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All Educational Materials for STA457H1 at University of Toronto St. George (UTSG)

UTSGSTA457H1Zhou ZhouSummer

Sta457 Chapter 5 Collection Very Good Notes collection taken in Class

OC24 Page
4 Oct 2011
37
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UTSGSTA457H1Jen Wen LinSummer

STA457H1 Study Guide - Final Guide: Time Series, Unit Root, Trend Stationary

OC25319918 Page
21 Oct 2015
82
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UTSGSTA457H1WilliamFall

STA457H1 Study Guide - Net.

OC590852 Page
20 Nov 2012
19
Sii 199 u of t seminar class - time use note 3 oct 16, 2012. One can always hypothesis is that men and woman would differ greatly in the extent of thei
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UTSGSTA457H1N/ AWinter

summary

OC682817 Page
27 Apr 2011
37
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UTSGSTA457H1Jen Wen LinSummer

STA457H1 Study Guide - Final Guide: Time Series, Null Hypothesis, Essive Case

OC2531992 Page
21 Oct 2015
41
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UTSGSTA457H1N/ ASummer

Summary notes

OC33417 Page
12 Sep 2010
70
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UTSGSTA255H1Shivon Sue-CheeWinter

STA255H1 Lecture Notes - Lecture 1: Fair Coin, Bayes Estimator

OC23345571 Page
16 Jul 2019
0
Instructions: this quiz is open book; it is worth 3 points and you have 8 minutes to complete it. No electronic devices with possible internet access,
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UTSGSTA255H1Shivon Sue-CheeWinter

STA255H1 Lecture 2: quiz1_255_w19_soln

OC23345571 Page
16 Jul 2019
0
Instructions: this is an open-book quiz; it is worth 3 points and you have 8 minutes to complete it. No electronic devices with possible internet acces
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UTSGSTA255H1Shivon Sue-CheeWinter

STA255H1 Study Guide - Quiz Guide: Sampling Distribution, Standard Deviation

OC23345571 Page
16 Jul 2019
0
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UTSGSTA255H1Shivon Sue-CheeWinter

STA255H1 Study Guide - Quiz Guide: Null Hypothesis, Alternative Hypothesis, Scatter Plot

OC23345571 Page
16 Jul 2019
0
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UTSGSTA255H1Shivon Sue-CheeWinter

STA255H1 Quiz: quiz8_255_w19_Solutions

OC23345571 Page
16 Jul 2019
0
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UTSGSTA255H1Shivon Sue-CheeWinter

STA255H1 Lecture Notes - Lecture 4: Bernoulli Distribution

OC23345571 Page
16 Jul 2019
0
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UTSGSTA255H1Shivon Sue-CheeWinter

STA255H1 Study Guide - Quiz Guide: Scotiabank, Frequency Distribution, Standard Deviation

OC23345571 Page
16 Jul 2019
0
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UTSGSTA255H1Shivon Sue-CheeWinter

STA255H1 Study Guide - Quiz Guide: Exponential Distribution

OC23345571 Page
16 Jul 2019
0
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UTSGSTA255H1Shivon Sue-CheeWinter

STA255H1 Lecture Notes - Lecture 3: Tim Hortons

OC23345571 Page
16 Jul 2019
0
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UTSGSTA255H1Shivon Sue-CheeWinter

STA255H1 Study Guide - Quiz Guide: Likelihood Function, Partition Coefficient

OC23345571 Page
16 Jul 2019
0
Instructions: this quiz is open book; it is worth 3 points and you have 8 minutes to complete it. No electronic devices with possible internet access,
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UTSGSTA457H1allFall

STA457:2202 Practices Questions

OC820346 Page
13 Dec 2012
80
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UTSGSTA457H1Zhou ZhouWinter

2.4 Properties of mu and rho.docx

OC1415064 Page
6 Mar 2014
30
X i)= n[n x (0)+2 j=1 i=1 j=1. X( i j )=n x (0)+2 (n 1) x (1)+2( n 2) x (2)+ +2 x (n 1 ) n n j=1 i=1. Cov (xi , x j)= (n j) x ( j)]=lim n [ x(0)+2 n 1
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UTSGSTA457H1Zhou ZhouWinter

2.5 Forecasting (update).docx

OC1415069 Page
12 Mar 2014
38
We want to find the best linear combination a0+a1w 1+a2w 2+ +an w n error (mse) of e[(u (a0+a1 w 1+a2 w 2+ +anw n))2] is minimized such that the mean s
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UTSGSTA457H1Zhou ZhouWinter

