/*
The test is scheduled on Thursday (Oct 24) 10:1012:00pm.
The students are divided into 3 groups alphabetically to take the test in different locations:
(i) from Maira Abdukarimov to SouYoung Park in MC102
(ii) from Amipreet Patle to Yu Wu in BL205
This Book is exactly the same as
Applied Linear Statistical Models
Kutner, Nachtsheim, Neter, Li
2005
5th edition
Actually, for ALRM4e
 chapters 114 are exactly the same (wordforword)
except that ALSM5e has 16 extra chapters!
as ALSM5e.
Plus the AL
Rainfall Example
The data set contains cord yield (bushes per acre) and rainfall
(inches) in six US cornproducing states (Iowa, Nebraska, Illinois,
Indiana, Missouri and Ohio).
Straight line model is not adequate up to 12 rainfall yield
increases and t
University of Toronto
Department of Statistics
STA302H1F / 1001 HF  Fall 2012
Term Test  Solution
October 23, 2012
Last Name:_Solution_
First Name:_
Student Number:_
Enrolled in (Circle one):
STA302
STA1001
Instructions:
Time: 1 hour and 50 minutes.
No
UNIVERSITY OF TORONTO
FACULTY OF ARTS & SCIENCE
JUNE FINAL EXAM, 2013
STA302H1 F / STA1001H F
DURATION: 3 Hours
AIDS ALLOWED: Scientific Calculator
LAST NAME: _ FIRST NAME: _
STUDENT NUMBER: _
ENROLLED IN:
STA 302 STA 1001
Instructions:
1.
2.
3.
4.
5.
6.
STA 302 / 1001 Summer 2014
Term Test #1
LAST NAME : _
STUDENT # :
FIRST NAME: _
_
STA 302
ENROLLED IN (tick one):
STA 1001
INSTRUCTIONS:
Time: 90 minutes
Aids allowed: calculator
A tdistribution table is provided on the last page
There are 3 questi
STA 302 / 1001 Summer 2013
Term Test
May 29, 2013
LAST NAME : _
STUDENT # :
FIRST NAME: _
_
STA 302
ENROLLED IN (tick one):
STA 1001
INSTRUCTIONS:
Time: 100 minutes
Aids allowed: calculator and dictionary
A tdistribution table is provided on the las
STA 302/1001
Summer 2015
Midterm
6/1/2015
Time Limit: 1h 50 min
Last Name (Print):
First Name:
Student Number:
Check one: STA302
STA1001
This exam contains 8 pages (including this cover page) and 3 problems. Check to see if any pages
are missing. Enter al
Chapter 1
Introduction
1.1
Building Valid Models
This book focuses on tools and techniques for building valid regression models
for realworld data. We shall see that a key step in any regression analysis is
assessing the validity of the given model. When
STA 302 / 1001 Fall 2014
Term Test Solutions
LAST NAME : _tions_
STUDENT # :
FIRST NAME: _Solu_
_
STA 302
ENROLLED IN (tick one):
STA 1001
INSTRUCTIONS:
Time: 100 minutes
Aids allowed: calculator
A tdistribution table is provided on the last page
T
Chapter 7
Variable Selection
In this chapter we consider methods for choosing the best model from a class of
multiple regression models using what are called variable selection methods.
Interestingly, while there is little agreement on how to define best,
STA 302 / 1001
Lecture 1
1
About me
Craig Burkett
Formerly an aerospace engineer, then a
highschool teacher
Now a Lecturer at UBC and a statistical
consultant
2
Course Outline
3
Important Dates
Sun
Wed
Thu
Fri
Sat
12
13
14
15
16
17
18
20
no class
21
22
2
exceeds 10 here, we use test statistic (2.101):
_ .895,/12 — 2
_ ./1—(.895)2
For a = .01, We require t(.995; 10) = 3.169. Since t*l = 6.34 > 3.169, we conclude Ha,
that there is an association between population size and per capita expenditures for the f
Chapter 2
Simple Linear Regression
2.1
Introduction and Least Squares Estimates
Regression analysis is a method for investigating the functional relationship among
variables. In this chapter we consider problems involving modeling the relationship
between
172 Part One Simple Linear Regression
Cited 4.1.
References 1:
4.4.
Problems 4.1
4.2.
*4.3.
224.4.
4.5.
%‘4.6.
*4.7.
Miller, R. G., Jr. Simultaneous Statistical Inference. 2nd ed. New York: Springer—Verlag, 1991.
