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Course
Catalog Description:
Sample
estimation and hypothesis testing, nonparametric analogs for t-tests,
contingency tables, simple linear regression, multiple regression, subset
selection procedures; residual, influence, and collinearity diagnostics.
Prerequisites:
STAT 221
and concurrent registration in STAT 322. (Strongly suggest concurrent
registration in STAT 212.)
Course
Goals:
This
course is covers statistical methods for modeling a dependent or response
variable as a function of independent or predictor variables. Lines Through
Dots! The classical approach is to use linear regression methods that are
elegant in theory and highly effective in practice. The fundamental
concepts of estimation, hypothesis testing, confidence intervals and
prediction will be reviewed. Regression diagnostics and model selection
will be motivated from the application of linear regression methods.
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Assignment
1, Due Friday 2 Sep: Chapter 1 #1-16
Assignment
2, Due Friday 9 Sep: Chapter 1 #17,26-32
Assignment
3, Due Friday 16 Sep: Chapter 1 #18,33
Assignment
4, Due Friday 23 Sep: Chapter 1 #19-25,34,36
Assignment
5, Due Friday 30 Sep: Chapter 1 #37-39,43-54
Assignment
6, Due Tuesday 22 Oct (Beginning of Class): Chapter 2 #10-26
Assignment
7, Due Friday 14 Oct: Chapter 2 #1-4,27-37
Assignment
8, Due Friday 21 Oct: Chapter 2 #38-43,50-52
Assignment
9, Due Friday 28 Oct: Chapter 2 #44-46,53-54,58,59-61,69-72
Assignment
10, Due Friday 4 Nov: Chapter 2 #47-49,55-58,62-68
Assignment
11, Due Friday 11 Nov: Chapter 2 #75-86
Assignment
12, Due Friday 18 Nov: In SAS and S repeat #51-58,60,62,63,66,67,69,70
Assignment
13, Due Friday 2 Dec: Chapter 3 #1-13
Assignment
14, Due Thursday 8 Dec: Chapter 3 #14-26
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Lab 1
Friday 2 Sep: Chapter 1, Questions on p. 11,12
Lab 2
Friday 9 Sep: Chapter 1 #35
Lab 3
Friday 16 Sep: Open Lab
Lab 4
Friday 23 Sep: Chapter 1 #40-42
Lab 5
Friday 30 Sep: Linear Algebra Review
Lab 6
Friday 7 Oct: Chapter 2 #5-9
Lab 7
Friday 14 Oct:
Lab 8
Friday 21 Oct: Chapter 2 “Example: Repeat Share Linear Model” in SAS &
S
Lab 9
Friday 28 Oct:
Lab
10 Friday 4 Nov: Chapter 2 #73-74
Lab
11 Friday 11 Nov:
Lab
12 Friday 18 Nov: Obtain Chapter 3 Data Sets
Lab
13 Friday (according to BYU) 22 Nov: CANCELLED
Lab
14 Friday 2 Dec:
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Chapter
1 Simulation of Power for Ho: beta1=0 (SAS or S)
Chapter
2 Magazine Ads Example (SAS
or S)
Chapter
3 Iron County Home Value Data (SAS Data Set)
Chapter
3 Home Value All Possible Subsets Compute R2 and Cp (SAS)
Chapter
3 Home Value All Possible Subsets Compute R2 and Cp But Print Best 10 (SAS)
Chapter
3 Home Value Forward Selection, Backward Elimination and Stepwise Selection
(SAS)
Chapter
3 Credit Bureau Data (SAS Data
Set)
Chapter
3 Dilemma Data (Text File)
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In keeping
with the principles of the BYU Honor Code, students are expected to be
honest in all of their academic work.
Academic honesty means, most fundamentally, that any work you
present as your own must in fact be your own work and not that of
another. Violations of this
principle may result in a failing grade in the course and additional
disciplinary action by the university.
Students
are also expected to adhere to the Dress and Grooming Standards. Adherence demonstrates respect for
yourself and others and ensures an effective learning and working
environment. It is the university’s
expectation, and my own expectation in class, that each student will abide
by all Honor Code standards. Please
call the Honor Code Office at 422-2847 if you have questions about those
standards.
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I
have done my best to design a course that will accomplish course objectives
while recognizing the workload of a three credit hour class. If you have
any concerns regarding any aspect of this course (content, workload,
evaluation, etc), please come and discuss these matters with me. If we
cannot come to a mutually agreeable solution, I will direct you to my
superiors.
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