STA457H1 Lecture Notes - Lipschitz Continuity, Lag Operator, Polynomial

OC1415064 Page
3 Feb 2014
35
X(h) = cov[xt, xt+h] correlation function: x(h) = corr[xt, xt+h] = cov[xt, xt+h]/ var(xt)var(xt+h) = (h)/ (0) (0) = | (h)| (0) for any h: (h) = (-h) fo
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UTSGSTA457H1Zhou ZhouWinter

STA457H1 Lecture Notes - Time Series, Independent And Identically Distributed Random Variables

OC1415061 Page
14 Jan 2014
19
A sequence (maybe multivariate) which is naturally ordered in time is called a time series. {xt} to denote time series t can be continuous or discrete.
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UTSGSTA457H1Zhou ZhouWinter

2.5 Forecasting.docx

OC1415062 Page
6 Mar 2014
33
We want to find the best linear combination a0+a1w 1+a2w 2+ +an w n error (mse) of e[(u (a0+a1 w 1+a2 w 2+ +anw n))2] is minimized such that the mean s
View Document
UTSGSTA457H1Zhou ZhouWinter

STA457H1 Lecture Notes - Null Hypothesis, Test Statistic, Time Series

OC1415064 Page
3 Feb 2014
23
White noise: white noise process may not be independent. Let x1 = y1, x2 = y1y2, , xn = y1y2 yn. It is easy to see that {xi} is not independent process
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UTSGSTA457H1MichelsonFall

STA457H1 Lecture Notes - Lecture 8: Observer-Expectancy Effect

OC591191 Page
27 Nov 2012
39
The researchers got the sample of men by asking single men if they wanted to partake in a study about kitchen use. They were also offered a re- ward, a
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UTSGSTA457H1Zhou ZhouWinter

STA457H1 Lecture Notes - Time Series, Mean Squared Error, Multivariate Random Variable

OC1415065 Page
6 Feb 2014
28
* forecasting with optimality: the case of normal time series. An easy-to-implement criterion for forecasting xn+1 is to minimize the expected squared
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UTSGSTA457H1Zhou ZhouWinter

Sta457 Chapter 1 Collection Very Good Notes collection taken in Class

OC26 Page
4 Oct 2011
203
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UTSGSTA457H1Zhou ZhouWinter

Sta457 Chapter 2 Collection Very Good Notes collection taken in Class

OC210 Page
4 Oct 2011
92
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UTSGSTA457H1Zhou ZhouWinter

STA457H1 Chapter Notes -If And Only If

OC14150618 Page
6 Mar 2014
47
X (r ,s)=cov[ x r , xs]=e[( x r x (r))(x s x (s))] r , s is. X (t ,t+h) is independent of t for each h. Note: x (h)= x (h ,0)= x (t ,t +h) X (h)=cov [
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UTSGSTA457H1Zhou ZhouWinter

STA457H1 Chapter Notes - Chapter 4: Spectral Density, Stater

OC26 Page
4 Oct 2011
47
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UTSGSTA457H1Zhou ZhouWinter

Sta457 Chapter 3 Collection Very Good Notes collection taken in Class

OC26 Page
4 Oct 2011
34
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UTSGSTA457H1Zhou ZhouWinter

Chapter 3.docx

OC1415066 Page
8 Apr 2014
49
{xt} is called an arma ( p,q) process ( p 0,q 0) if. X t 1 x t 1 p x t p=zt+ 1 zt 1+ + q zt q( ) ( b)=1 1 b 2 b2 p b p. ( z)=1 1 z p z p 0 z z , z =1.
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UTSGSTA457H1Zhou ZhouWinter

STA457H1 Chapter Notes - Chapter 6: Autoregressive Integrated Moving Average

OC1415062 Page
8 Apr 2014
32
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UTSGSTA457H1Zhou ZhouWinter

STA457H1 Chapter Notes - Chapter 5: Asparagine

OC14150612 Page
8 Apr 2014
32
X t 1 x t 1 p x t p=zt( ) Multiply x t 1, x t 2, , xt p. E[ xt xt i] 1 e[ xt 1 x t i] p e[ x t p xt i]=e[z t x t i] and take expectation to i=1, , p. I
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Your classmates’ favorite documents.
UTSGSTA457H1Jen Wen LinSummer

STA457H1 Study Guide - Final Guide: Time Series, Null Hypothesis, Essive Case

OC2531992 Page
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UTSGSTA457H1Jen Wen LinSummer

STA457H1 Study Guide - Final Guide: Time Series, Unit Root, Trend Stationary

OC25319918 Page
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UTSGSTA457H1Zhou ZhouWinter

Sta457 Chapter 1 Collection Very Good Notes collection taken in Class

OC26 Page
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UTSGSTA457H1Zhou ZhouSummer

