STA302/1001: Quiz #1, 10:2011:00am, October 1, 2013
Let xi denote the predictor variable and yi denote the response variable. The simple linear
regression model is given by yi = 0 + 1 xi + ei , i = 1, . . . , n, where the error ei is independently
and ide
University of Toronto at Mississauga
Regression Analysis
STA331H5F 2010
Term Test #1
Aids Allowed: non-graphing calculator without a text keypad
Aids Provided: none
1.
(6 marks) A public swimming pool operator believes that the amount of chlorine
necessar
STA 302 H1F / 1001 HF Fall 2009 Test October 22, 2009
LAST NAME:
SOLUTIONS
FIRST NAME:
STUDENT NUMBER: ENROLLED IN: (circle one) STA 302 STA 1001
INSTRUCTIONS: Time: 90 minutes Aids allowed: calculator. A table of values from the t distribution is on the
UNIVERSITY OF TORONTO
Faculty of Arts and Science
DECEMBER EXAMINATIONS 2007
STA 302 H1F / STA 1001 HF
Duration - 3 hours
Aids Allowed: Calculator
LAST NAME:
SOLUTIONS
FIRST NAME:
STUDENT NUMBER:
There are 20 pages including this page.
The last page is
UNIVERSITY OF TORONTO
Faculty of Arts and Science
DECEMBER 2003 EXAMINATIONS
STA 302 H1F / 1001 H1F
Duration - 3 hours
Aids Allowed: Calculator
NAME:
SOLUTIONS
STUDENT NUMBER:
There are 22 pages including this page.
The last page is a table of formulae
UNIVERSITY OF TORONTO
Faculty of Arts and Science
DECEMBER EXAMINATIONS 2009
STA 302 H1F / STA 1001 HF
Duration - 3 hours
Aids Allowed: Calculator
LAST NAME:
SOLUTIONS
FIRST NAME:
STUDENT NUMBER:
There are 23 pages including this page.
The last page is
2
SCATTERPLOTS AND REGRESSION
values (xi , yi ), i = 1, . . . , n, of (X, Y ) observed on each of n units or cases. In
any particular problem, both X and Y will have other names such as Temperature
or Concentration that are more descriptive of the data th
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 t-distribution table is provided on the last page
T
STA 302 / 1001
Lecture 2
1
Simple Linear Regression
Yi =
0 + 1 X i + i
Yi is the response value (random variable)
Xi is the predictor value (known and constant)
0 is the Y-intercept (constant parameter)
1 is the slope (constant parameter)
i is the error
STA 302 / 1001
Lecture 5
1
Residuals Main Results
= 2 (1 )
Proved
= 2
2
Residuals Main Results
, = 2
3
Residuals Main Results
4
Residuals Main Results
, =
1 1
5
Normality of Errors
=
6
Normality of Errors
Residuals can look like they come from a
n
`2 VC 3 sD ) 2 # v9 3 sD ) 2 sD 6 3 sD ) 2 F ) Q # ec&$5cyTEAVETEAPePm&57 c x r p v q p sl xr n v w q Q Q q ~ |
E
5 ~ f | P z z ~ 5 5 ma | z z x n m n P
cfw_ ~ cfw_ Vcfw_ cfw_ | z z
Vcfw_ ~ Ycfw_ Ycfw_ ub | cfw_ z z y c
n P v wu q p q Yy
n l
STA 302 / 1001 Answers to recommended practice problems from chapter 6 Note: These are brief answers to the problems and many would need more detail in order to receive full marks on a test or exam. 6.2 (a) Skip this one. There is no intercept and we didn
STA 302 / 1001 Answers to recommended practice problems from chapter 4 Note: These are brief answers to the problems and many would need more detail in order to receive full marks on a test or exam. 4.1 No and no. At least 90% of the time the joint conden
STA 302 / 1001
Answers to recommended practice problems from chapter 3
Note: These are brief answers to the problems and many would need more
detail in order to receive full marks on a test or exam.
3.1 Skip (1).
(2) There are two distinctions to be made.
STA302H1F/1001HF: Methods of Data Analysis I, Fall 2013
Please be aware that the information provided below is subject to change.
Class Information:
Lectures are given on Tues 11:1012:00pm and Thurs 10:1012:00pm in MC102.
Tutorials are held on Tuesday 1
STA302/1001: Methods of Data Analysis
Instructor: Fang Yao
Chapter 6: Polynomials and Factors
STA302/1001 Lectures p. 1/20
Polynomials
what shall we do if lack of t exists?
we could do nothing and just sit there and cry
or we could improve our model
Polyn
STA302/1001: Methods of Data Analysis
Instructor: Fang Yao
Chapter 9: Outliers and Inuence
STA302/1001 Lectures p. 1/24
Outliers
quote from textbook:
"cases that do not follow the same model as the rest of
the data are called outliers"
note: outliers are
University of Toronto at Mississauga
STA331H5F 2011
Term Test #1
VERSION 1
Questions 1-6 are about the following situation An insurance company wants to relate the amount of fire damage (y, in $1,000s) in major
residential fires to the distance between th
University of Toronto at Mississauga
Regression Analysis
STA331H5F 2010
Term Test #2
Aids Allowed: non-graphing calculator without a text keypad
Aids Provided: none
Printed Name: _
Signature: _
Student Number: _
1. (3 marks) The plot to the right is based
University of Toronto at Mississauga
STA331H5F 2011
Term Test #2
1. (1 mark) Is this statement True or False? If its False, correct it. To correct the
statement, draw a line through the incorrect part and write a correction below.
You are working with a s
University
University of Toronto at Mississauga
Regression Analysis
STA331H5F 2010
Term Test #3
Aids Allowed: non-graphing calculator without a text keypad, computer output
Aids Provided: none
1. (6 marks) Y is a random vector such that
Let W satisfy
a. L
University of Toronto at Mississauga
STA331H5F 2011
Term Test #3
Aids Allowed: non-graphing calculator without a text keypad
Aids Provided: none
1. (6 marks) For SLR data, the predictor and the response have both been standardized,
creating
yi* = (yi /) s
University of Toronto at Mississauga
Regression Analysis
STA331H5F 2010
Term Test #4
Aids Allowed: non-graphing calculator without a text keypad, computer output
Aids Provided: none
1. (12 marks) A researcher considers three models:
Model 1
Model 2
Model
University
University of Toronto at Mississauga
STA331H5F 2011
Term Test #4
Aids Allowed: non-graphing calculator without a text keypad
Aids Provided: none
1. (6 marks) Consider these functions of random variables Y1, Y2, and Y3.
W1 = 2Y1 Y2 + Y3
W2= Y1 Y
University of Toronto at Mississauga
STA331H5F 2011
Term Test #5
Aids Allowed: non-graphing calculator without a text keypad
Aids Provided: none
1. (2 marks) Consider the SLR model Y = 0 + 1X + . We often use an F statistic to test the
hypothesis Ho: 1 =
University of Toronto at Mississauga
STA331H5F 2011
Term Test #6
Aids Allowed: non-graphing calculator without a text keypad
Aids Provided: none
1. (4 marks) A student had regression data with response Y and one observed predictor X. He
fit a polynomial r
University of Toronto at Mississauga
STA331H5F 2011
Term Test #7
Aids Allowed: non-graphing calculator without a text keypad
Aids Provided: none
1.
(3 marks) A student is selecting a MLR model using stepwise regression (forwards and
backwards). She decide