Data Visualization
Assignment No 1.
Ren, Zeyu (Student ID 20341228.)
2013914 13:07:56
1. Evolution of the Eye. Watch History channels episode of Evolve on The
Eye.Watch this and the remaining 4 clips, then answer the following questions:
(a) Kent Steven
STAT 332 Spring 2013 Test 2
Basic Rules:
1. Only a math approved, nonprogrammable, non graphical calculator is allowed.
2. Please put away your cell phone in your bag. A cell phone that is within reach is
considered to be an illegal tool.
3. Please leav
Stat 332  Mid Term Examination 1  Spring 2014
Solutions
Instructions:
This examination is closed book.
You have 80 minutes to complete the examination.
The problems you need to solve are on the last sheet.
Please wait to begin the examination until
Stat 332  Fall 2013
Instructor: M. Schonlau, Ph.D.
Email: [email protected]
Office: M3, room 4111
Lectures:
Monday, Wednesday and Friday 12:301:20 pm in QNC 1502 (Quantum Nano center)
I will not use the tutorial slots (4:3005:20Mon, MC4021), exce
University of Waterloo
STAT 332
Sampling and Experimental Design
Course Notes
Spring 2014
by
JOCK MACKAY
[email protected]
STEFAN STEINER
[email protected]
MATTHIAS SCHONLAU
[email protected]
Stat 332 RJ MacKay and SH Steiner, University of W
M idterm Exam STAT 332
University o f Waterloo
Mid Term Exam
Term: Spring
Year: 2011
I Student Name
Student ID Number
Course Abbreviation and N umber
STAT 332
Course Title
Experimental Design and Sampling
Instructor
Paquita Freire
Date o f Exam
June 22/20
Homework July 10, 2013
Question 1: In Simple Random sampling with replacement (SRSWR), we select units from a population and
then replace the unit back before we select a second unit. What is the inclusion probability for unit i?
Question 2: Show that the
Stat 332 MidTerm 1 Practice Problems
Solutions
1. You are a researcher in a lab in which mice navigate through two different mazes. You have
been assigned the job of determining whether one maze is more difcult (i.e. takes longer to
complete) than the oth
Question : Balloon experiment [13 marks]
Prior to 1985, the experimenter had observed that some colors of birthday balloons seem to be
harder to inflate than others. She ran this experiment to determine whether balloons of different
colors are similar in
University of Waterloo
STAT 332
Sampling and Experimental Design
Course Notes
Spring 2013
by
JOCK MACKAY
[email protected]
STEFAN STEINER
[email protected]
MATTHIAS SCHONLAU
[email protected]
Stat 332 RJ MacKay and SH Steiner, University of W
Stat 332  Assignment 05  Solutions
Remarks:
You may use any software that you like to perform your computations.
You may include output from your software in your solutions to show how you are obtaining
your results.
You must show your setup clearly,
Assumptions behind the ANOVA Ftest
1. The samples are randomly selected in an independent manner
from the t treatment populations. (This is satisfied in a CRD)
2. All t treatment groups have distributions that are
approximately normal. (Check graphically
A brief introduction to me:
STAT 332  Sampling and Experimental Design!
Michael Wallace
I
Office: M3 4114
I
Email: [email protected]
I
Facebook group: search for UW STAT 332  Winter 2017
I
Im a biostatistician
I
Interests: sports, trivia, vid
STAT 332 Assignment 3: SOLUTIONS
As always, answers worth full marks are in blue, additional comments are in red! If youre
unsure why you lost a mark, please let me know and Ill take a look :) Theres also an R file
called Assignment3Sol.R which does most
Comparing Two Treatments (Without Blocking)
If we want a pvalue for the null hypothesis of no difference
between treatments, i.e., H0 : 1 2 = 0, we construct the test
statistic

2 
q1
= 4.282
n11 + n12
and find p = P(T  4.282) = 2 1.897 105 = 3.79
STAT 332: Formula Guide (Part 2)
This document explains which formulas you are, and are not, expected to know for the
second tutorial test. Its intended to help remove most of the uncertainty you might have
about what you could be asked on an exam. This d
Comparing Two Treatments (Without Blocking)
Some things to note about the model:
Yij = + i + Rij
i = 1, 2,
j = 1, ., ni
I
can be thought of as the average response across our two
treatment groups (low and high dose), but this isnt of
interest to us.
I
Th
Experimental Plans
So far weve considered sampling, which has some particular
characteristics:
Part 2: Experimental Design
I
Observational: we obtain information through simple reading,
counting, or measurement.
I
We have a defined, finite population.
Exp
STAT 332  Sampling and Experimental Design!
Michael Wallace
I
Office: M3 4114
I
Email: [email protected]
I
Facebook group: search for UW STAT 332  Winter 2017
1
A brief introduction to me:
I
Im a biostatistician
I
Interests: sports, trivia, v
Ratio Estimation
So far weve focused on estimating an average:
Ratio Estimation
P
i
yi
Aside: a bushel is about 35.2 litres.
n
An acre is about 4,047 m2 .
Sometimes we may be interested in estimating a ratio, e.g.:
I
A farmer has a number of fields of var
STAT 332: Formula Guide (Part 1)
This document explains which formulas you are, and are not, expected to know from
the course for the final exam. Its intended to help remove most of the uncertainty you
might have about what you could be asked on an exam.
STAT 332: Survey Sampling Review
Terminology
You are expected to be able to identify these from a study
description:
Fundamentals of survey sampling:
I
I
Finite population: we want to learn about some attribute of
the population.
I
Observational unit.
I
T
# Analyzing LinkedIn survey data
# set up the data:
# faculty names (useful if we decide to plot the data)
faculty < c("AHS","Arts","Eng","Env","Math","Sci")
# total students in each faculty
population < c(2434,6661,7998,2503,6661,5374)
# sample size in
Comparing Two Treatments (Without Blocking)
We can model this as
Yij = + i + Rij
i = 1, 2,
j = 1, ., ni
where:
I
: true average improvement in visual acuity.
I
1 , 2 : treatment effect from a low or high dose of patching,
respectively.
I
Rij N(0, 2 ): exp
Clicker Test S332  28
age = age  mean(age)
summary(lm(income~age)
Call:
lm(formula = income ~ age)
Residuals:
Min
1Q
18.6185 4.6881
Median
0.3733
3Q
4.5456
Max
21.0509
Coefficients:
Estimate Std. Error t value Pr(>t)
(Intercept) 45.1400
1.1804 38.24
Clicker Test  S332  12
A study on the effects of sleep deprivation randomly selects
two groups of students. 5 students were given a proper
nights rest and 5 students were asked to stay awake prior to
a mathematics test. Their grades are given below:
Sle
Clicker Test S332  14
Call:
lm(formula = Y ~ x)
Coefficients:
Estimate Std. Error t value Pr(>t)
(Intercept)
77.500
A
18.239 5.32e05
x1
4.167
4.249
B
0.382
Residual standard error: C on 4 degrees of freedom
Multiple Rsquared: 0.1938,
Adjusted Rsquar