Sta457 Chapter 5 Collection Very Good Notes collection taken in Class

OC24 Page
4 Oct 2011
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UTSGSTA457H1N/ ASummer

Summary notes

OC33417 Page
12 Sep 2010
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UTSGSTA457H1allFall

STA457:2202 Practices Questions

OC820346 Page
13 Dec 2012
80
View Document
UTSGSTA457H1Zhou ZhouWinter

Sta457 Chapter 2 Collection Very Good Notes collection taken in Class

OC210 Page
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View Document
UTSGSTA457H1Zhou ZhouWinter

STA457H1 Chapter Notes -If And Only If

OC14150618 Page
6 Mar 2014
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X (r ,s)=cov[ x r , xs]=e[( x r x (r))(x s x (s))] r , s is. X (t ,t+h) is independent of t for each h. Note: x (h)= x (h ,0)= x (t ,t +h) X (h)=cov [
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UTSGSTA457H1N/ AWinter

summary

OC682817 Page
27 Apr 2011
37
View Document
UTSGSTA457H1Zhou ZhouWinter

STA457H1 Chapter Notes - Chapter 4: Spectral Density, Stater

OC26 Page
4 Oct 2011
47
View Document

Most Recent

The latest uploaded documents.
UTSGSTA457H1Jen Wen LinSummer

STA457H1 Study Guide - Final Guide: Time Series, Null Hypothesis, Essive Case

OC2531992 Page
21 Oct 2015
41
View Document
UTSGSTA457H1Jen Wen LinSummer

STA457H1 Study Guide - Final Guide: Time Series, Unit Root, Trend Stationary

OC25319918 Page
21 Oct 2015
82
View Document
UTSGSTA457H1Zhou ZhouWinter

STA457H1 Chapter Notes - Chapter 6: Autoregressive Integrated Moving Average

OC1415062 Page
8 Apr 2014
32
View Document
UTSGSTA457H1Zhou ZhouWinter

Chapter 3.docx

OC1415066 Page
8 Apr 2014
49
{xt} is called an arma ( p,q) process ( p 0,q 0) if. X t 1 x t 1 p x t p=zt+ 1 zt 1+ + q zt q( ) ( b)=1 1 b 2 b2 p b p. ( z)=1 1 z p z p 0 z z , z =1.
View Document
UTSGSTA457H1Zhou ZhouWinter

STA457H1 Chapter Notes - Chapter 5: Asparagine

OC14150612 Page
8 Apr 2014
32
X t 1 x t 1 p x t p=zt( ) Multiply x t 1, x t 2, , xt p. E[ xt xt i] 1 e[ xt 1 x t i] p e[ x t p xt i]=e[z t x t i] and take expectation to i=1, , p. I
View Document
UTSGSTA457H1Zhou ZhouWinter

2.5 Forecasting (update).docx

OC1415069 Page
12 Mar 2014
38
We want to find the best linear combination a0+a1w 1+a2w 2+ +an w n error (mse) of e[(u (a0+a1 w 1+a2 w 2+ +anw n))2] is minimized such that the mean s
View Document
UTSGSTA457H1Zhou ZhouWinter

STA457H1 Chapter Notes -If And Only If

OC14150618 Page
6 Mar 2014
47
X (r ,s)=cov[ x r , xs]=e[( x r x (r))(x s x (s))] r , s is. X (t ,t+h) is independent of t for each h. Note: x (h)= x (h ,0)= x (t ,t +h) X (h)=cov [
View Document
UTSGSTA457H1Zhou ZhouWinter

2.4 Properties of mu and rho.docx

OC1415064 Page
6 Mar 2014
30
X i)= n[n x (0)+2 j=1 i=1 j=1. X( i j )=n x (0)+2 (n 1) x (1)+2( n 2) x (2)+ +2 x (n 1 ) n n j=1 i=1. Cov (xi , x j)= (n j) x ( j)]=lim n [ x(0)+2 n 1
View Document
UTSGSTA457H1Zhou ZhouWinter

2.5 Forecasting.docx

OC1415062 Page
6 Mar 2014
33
We want to find the best linear combination a0+a1w 1+a2w 2+ +an w n error (mse) of e[(u (a0+a1 w 1+a2 w 2+ +anw n))2] is minimized such that the mean s
View Document
UTSGSTA457H1Zhou ZhouWinter

STA457H1 Lecture Notes - Time Series, Mean Squared Error, Multivariate Random Variable

OC1415065 Page
6 Feb 2014
28
* forecasting with optimality: the case of normal time series. An easy-to-implement criterion for forecasting xn+1 is to minimize the expected squared
View Document

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