LAB
INSTRUCTORS:
TEACHING ASSISTANTS:
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Yibing (Oliver) Chen
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Yu Du
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Youssef Farag
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Emily Huang
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Jordan Johns
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Shuiqing Liu
-
Yi-Chen Liu
|
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Haidong Lu
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Gina Norato
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Claire Ruberman
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Genevieve Stein-O'Brien
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Yuting Xu
-
Chao Yang
|
OFFICE HOURS for Teaching Assistants
(starting Tues, Oct 27 optional)
| Monday thru Friday |
12:15 PM - 1:15 PM, |
W2009 |
LECTURE NOTES:
-
Copies of the transparencies used in the
lectures are distributed weekly during class. Supplementary materials
will be distributed as appropriate. Purchase of these materials is
included in the registration. Copies of most materials are available
for downloading in the "Classes"
section of the course web site.
-
Version 9 or higher of Acrobat Reader
is needed for opening the course materials on the website.
Download Acrobat
Reader
WEB SITE:
http://www.biostat.jhsph.edu/courses/bio622
Userid: bio622
Password: (given in
class)
Contains course schedule,
office hours, lecture notes, self-evaluation problems, Stata lecture
notes, problem sets, quizzes, solution keys, and data sets.
AUDIO:
TEXTBOOK:
Recommended book for which we will provide reading assignments:
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Bernard Rosner, Fundamentals of Biostatistics,
2011, Duxbury Press, Belmont, CA.
Suggested book:
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Lawrence C. Hamilton.
Statistics with Stata
12, 2013, Duxbury Press,
Belmont, CA.
CALCULATOR:
Basic functions (+, -, x, ÷), logarithms and
exponents, simple memory and recall, factorial key.
COMPUTING PACKAGE:
GRADING based on:
- 5% Problem set 1 (points deducted if late)
- 5% Problem set 2 (points deducted if late)
- 5% Problem set 3 (points deducted if late)
- 5% Problem set 4 (points deducted if late)
Students may work together, but must hand in their own version of
the problems set -- DO NOT SUBMIT AN EXACT COPY of another
student's work.
- 5% Quiz 1 (via Quiz Generator)
- 5% Quiz 2 (via Quiz Generator)
- 35% Midterm examination (in class)
- 35% Final examination (in class)
COURSE OBJECTIVE:
Students who successfully master this course will be able to:
1. Use statistical reasoning to formulate public health questions in
quantitative terms:
(a) Distinguish the summary measures of association
applicable to
retrospective and prospective study designs.
(b) Distinguish between the appropriate regression models for
handling
continuous outcomes, binary outcomes and
time-to-events.
(c) Conduct an intent-to-treat statistical analysis of data
from a randomized community trial and correctly interpret the
findings about the treatment efficacy.
(d) Conduct a basic analysis of data from a cohort study and
correctly interpret the findings about the association between
exposure and outcome.
(e) Conduct a basic analysis of data from a case-control
study and correctly interpret the findings about exposure and
outcome.
(f) Use stratification in design and analysis to minimize
confounding and identify effect modification
2. Design and interpret graphical and tabular displays of
statistical information:
(a) Use the statistical analysis package Stata to construct
statistical
tables and graphs of journal quality.
3. Use probability models to describe trends and random variation
in public
health data:
(a) Distinguish among the underlying probability
distributions for modeling continuous, categorical, binary and
time-to-event data.
(b) Calculate the sample size necessary for estimating either
a continuous or binary outcome in a single group.
(c) Estimate the sample size necessary for determining a
statistically
significant difference in either a continuous or
binary outcome between two groups.
(d) Recognize the assumptions required in performing
statistical tests
assessing relationships between an outcome and
a risk factor.
4. Use statistical methods for inference, including confidence
intervals
and tests, to draw valid public health inferences from
study data:
(a) Estimate two proportions and their difference, and
confidence intervals for each. Interpret the interval estimates
within a scientific context. Recognize the importance of other
sources of uncertainty beyond those captured by the confidence
interval
(b) Estimate an odds ratio or relative and its associated
confidence interval. Explain the difference between the two and
when each is appropriate.
(c) Perform and interpret one-way analysis of variance to
test for
differences in means among three or more populations.
Evaluate whether underlying probability model assumptions are
appropriate.
(d) Contrast mean outcomes among pairwise groups using
multiple
comparisons procedures.
(e) Interpret the correlation coefficient as a measure of the
strength of
a linear association between a continuous response
variable and a
continuous predictor variable.
(f) Perform and correctly interpret the results from a simple
linear
regression analysis to describe the dependence of a
continuous response
variable on a single predictor variable.
(g) Use data transformations such as logs and square roots so
that
regression model assumptions are more nearly satisfied.
(h) Perform and correctly interpret the results from a simple
logistic
regression analysis to describe the dependence of a
dichotomous response variable on a single predictor variable.
The course is designed to enable students to develop their data
analysis skills.
Four important datasets will be analyzed by the
students using the statistical
package Stata throughout the 621-624
course sequence.
OTHER LINKS
For a better understanding of Type I and Type II errors
and their
real life applications, go to:
Central limit theorem was also expressed in a nice way here,
use
the applet on the right side of the page for variety:
Linear regression applets:
Scroll down far enough and you will find F table for alpha
= .10, .05, .025, and .01 successively
Newspaper articles
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