Fuller, W. A. Measurement Error Models
Chapter 4
Other topics in SLR:
Simultaneous Inference
Regression through the Origin
Choice of Levels
Measurement Error
1
Joint Inference for b0, b1
We know how to build individual (1)
confidence intervals (CIs) for 0, 1
b0 t1 /2;n 2 s cfw_b0 & b1 t1 /2;
Chapter 5
Matrix approach to SLR
1
Matrix Approach to Linear
Regression
Before we take on multiple linear regression,
we look at simple regression from a matrix
perspective
Allows for general analysis of linear models
A matrix is a rectangular array of el
STA302/1001  Methods of Data Analysis I
(Week 04 lecture notes)
Wei (Becky) Lin
Oct. 03, 2016
1/53
Last Week
Review of distribution theory.
Inference for SLR.
Interval estimation of mean response.
Prediction interval.
Difference between prediction interv
STA302/1001  Methods of Data Analysis I
(Week 09 lecture notes)
Wei (Becky) Lin
Nov. 07, 2016
1/39
Last Week
Review on matrices
Simple linear regression model in matrix form.
Yi = 0 + 1 Xi + i , i = 1, . . . , n
Yn1 = Xn2 21 + n1
That is
Y1
1
Y2 1
STA302/1001  Methods of Data Analysis I
(Week 05 lecture notes)
Wei (Becky) Lin
Oct. 10, 2016
1/57
Midterm
No cheat sheet. A formula page is provided. Bring: a calculator,
student card.
Cover the first 4 lectures (questions: proof and analysis).
TA of
STA302/1001  Methods of Data Analysis I
(Week 02 lecture notes)
Wei (Becky) Lin
Sept. 19, 2016
1/39
Last Week
Notes about syllabus.
A functional relationship vs a statistical relationship.
Simple linear regression: Yi = 0 + 1 Xi + i .
Least Square Estima
UNIVERSITY OF TORONTO
Faculty of Arts and Science
JUNE 2015 EXAMINATIONS
STA302H1F
Duration  3 hours
Examination Aids: Scientific Calculator
STA 302/1001
Summer 2015
Final Exam
6/22/2015
Time Limit: 3 hours
Last Name (Print):
First Name:
Student Number:
Chapter 7
Extending the MLR model
1
Multiple Regression in Matrix
Form
Multiple regression in matrix form
Yi = 0 + 1 X i ,1 + + p 1 X i , p 1 + i , i = 1,., n
Yn1 = X n p p1 + n1
Where:
1
, E =
=
n1
cfw_ n1
n
0
=
0 , V =
n1 cfw_ n1
0
2 0
2
STA302/1001HF: Methods of Data Analysis I
Fall 2016
Assignment 2 : Forced Expiratory Volume
Out: Oct. 26,2016
Due: Nov. 17, 2016
Reminder : You MUST write your solution independently and turn in your own writeup.
This assignment is due 11 :00pm, Nov. 17,
Chapter 3
Diagnostics and Remedial
Measures
1
Regression Diagnostics
Diagnostic procedures are used for:
Assessing a models appropriateness (fit)
Checking if models assumptions are reasonable
Finding observations that are problematic
Identifying ways to i
STA302/1001  Methods of Data Analysis I
(Week 08 lecture notes)
Wei (Becky) Lin
Oct. 31, 2016
1/50
Midterm and Final
If you wrote the midterm, you should receive a mail from Crowdmark.
For remark request:
Write me an email within 7 days.
The request mu
STA302/1001  Methods of Data Analysis I
(Week 07 lecture notes)
Wei (Becky) Lin
Oct 24, 2016
1/48
Important
Morning sections: Class on October 27 will take place in ES 1050.
A1 result is available today
Write me mail with your Last name + first name +
STA302/1001  Methods of Data Analysis I
(Week 10 lecture notes)
Wei (Becky) Lin
Nov. 14, 2016
1/48
Last Week
Linear
in
X
's
First order model with two predictor variables.
Multiple Linear regression (MLR).

MLR with dummy variable.
MLR with regressor
STA302/1001  Methods of Data Analysis I
(Week 09 lecture notes)
Wei (Becky) Lin
Nov. 07, 2016
1/39
Last Week
Review on matrices
Simple linear regression model in matrix form.
Yi = 0 + 1 Xi + i , i = 1, . . . , n
Yn1 = Xn2 21 + n1
That is
S
T S
Y1
1
WY2
STA302 / 1001 HF  Methods of Data Analysis I
Wei(Becky) Lin
Fall 2016
Course description
The main objectives of this course are to gain a solid understanding of the theory and application of linear
regression analysis and practical skills for developing