LECTURER:
Karen Bandeen-Roche, PhD
Department of Biostatistics, Hygiene
E3624
Johns Hopkins University
Bloomberg School of Public Health
phone: 410-955-1166
fax: 410-955-0958
Office Hours: Thursday, 1:15 - 2:30 PM
LECTURES:
10:30 AM - 12:00 PM
Tuesday, Thursday
Room W4030
LABS for
review, auxiliary material questions, and help with the problem sets:
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Tuesday, |
12:15 PM - 1:00 PM |
W4030
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TEACHING ASSISTANTS/LAB INSTRUCTORS:
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Yong Chen
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Marie Thoma
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Hao Wu
OFFICE HOURS for Teaching Assistants:
- Time: 3:00 - 4:00 PM, Monday.
WEB SITE:
TEXTBOOKS:
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FEH: Harrell, F.E.
(2001), Regression Modeling Strategies, With Applications to
Linear Models, Logistic Regression, and Survival Analysis, New
York: Springer.
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SW: Weisberg S. (2005),
Applied Linear Regression, 3rd. Ed., New York: John Wiley & Sons
http://www3.interscience.wiley.com/cgi-bin/bookhome/109880490/
Suggested Supplemental
Books:
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Carroll, R. J. and Ruppert,
D. (1988), Transformation and Weighting in Regression,
New York, Chapman and Hall.
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Draper, N. R. and
Smith, H. (1998), Applied Regression Analysis, 3rd. Ed., New
York: John Wiley & Sons.
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Miller, R. G. (1986)
Beyond ANOVA, Basics of Applied Statistics, New York: John
Wiley & Sons.
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Mosteller, F. and Tukey, J. W. (1977), Data Analysis and Regression: A Second Course
in Statistics, Reading, MA: Addison-Wesley.
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Scheffe', H. (1959),
The Analysis of Variance, New York: John Wiley & Sons.
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Seber, G. A. F.
(1977), Linear Regression Analysis, New York: John Wiley &
Sons.
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Vittinghoff, E.,
Glidden, D.V., Shiboski, S.C., and McCullock, C.E. (2004),
Regression Methods in Biostatistics: Linear, Logistic,
Survival, and Repeated Measures Models, New York: Springer.
References for matrix algebra:
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Matrix Algebra Useful for
Statistics, Searle
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Matrix Algebra from a
Statistician's Perspective, Harville
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Matrix Analysis for
Statistics, Schott
GRADING
Homework assignments (4)
- In lieu of late allowance:
Homework score will be calculated using the THREE assignments
yielding the highest average score
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40% |
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Midterm (1) and Final (1) Exam
in-class - 30% for each exam |
60% |
Guaranteed grades: |
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| A = 90% on both components |
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| B = 80% on both components
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| C = 70% on both components |
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Curve may also be implemented
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There will be no extra or make-up
credit, except as may occasionally be offered on homework
assignments or exams. |
ETHICS POLICY:
Homework assignments
Please feel free to study
together and talk to one another about homework assignments. The
mutual instruction that student colleagues give each other by doing
this is among the most valuable that can be achieved. However, it is
expected that homework assignments will be implemented and written up
independently. Specifically, please do not share analytic code or
output. Please do not collaborate on write-up and interpretation.
Please do not access or use solutions from any source before your
homework assignment is submitted for grading. Thanks.
LATE POLICY:
Course requirement due
dates for the term are provided below. In general, homeworks and
tests must be submitted and taken on time to receive credit. At the
instructor's
discretion, exceptions will be made for unforeseen personal or family
health emergency or other crisis.
COMPUTING PACKAGE:
CALCULATOR:
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Basic
functions (+, -, x, ÷), logarithms and exponents, simple memory and
recall, factorial key.
REGISTER FOR COURSE
e-MAIL:
COURSE DESCRIPTION:
Biostatistics 140.653 introduces linear regression analysis
for public health science. Foundational topics include:
correlation, regression and analysis of
variance (ANOVA) models and their uses; least squares
estimation and inference for parameters; model formulation, checking
for adequacy, and interpretation; and
making predictions. Topics are introduced using simple linear
regression equations, then amplified in the context of multiple
linear regression and matrices. Techniques are introduced for:
identifying influential points; modeling variable adjustments, effect
modification, and nonlinear relationships; and identifying and
handling departures from basic mode assumptions.
COURSE OBJECTIVES:
By the end of the course a student should be familiar with:
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the
definition and interpretation of the standard linear regression
model;
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least
squares estimation of parameters;
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appropriate methods for
making inferences about model parameters, statistical assumptions
that underlie the methods, and statistical properties of estimators,
tests, prediction strategies
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methods to describe fit of
models to observed data.
The student should be able
to:
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build and fit
regression models that address specific scientific questions using
linear, polynomial, spline, and interacting relationships of multiple
predictors with outcome variables;
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models to make
inferences about direct associations, confounding, effect
modification, and statistical and scientific importance of findings;
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correctly interpret
and develop predictions from linear regression models;
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evaluate regression
analyses for quality of description, inference, and predictions.
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