STAT:3510 Biostatistics
Fall 2016
Instructor: M. Larson
HOMEWORK 2
Homework 2 (hand-in on 09/07/16)
Put your name, Homework 2, and discussion section/time at the top of the first page of your
homework. Answer the questions neatly and in order. Use complet
STAT:3510 Biostatistics
Fall 2016
Instructor: M. Larson
HOMEWORK 9
Homework 9 (hand-in on 11/02/16)
Put your name, Homework 9, and lecture and/or discussion section/time on the top of the first page.
Homework is to be stapled and defuzzed.
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STAT:3510 Biostatistics
Fall 2016
Instructor: M. Larson
HOMEWORK 4
Homework 4 (hand-in on 09/21/16)
Put your name, Homework 4, and discussion section/time at the top of the first page.
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Problem #1
Assume that 75% of
STAT:3510 Biostatistics
Fall 2016
Instructor: M. Larson
HOMEWORK 3
Homework 3 (hand-in on 09/14/16)
Put your Name, Homework 3, and discussion section/time at the top of the first page.
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Problem #1
For each part of th
STAT:3510 Biostatistics
Fall 2016
Instructor: M. Larson
HOMEWORK 5
Homework 5 (hand-in on 09/28/16)
Put your name, Homework 5, and lecture and/or discussion section/time on the top of the first page.
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Problem #1
A wi
STAT:3510 Biostatistics
Fall 2016
Instructor: M. Larson
HOMEWORK 10
Homework 10 (hand-in on 11/16/16)
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STAT:3510 Biostatistics
Fall 2016
Instructor: M. Larson
HOMEWORK 6
Homework 5 (hand-in on 10/12/16)
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Problem #1
The
STAT:3510 Biostatistics
Fall 2016
Instructor: M. Larson
HOMEWORK 8
Homework 8 (hand-in on 10/26/16)
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STAT:3510 Biostatistics
Fall 2016
Instructor: M. Larson
HOMEWORK 7
Homework 5 (hand-in on 10/19/16)
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STAT:3510 Biostatistics
Fall 2016
Instructor: M. Larson
HOMEWORK 1
Homework 1 (hand-in on 08/31/16)
Put your name, Homework 1, and discussion section/time at the top of the first page of your
homework. Answer the questions neatly and in order. Use complet
Survival Data Analysis (BIOS 7210)
Breheny
Assignment 3
Due: Thursday, September 17
1. [KP 1.8] Consider solving for the nonparametric MLE of S(t) with an imposed constraint that for
some time b > t1 , S(b) = c.
(a) Show that, under this constraint, the n
Survival Data Analysis (BIOS 7210)
Breheny
Assignment 4
Due: Tuesday, September 29
P
P
1. Show that i ti = j uj , where ti is the time on study for individual i and uj is the jth normalized
spacing, uj = (n j + 1)(T(j) T(j1) ).
2. Under the setup we intro
Survival Data Analysis (BIOS 7210)
Breheny
Assignment 1
Due: Thursday, September 3
1. Suppose
T is a continuous, nonnegative random variable and that ET exists. Show that ET =
R
S(u)du.
0
2. Suppose T1 , T2 , . . . , Tn are independent, continuous, nonneg
Survival Data Analysis (BIOS 7210)
Breheny
Assignment 2
Due: Thursday, September 10
1. In class, we looked at the hazard function of the exponential distribution. Consider the hazard
function for the gamma distribution.
(a) Choose a few different shape pa
1. The main difference between qualitative and quantitative data is that quantitative data is numerical,
while qualitative data is not. An example of quantitative data would be something like heart rate or
blood pressure. An example of qualitative data wo
Thomas Rashid
BIOS:4120
September 27, 2016
Homework 5
Problem 12
a.) The probability a newborn infant will live to see his or her 5th birthday is
98,978/100,000=0.98978.
b.) The probability a 60 year old will survive for the next ten years is 72,063/85,99
Thomas Rashid
BIOS:4120:0A02
Homework 1
August 25, 2016
1. sum(c(1:500)*2)
2. The difference is for a->b, the value for the variable a is being inserted for the value
for variable b, while a<-b has the value for b being inserted into the variable a.
For e
Rate= #/time.*both numerator and denominator important*In terms of outcomes per 100 or 1000 or 10,000*
Morbidity Rate= # of new cases in a given time frame/total # of people who live in that country in that time frame
Mortality Rate= # people who die duri
CHAPTER 7
Discrete Random Variable- may assume only specific numeric values (integers). Probability is p(x). P(A|B)=P(A)/P(B)
Continuous Random Variable- may assume any value over some interval
Probability density function of a continuous random variable
Chapter 15: Contingency Tables
2 x 2 Tables- drawing samples from 2 populations and measuring a binary variable on each of the subjects in the two samples
Test of homogeneity- testing the hypothesis that the probability of a success is the same for the fi
12 :
CI on
H o : 12= o
If n1=n2
2
2
1 2
Case III:
use test statistic:
then
both unknown
Use df:
CI on
12 :
Test Statistic:
2
2
1= 2
2 2
1- if s 1 /s 2 > 3 or < 1/3 assume
2
2
2
2
2- do F test of H o : 1= 2 vs H A : 1 2
Deciding if
F- Test: (table A5):
10.5 Hypothesis Test for a Population Mean
Hypothesis testing is concerned with testing conjectures about population parameters.
An objective framework for making decisions using probabilistic methods.
-Example 10.7 Simple Hypothesis Test
A current area
STAT:3510 Biostatistics
Spring 2016
Instructor: Dr. Larson
Inferences About Means
10.1 Objectives
Distinguish between Estimation and Testing
Specify the sampling distribution of a sample mean using a Students t distribution
Specify the standard error f
STAT:3510 Biostatistics
Spring 2016
Instructor: Dr. Larson
The Normal Distribution
7.1 Objectives
Sketch and label a Normal distribution
Understand and state the Empirical Rule
Sketch and label pictures for areas and probabilities
Calculate and interp
STAT:3510 Biostatistics
Spring 2016
Instructor: Dr. Larson
Producing Data
9.1 Objectives
Distinguish between an experiment and an observational study
Distinguish between random selection and random assignment
Define bias
Define and understand differen
STAT:3510 Biostatistics
Spring 2016
Instructor: Dr. Larson
The Central Limit Theorem
8.1 Objectives
Understand and classify: population vs. sample
Understand and classify: parameter vs. statistic
Define a sampling distribution
Understand and apply the
STAT:3510 Biostatistics
Spring 2015
Instructor: Dr. Larson
Quantitative Data Displays
2.1 Objectives
Identify Quantitative Data
Create visual displays of quantitative data: Histogram, Stem-and-Leaf Plot, Dotplot
Describe a distribution for a quantitati
4.8 The Binomial Model
The binomial distribution is a frequently used probability model. The random variable of interest,
X, is the number of successes in exactly n independent trials each of which yields success with
probability p. The parameters of the
STAT:3510 Biostatistics
Spring 2015
Instructor: Dr. Larson
Introduction and Categorical Data
1.1 Objectives
Define Statistics and Biostatistics
Define Data
Classify data by type
Categorical Data
o Summarize and compare using numerical techniques
Coun
STAT:3510 Biostatistics
Spring 2016
Instructor: Dr. Larson
Random Variables and Discrete Probability Models
4.1 Objectives
Define Random variables and probability distributions
Create probability distributions/models for discrete random variables
Compu