Transcript Lecture 1 Outline: Thu, Sep 4

Lecture 1 Outline: Tue, Jan 13
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Introduction/Syllabus
Course outline
Some useful guidelines
Case studies 1.1.1 and 1.1.2
Course objectives
• Understand distinctions between various types of
studies (e.g., observational studies, controlled
experiments), the questions they can address and
what types of statistical methods are appropriate
for analyzing them.
• Statistical tools: two sample methods, several
sample methods (ANOVA), multiple comparisons,
simple linear regression, multiple linear regression
• Hands on experience analyzing data and
computing with data (using JMP).
• Interpretation and communication of results
Guidelines
• The lectures will be used to present the
basic ideas, illuminate key concepts, present
examples and have class discussion.
• You are responsible for both the material
presented in lecture and the reading. The
reading for each lecture is on the lecture
schedule (check web page for updates to
schedule).
Guidelines (Contd.)
• At the end of each chapter, the book
contains conceptual questions with answers
which you should use to test your
understanding of material.
• JMP-IN
– Used extensively for assignments.
– Familiarity with output needed for exams
– Recommended JMP-IN text is a good reference
Guidelines (Contd.)
• The final grade is determined based on the
assignments, midterms, final project and
final.
• Preparation for exams
– Review lectures.
– Review reading.
– Review assignments.
Guidelines (Contd.)
• Feedback on lecture style, assignments,
other aspects of course is encouraged.
• Constant interaction encouraged to better
understand the material.
• I encourage you to come see me at least
once during semester to chat about your
background, interest and concerns about the
class.
Chapter 1 topics
• Study design and its impact on what
statistical inferences can be drawn
• Measurements of statistical uncertainty (pvalues and confidence intervals)
• Graphical methods for displaying data
• Discussed in context of two sample problem
Two Sample Problems
• Compare the response variables of two groups
based on samples from the groups. Two types of
problems
– (1) Two distinct populations (e.g, the opinions of men
vs. women on President Bush’s job performance)
– (2) Two different treatments applied to one population
(e.g., the effect of taking a drug vs. a placebo on
depression).
Case Study 1.1.1: Motivation and Creativity
• Broad scientific questions of interest: Do grading
systems promote creativity in students? Do
ranking systems and awards increase productivity
among employees? Do rewards and praise
stimulate children to learn?
• Experiment: Students in a creative writing class
were randomly assigned to one of two groups.
One received an “intrinsic” and the other an
“extrinsic” questionnaire. Afterwards, they wrote
Haiku poems that were scored for creativity.
Descriptive Statistics, Graphs in
JMP
• motivcreat.JMP
• Click Analyze, Distribution. Put Score into
Y, Columns and put Group into By and
click OK.
• JMP will display for each group a histogram
and box plot, quantiles of the sample data
and summary statistics for the sample data
(mean and standard deviation).
Statistical Analysis
• Specific question of interest: Is there any evidence
that creativity scores tend to be affected by the
type of motivation (intrinsic/extrinsic) induced by
the questionnaire?
• Statistical tools for addressing this question
– Graphical methods
– Hypothesis test (p-value)
– Confidence interval
Things to Think About
• Can we infer that the difference in creativity
scores was caused by the difference in
motivational questionnaires?
• The poems were given to judges in random
order. Why was that important?
• To what extent does this experiment address
the real scientific question of interest, do
external incentives promote creativity?
Case study 1.1.2: Sex
discrimination in employment
• Legal/scientific question of interest: Did a bank
discriminatorily pay higher starting salaries to men
than women?
• The data: Starting salaries were available for all
male and female entry-level clerical employees of
the Harris bank.
• Specific question: Did male starting salaries tend
to be larger than female starting salaries? (more
so than could be explained by chance?)
Things to think about
• Can we infer that discrimination has occurred on
the basis of the evidence that men received larger
starting salaries than women?
• Suppose we also had available information about
education and experience of the employees. How
could we use this information? Would it allow us
to infer that discrimination has occurred?