=0.20) (or Binomial(n=1, p=0.20)
(a
Finck, Amy
V00878512
STAT 255 Assignment 2
Instructor: Hassan AlNasser
Part 1
a) Let X = the number of patients that enter in a two-hour period
= 7 patients / 1 hour
s = 2 hours
X ~ Poiss(s = 14)
P(X
Stat 260 - Lecture 11
Recap of Last Class
1. We discussed expected value and variance
for discrete random variables.
2. Presented some rules and properties of variance and expected value and in partic
Stat 260 - Lecture 8
Recap of Last Class
1. We worked through an example illustrating
the use of the law of total probability and
Bayes theorem.
2. We discussed what it means for events to
T
be indepe
Stat 260 - Lecture 2
Have you registered?
Last class:
1. Introduced statistics
2. Defined: data, population, sample, variable
3. Talk about the difference between descriptive statistics and inferenti
Stat 260 - Lecture 13
Recap of Last Class
Last lecture we defined the Poisson distribution and examined its properties.
Today:
We will introduce continuous random variables.
Continuous Random Variab
STATISTICS 260
We begin the course with a short lecture. I
will give some information about the course,
go over the course outline and present a few
preliminary definitions.
Course Information:
Instr
Stat 260 - Lecture 10
Recap of Last Class
1. Introduced the expected value of a random
variable and discussed the calculation of
expected values.
Today:
1. Discuss some rules of expected values.
2. De
Stat 260 - Lecture 4
Last Class:
We defined an experiment, a sample space
and events.
We began our discussion of probability with
an introduction to set theory. We defined
the union, intersection an
Stat 260 - Lecture 7
Recap of Last Class
1. We began by working through two examples illustrating the calculation of conditional probability.
2. We derived the Law of Total probability.
3. We derived
Stat 260 - Lecture 27
Recap of Last Class: We introduced the t
distribution and used it to derive a 100(1
)% CI for the mean of a normal population (valid for small samples).
Today: We will introdu
Stat 260 - Lecture 28
Recap of Last Class: We introduced hypothesis testing and P-values.
We derived a large sample Z-test for a population mean, , and showed how to compute the P-values for the tes
Stat 260 - Lecture 24
Recap of Last Class We introduced some
basic concepts of point estimation
1. Unbiased estimators
2. Minimum variance unbiased estimators
3. Standard errors a way of quantifying
Stat 260 - Lecture 3
Course web page:
http:/www.math.uvic.ca/~nathoo/stat260.html
I will post lecture overheads on the web page.
Last class:
1. Discussed descriptive statistics
2. Defined the sample
Stat 260 - Lecture 5
Recap of Last Class
1. Discussed some of the basic axioms of probability.
2. Discussed the interpretation of probability
in terms of relative frequencies and gave a
(somewhat) rig
Stat 260 - Lecture 15
Recap of Last Class
Last class we discussed several concepts
related to continuous random variable: the
cdf, 100pth percentile, mean and variance.
Today:
We will introduce a pa
Stat 260 - Lecture 12
Recap of Last Class
We introduced the binomial distribution and
binomial random variables.
Today:
Begin with an example illustrating the use
of the binomial distribution.
We w
Stat 260 - Lecture 6
Recap of Last Class
1. Reviewed a problem from an old midterm
calculating probability in a standard problem.
2. Introduced the concept of conditional probability and gave a mathe
Stat 260 - Lecture 21
Recap of Last Class
We introduced the idea of a statistic which
is some function of sample data.
We discussed ways of determining the probability distribution of a statistic -
Stat 260 - Lecture 29
Recap of Last Class: We derived a large
sample Z-test for a population proportion.
Test procedure for the mean of a normal
population that is valid even when n is
small.
Today
Stat 260 - Lecture 25
Recap of Last Class We introduced the
most popular technique for deriving estimators of unknown parameters: Maximum
likelihood estimation.
1. The technique is based on the likel