BST 601 Introduction to Biostatistics
Learning Check 2
Due: September 6, 2015 (by 11:59pm no late assignments accepted)
Directions: Complete the following questions using this sheet to report your answers. Giving or
receiving help on specific problems is
BST 601 Introduction to Biostatistics
Learning Check 3
Due: September 20, 2015 (by 11:59pm no late assignments accepted)
Directions: Complete the following questions using this sheet to report your answers. Giving or
receiving help on specific problems is
BST 601 Introduction to Biostatistics
Learning Check 5
Due: October 4, 2015 (by 11:59pm no late assignments accepted)
Directions: Complete the following questions using this sheet to report your answers. Giving or
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BST 601 Introduction to Biostatistics
Learning Check 8
Due: November 1, 2015 (by 11:59pm no late assignments accepted)
Directions: Complete the following questions using this sheet to report your answers. Giving or
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BST 601 Introduction to Biostatistics
Learning Check 9
Due: November 15, 2015 (by 11:59pm no late assignments accepted)
Directions: Complete the following questions using this sheet to report your answers. Giving or
receiving help on specific problems is
BST 601 Introduction to Biostatistics
Learning Check 7
Due: October 25, 2015 (by 11:59pm no late assignments accepted)
Directions: Complete the following questions using this sheet to report your answers. Giving or
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BST 601 Introduction to Biostatistics
Learning Check 4
Due: September 27, 2015 (by 11:59pm no late assignments accepted)
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The National Center for Health Statistics conducted a national study of body mass index
(BMI) among women ages 20-29. They found t
BST 601 Introduction to Biostatistics
Learning Check 6
Due: October 18, 2015 (by 11:59pm no late assignments accepted)
Directions: Complete the following questions using this sheet to report your answers. Giving or
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Lecture 1 for BST 632: Statistical Theory II Kui Zhang, Spring 2010
Chapter 5 Properties of a Random Sample
Section 5.4 Order Statistics
Definition 5.4.1: The order statistics of a random sample X 1 , X n are the sample values placed in ascending
order. T
Homework 5 for BST 632: Statistical Theory II Problems
Due Time: 10:45AM Thursday, on 03/25/2010.
Problem 1 (10 points). Let X 1 , , X n be a random sample from the population with the pdf
f ( x | ) and T is the complete, sufficient statistic for . Prove
Homework 6 for BST 632: Statistical Theory II Problems
Due Time: 10:45AM Thursday, on 04/01/2010.
Problem 1 (15 points). Book problem 8.15. [Hint: Neyman-Pearson]
Problem 2 (10 points). Book problem 8.20.
Problem 3 (20 points). Book problem 8.38.
Problem
Homework 4 for BST 632: Statistical Theory II Problems
Due Time: 10:45AM Thursday, on 02/25/2010.
Problem 1 (20 points). Suppose X 1 and X 2 are random samples from a population the
following pmf f ( x | ) , where cfw_1, 2,3 :
x
f ( x | 1)
f ( x | 2)
f (
Homework 1 for BST 632: Statistical Theory II Problems
Due Time: 10:45AM Tuesday, on 01/26/2010.
3
Problem 1 (25 points). Let X 1 , , X n be iid with pdf f X ( x) x 2 (0 x 2) . Let
8
X (1) X ( n ) be the order statistics. Show that X (1) / X (2) , X (2) /
Homework 7 for BST 632: Statistical Theory II Problems
Due Time: Problem 1-4 -10:45AM Tuesday, on 04/20/2010.
Due time: Problem 5 - 10:45AM Tuesday, on 04/27/2010.
Problem 1 (15 points). Book problem 9.34. [Hint: use (1) the pivotal quantity
and
X
S2 / n
Lecture 1 for BST 632: Statistical Theory II Kui Zhang, Spring 2010
Chapter 5 Properties of a Random Sample
Section 5.4 Order Statistics
Definition 5.4.1: The order statistics of a random sample X 1 , , X n are the sample values placed in ascending
order.
