STAT 443: Assignment 3
(Winter 2014)
SOLUTIONS (Total = 80 marks)
This assignment is due in class on Thursday April 03. For the data analysis section,
you should hand in the R code and output, as well as your interpretations of the outputs.
You will NOT r
STAT 443: Assignment 2
(Winter 2014)
SOLUTIONS (Total : 50 marks)
This assignment is due in class on Thursday March 13. For the data analysis section,
you should hand in the R. code and output, as well as your interpretations of the outputs.
You will NOT
Stat 443: Forecasting (Winter 2014)
Assignment #3
Due date: Thursday, April 03 in class
(Print):
,
(Last name)
(First name)
UW Student ID Number:
Section (001=2:30-3:50pm , 002=4:00-5:20pm):
1
STAT 443: Assignment 3
(Winter 2014)
This assignment is due in
Stat 443: Forecasting (Winter 2014)
Assignment #2
Due date: Thursday, March 13 in class
(Print):
,
(Last name)
(First name)
UW Student ID Number:
Section (001=2:30-3:50pm , 002=4:00-5:20pm):
1
STAT 443: Assignment 2
(Winter 2014)
This assignment is due in
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STAT 443: Assignment #3
(Spring 2014)
This assignment is due in class on Wednesday July 30. For the data analysis sec
tion, you should hand in the R code and output, as well as your interpretations of the
outputs. Please make sure that
STAT 443: Assignment 1
(Spring 2014) - SOLUTIONS
(Totalr marks)
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This assignment is due in class on Wednesday June 4. For the data analysis section,
you should hand in the R code and output, as well as your interpretations of the outputs.
Yo
Stat 443: Forecasting
Assignment #3
Due date: July 25 in class
(Print):
,
(Last name)
(First name)
UW Student ID Number:
Section (001=Reza , 002=Slava):
1
STAT 443: Assignment 3
(Spring 2013)
This assignment is due in class on Thursday July 25. For the da
Stat 443: Forecasting
Assignment #2
Due date: July 02 in class
(Print):
,
(Last name)
(First name)
UW Student ID Number:
Section (001=Reza , 002=Slava):
1
STAT 443: Assignment 2
(Spring 2013)
This assignment is due in class on Tuesday July 2nd. For the da
STAT 443: Assignment 1
(Spring 2013)
This assignment is due in class on Thursday June 6th. Please make sure that you
write your name, Section (Reza=001, Slava=002) and ID number on the front
page of your assignment. For the data analysis section, you shou
STAT 443: Assignment 2
(Fall 2013) - SOLUTIONS
(total: 45 marks)
This assignment is due in class on Thursday November 7th. For the data analy-
sis section, you should hand in the R code and output, as well as your interpretations
of the outputs. You wil
Stat 443: Forecasting
Midterm - October 24th, 2013
4:005:20pm
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(Family name) (Given name)
Name (Print):
Section 001: Rezas class UW Student ID Number:
Aids: Calculator, English to other language dictionary
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Stat 443: Forecasting (Fall 2013)
Assignment #3
Due date: Thursday, November 21 in class
(Print):
,
(Last name)
(First name)
UW Student ID Number:
Section (001=Reza , 002=Surya):
1
STAT 443: Assignment 3
(Fall 2013)
This assignment is due in class on Thur
Stat 443: Forecasting (Fall 2013)
Assignment #2
Due date: Thursday, November 7th in class
(Print):
,
(Last name)
(First name)
UW Student ID Number:
Section (001=Reza , 002=Surya):
1
STAT 443: Assignment 2
(Fall 2013)
This assignment is due in class on Thu
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Stat 443: Forecasting
Test #3 November 6, 2014
8:30 9:50 am
Name (Print): ,
(Family name) (Given name)
UW Student ID Number:
Aidsz. Calculator, English to other language dictionary
M
QUESTION MARK
1. Your exam has 9 pages (including this co
Stat 4433: Forecasting
Test #1 - September 25, 2014
5:30 - 6:20 pm
Name (Print):
(Family name) (Given name)
UW Student ID Number:
Aids: Calculator, English to other language dictionary
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i. Your exam has 7 pages (including this cover sheet). 011
STAT 443 Final Exam Review
A
L TEXer:
1
W. Kong
Basic Denitions
2
Denition 1.1. The time series cfw_Xt with E[Xt ] < is said to be weakly stationary if:
1. X (t) = E[Xt ] is independent of t
2. X (t, t + h) = Cov(Xt , Xt+h ) is independent of t for all h
40
Chapter 1
Introduction
W
Problems
(e) The sample value of the rank statistic P is 10310, and the asymptotic dis-
tribution under the iid hypothesis (with n. = 700) is N(9950, 2.239 x 105). Thus
I? m up I/Up = 0.76, corresponding to a computed pvalue of
Solutions to selected problems in
Brockwell and Davis
Anna Carlsund
Henrik Hult
Spring 2003
This document contains solutions to selected problems in
Peter J. Brockwell and Richard A. Davis, Introduction to Time Series and Forecasting, 2nd Edition, Springe
Multivariate Normal Distribution
1
1
(det ) 2
(2)n/2
exp 1 (X )T 1 (X ) , where X = (X1 , ., Xn )T .
2
If X = (X1 , X2 ) M W N (, ), where = (1 , 2 ) and
X1 |X2 = x2 N 1 +
12
2 (x2
2
2
2 ) , 1
2
1 12
2
12 2
, then
12
2
2
Regression
If the model is: Yi =