Transcript Materials

‘The’ Second Course in
Statistics
Robin Lock, Burry Professor of Statistics
St. Lawrence University, [email protected]
Dick De Veaux, Williams College
[email protected]
Breakout Session at USCOTS07
Positioning the “Second” Course
(in the “good old days”)
Calculus I
Calculus II
Calculus III
Intro Stat
Linear Alg.
Probability
Math Stat
Current Consensus
Intro Stat
?????
AP Stat
GAISE: http://www.amstat.org/education/gaise/
AP: http://apcentral.collegeboard.com
What (could be, should be, might be, is)
the (or a) second course in statistics?
Mathematics
Proofs
One Variable
Calculus
MultiVariable
Calculus
Linear Alg.
Discrete
Diff. Eq’s
Economics
Financial
Micro
Intro
Economics
Industrial
Macro
Labor
Public
Chemistry
General
Organic
Physical
Psychology
Behavioral
I
N
T
R
O
Developmental
Social
Physiological
Abnormal
Borrowing from Other Disciplines
Math: In the second course, repeat the topics in the
first course in a multivariable setting
Stat: Multiple regression, multi-factor ANOVA, …
Economics: Have two main second courses that
build on the ideas of the first course
Stat: Experimental Design & Applied Regression
Borrowing from Other Disciplines
Chemistry: Develop a single sequence
Stat: Might require redesign of intro
Psychology: Have lots of “second” courses that
expand on different aspects of the first course.
Stat: Experimental Design, Sampling, Applied
Regression, Nonparametric Methods, Categorical
Unique to Statistics
Modeling approach:
Data
Response
Variable
=
=
Model
+
Error
F(Predictors & Explanatory factors)
+
“Unexplained” Variability
Categorical vs. Quantitative Approach
Predictor(s)
Quantitative Categorical
Quantitative
Response
Categorical
Multiple
Regression
Multifactor
ANOVA
Logistic
Regression
Loglinear
Models
Data Production Approach
Experimental Design
Expand on designs from intro
Randomized, factorial, block, …
Analyzing designed data
Sampling
Beyond the SRS
Cluster, stratified, …
Estimates based on sample method
What if the Standard Method Doesn’t Work?
Approach
Data not normal?
Nonparametric methods
Bootstrap CI’s and Permutation Tests
Transformations
Errors not independent?
Time series analysis
Questions?
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How do we attract students to take Stat II?
What should we assume from Stat I?
What about software/technology?
Can we still use activities and explorations?
Do we have to assume more math background?
Is there a good textbook?
• What will students do after Stat II?
Create Your Own Stat II
Divide into groups - perhaps by similar
institutions or ideas for a second course
Develop a syllabus for a second course – by
consensus or with minority reports
Assume A GAISE compliant Stat I
Prepare to report back to the rest of the group.