Lecture 6 for BST 632: Statistical Theory II Kui Zhang, Spring 2010
Chapter 9 Interval Estimation
Section 9.1 Introduction
Definition 9.1.1 An interval estimate of a real-valued parameter is any pair of functions, L( x1 , xn ) and
U ( x1 , xn ) , of a sam
Lecture 7 for BST 632: Statistical Theory II Kui Zhang, Spring 2010
Chapter 5 Properties of Random Sample
Section 5.5 Convergence Concepts
Definition 5.5.1: A sequence of random variables, X 1 , X 2 , , converges in probability to a random variable X if,
Lecture 5 for BST 632: Statistical Theory II Kui Zhang, Spring 2010
Chapter 8 Hypothesis Testing
Section 8.1 Introduction
Definition 8.1.1 A hypothesis is a statement about a population parameter.
Definition 8.1.2 The two complementary hypotheses in a hyp
Lecture 4 for BST 632: Statistical Theory II Kui Zhang, Spring 2010
Chapter 7 Methods of Finding Estimators
Section 7.3 Methods of Evaluating Estimators
7.3.1 Mean Squared Error
Definition 7.3.1 The mean squared error (MSE) of an estimator W of a paramete
Lecture 2 for BST 632: Statistical Theory II Kui Zhang, Spring 2010
Chapter 6 Principles of Data Reduction
Section 6.1 Introduction
Goal: To summarize or reduce the data X 1 , X 2 , X n to get information about an unknown parameter .
Notes:
1. X denotes t
Lecture 3 for BST 632: Statistical Theory II Kui Zhang, Spring 2010
Chapter 6 Principles of Data Reduction
Section 6.3 Likelihood Principle
In his section, we study a specific, important statistic called the likelihood function that also can be used to
su
Chapter 2 for BST 695: Special Topics in Statistical Theory. Kui Zhang, 2011
Chapter 2 Transformations and Expectations
Chapter 2.1 Distributions of Functions of a Random Variable
Problem: Let X be a random variable with cdf FX ( x) . If we define any fun
Chapter 1 for BST 695: Special Topics in Statistical Theory, Kui Zhang, 2011
Chapter 1 Probability Theory
Chapter 1.1 Set Theory
Definition: A set is a collection of finite or infinite elements where ordering and multiplicity are generally ignored.
Defini
Chapter 3 for BST 695: Special Topics in Statistical Theory. Kui Zhang, 2011
Chapter 3 Common Families of Distributions
Section 3.1 - Introduction
Purpose of this Chapter: Catalog many of common statistical distributions (families of distributions that ar
Chapter 4 for BST 695: Special Topics in Statistical Theory. Kui Zhang, 2011
Chapter 4 Multiple Random Variables
Chapter 4.1 Joint and Marginal Distributions
Definition 4.1.1: An n -dimensional random vector is a function from a sample space S into n , n
Chapter 9 for BST 695: Special Topics in Statistical Theory. Kui Zhang, 2011
Chapter 9 Interval Estimation
Section 9.1 Introduction
Definition 9.1.1 An interval estimate of a real-valued parameter is any pair of functions, L( x1 , , xn ) and
U ( x1 , , xn
Chapter 10 for BST 695: Special Topics in Statistical Theory. Kui Zhang, 2011
Chapter 5 Properties of Random Sample
Section 5.5 Convergence Concepts
Definition 5.5.1: A sequence of random variables, X 1 , X 2 , , converges in probability to a random varia
Chapter 8 for BST 695: Special Topics in Statistical Theory. Kui Zhang, 2011
Chapter 8 Hypothesis Testing
Section 8.1 Introduction
Definition 8.1.1 A hypothesis is a statement about a population parameter.
Definition 8.1.2 The two complementary hypotheses
Chapter 7 for BST 695: Special Topics in Statistical Theory. Kui Zhang, 2011
Chapter 7 Methods of Finding Estimators
Section 7.1 Introduction
Definition 7.1.1 A point estimator is any function W ( X) W ( X 1 , X 2 , X n ) of a sample; that is, any statist
Chapter 6 for BST 695: Special Topics in Statistical Theory. Kui Zhang, 2011
Chapter 6 Principles of Data Reduction
Section 6.1 Introduction
Goal: To summarize or reduce the data X 1 , X 2 , , X n to get information about an unknown parameter .
Notes:
1.