Transcript Intro Lecture

STAT 1301
Introduction to Statistics
Wayne Woodward
Office: 143 Heroy
Phone: 768-2457
Hours: 2:00-3:00 W
3:00-4:00 Th
2:00-3:00 F
others by appointment
Text: Statistics, by Freedman, Pisani,
and Purves (3rd edition)
Lecture Sessions
a short quiz will be given at the beginning
of each lecture session over the reading
assignment for that day
– these will start on Thursday, January 20
counts 10% of total grade
(lowest 3 quiz grades dropped)
Homework
(for the previous week) will be taken up at
beginning of lab on Monday
counts 10% of grade
(2 lowest scores will be dropped)
Monday Lab Sections
meet every Monday
– TA: Julia Kozlitina
Tracy Xu
lab project
counts 10% of grade
(2 lowest scores will be dropped)
NOTE:
– first lab is Monday – January 24
(no lab this Monday -- Martin Luther King Day)
Major Exams
3 Hour Exams
- (lowest exam score dropped)
- count 40% of grade
Final Exam
- cumulative
- counts 25% of grade
NOTE: Special arrangements can be
made for students with learning
disabilities
Project
Internet-based assignment using
statistics web sites
Counts 5% of your grade
Course Packs
Course Packs for this class can be
picked up at the bookstore.
Includes:
 Reading Handouts
 Homework Handouts
 Lab Assignments
 Lecture Handouts
 Practice Exams
IMPORTANT
NO late work will be accepted
NO make-ups will be given for
- hour exams
- daily quizzes
- lab projects
Dates to Remember
January 19 -- Last day to Add/Drop
March 14-18 -- Spring Break
 April 4 -- Last day to drop a course
TIPS
always attend class
- lecture
- Monday labs
keep up
- reading
- homework
treat college like an 8 to 5 job
Math Fundamentals
(Quantitative Reasoning)
Literacy
– usual context: reading
Quantitative Literacy (“numeracy”)
– increasingly important in our society
– H.G. Wells: “The ability to think statistically will
someday be as important to a good citizen as the
ability to read and write.”
– most people have inadequate “numeracy” skills
In this course we will learn to deal critically
with data and numerical arguments
– develop the ability to apply statistical ideas to
“real world” situations
Elements of Statistics
collecting quantitative information
– surveys, experiments, etc.
describing and presenting data
– data summaries, graphs, etc.
drawing conclusions
– Statistics is the science of decision
making in the face of uncertainty
Examples:
Data Collection Issues
– Bias
- How was the question asked?
– Well-designed study or anecdotal
evidence?
- How were subjects selected?
- Placebo used?
Interpretation
– Simpson’s Paradox
Should Government Spending
for Public Television be Cut?
2 questions on a poll of 1031 people
(1) “I would be disappointed if Congress cut its
funding for public television.”
(2) “Cuts in funding for public television are justified
as part of an overall effort to reduce federal
spending.”
% saying cuts ok: (1) 40%
(2) 52%
A placebo might be the cure
Knight-Ridder Wire
Everybody
knows
what
a
placebo is: A phony pill the
doctors give to hypochondriacs
to make them think they’re
getting medical treatment. All
too true, but according to recent
studies, the deceptive little
sugar pills work pretty well on
people who are ill, too.
Science digest reports that
expectations and belief play so
important a part in making a
person
well
that
patients
suffering from very real disabilities
such as
coughs,
colds,
ulcers, seasickness, vertigo, and
hay fever have enjoyed improvement in their conditions, and
sometimes have been cured
when treated with placebos.
Other research indicates that
placebos can at times constrict
the pupil of the eye, alter the
blood pressure, change the
heart or respiratory rates,
influence, digestion, change
body temp-erature, and even
effect fat levels in the blood and
the number of certain types of
blood cells.
Gender Bias - Graduate Division, UC Berkeley

8442 men applied, 44% were admitted
 4321 women applied, 35% were admitted
Is it correct to say the university prefers men to
women?
Major
A
B
C
D
E
F
MEN
Number of Percent
applicants admitted
825
62
560
63
325
37
417
33
191
28
373
6
WOMEN
Number of Percent
applicants admitted
108
82
25
68
593
34
375
35
393
24
341
7
More Extreme -Hypothetical Example
What’s the
point?
Admit Deny Percent
Mathematics
Males
Females
English
Males
Females
Combined
Males
Females
80
9
1
20
81
29
20
1
9
80
29
81
80%
90%
10%
20%
74%
26%
Combined -- university
seems to favor men.
However, for each dept.% male applicants
admitted is less than that
for females.
Gender is confounded
with major.
(Simpson’s Paradox)
Uses of Statistics
Government
Census Bureau, Food and Drug Administration
Business
Market Research, Economic Forecasting, Quality Control
Social Sciences
Psychological Testing, Housing Quality, Educational Effectiveness
Medicine
Cancer Research, Drug Trials, fMRI
Natural Sciences
Global Warming, Nuclear Monitoring, Air Quality
Law
Employment Discrimination Cases, Tax Audits