BUEC 333: Some Answers to Study Questions for the Midterm
1. Construct the pdf and cdf for the sum of 3 dice. Using the cdf, show the probability of getting a
sum in the range of [6,8]. pdf and cdf in 216'ths.
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BUEC 333
Spring 2006
Prof. Simon D. Woodcock
Special Code: 0001
Name:
Student Number:
Section:
SOLUTIONS TO THE MIDTERM EXAM
Part 1: Multiple Choice. [20 points total]
1) The significance level of a test is the probability that you:
a) reject the null whe
Name:_
Student #:_
BUEC 333 FINAL EXAM
There is a formula sheet attached at the end of the exam and some blank pages for your rough work.
Use the bubble sheet to record your answers for the multiple choice questions and write your answers
to the short ans
SAMPLE MULTIPLE CHOICE QUESTIONS FOR MIDTERM
1.) Suppose the monthly demand for tomatoes (a perishable good) in a small town is random. With
probability 1/2, demand is 50; with probability 1/2, demand is 100. You are the only producer of tomatoes
in this
BUEC333, Summer 2016
Handin assignment #2
Deadline: August 4 at 11:59 (am)
Rules
1. The open labs are there for you to get help and ask questions: use them!
2. Your submission consists of an RMarkdown source code (an .Rmd file), as well as the
generated
1
IV questions
1. Instrumental variables I. In Acconcia et al. (2014), Mafia and Public Spending, American Economic Review 104(7), the authors use Italian data on 95 provinces (indexed by i)
to estimate the regression coefficient in the model
Yi = Gi + Xi
BUEC333
Statistical analysis of economic data
Summer 2016
Table of contents
Administration
Why take this course?
Review: Probability theory
Section 1
Administration
About me
I
Chris Muris
I
I
I
I
office: WMC 3639
email: Come and see me in person!
www: ch
BUEC 333
Problem set 1 Solutions
Due date 09/20
1
1. Problem 2.3 in Exercises, Chapter 2.
E (W )
var (W )
E (V )
var (V )
cov (W; V )
=
E (3 + 6X) = 3 + 6E (X)
=
3 + 6 [0 P (X = 0) + 1 P (X = 1)]
=
3 + 6 [P (X = 1; Y = 0) + P (X = 1; Y = 1)]
=
3 + 6 0:7 =
Given name:_
Student #:_
Family name:_
BUEC 333 FINAL
Multiple Choice (2 points each)
1) Suppose you draw a random sample of n observations, X1, X2, , Xn, from a population with unknown
mean . Which of the following estimators of is/are biased?
a.) half o
BUEC333 Exam 2
Part 2: Linear and Nonlinear Regression
50 points total
November 4, 2015
Abstract
There should be nothing on your desk except for:
(a) exam booklet
(b) exam
(c) formula sheet
(d) pens
All your stu (books, notebooks, (i)phone, etc) should be
BUEC333 Exam 3 (44 points total)
November 22, 2016
Abstract
Please, be RIGOROUS, and SHOW ALL YOUR WORK in order to
receive partial credit. If you do not show your work, no partial credit can
be given even if you give the right answer.
There should be not
BUEC333 Exam 1
Part 1: Probability Theory and Statistics
September 27, 2015
Abstract
There should be nothing on your desk except for:
(a) exam booklet
(b) exam
(c) formula sheet
(d) pens
All your stu (books, notebooks, (i)phone, etc) should be placed in a
BUEC 333
Problem set 2
Due date 09/26, by 4 PM.
1. Problem 2.4 in Review the Concepts, Chapter 2.
Population of interest: 80 students in a given econometrics class
Random variable of interest: the weight of the students in the class. Call this variable X.
Cheat sheet for BUEC 333, Summer 2016
June 13, 2016
Key Concept 2.3
Let X, Y and V be random variables, let x and x2 be the mean and variance of X, let XY be
the covariance between X and Y (and so forth for the other variables), and let a, b, and c be
con
Practice Questions for Midterm Exam
BUEC 333
Part I: Multiple choice questions
Select one answer (and one answer only) by circling it.
You do not need to justify your answer.
Q1) Econometrics can be defined as follows with the exception of
A) the science
Linear Regression with One Regressor
(SW Chapter 4)
Outline
1. The population linear regression model
2. The ordinary least squares (OLS) estimator and the
sample regression line
3. Measures of fit of the sample regression
4. The least squares assumptions
Linear Regression with Multiple Regressors
(SW Chapter 6)
Outline
0. Preliminary discussion
1. Omitted variable bias
2. Causality and regression analysis
3. Multiple regression and OLS
4. Measures of fit
5. Sampling distribution of the OLS estimator
BUEC
BUEC 333
Statistical Analysis of Economic Data
Lecture 1
Irene Botosaru
Simon Fraser University
Fall 2015
Outline of Lecture 1
Introduction to the course
I
What is econometrics?
I
Overview of some concepts that you will learn in this class via
an example
Expectation of linear transformations of continuous random variables
Our goal is to show that for a continuous random variable X, and known scalars
a and b, the following equality holds:
E (aX + b) = aE (X) + b.
Assume that the random variable X has sampl
BUEC 333, Summer 2016: Syllabus
Course:
Statistical Analysis of Economic Data
Lecture:
go.sfu.ca
Labs:
go.sfu.ca
Instructor:
Chris Muris
Office:
WMC 3639
Email:
Do not email me. Come and talk to me in person.
Office hours: Thursday, 10:0012:00
This docum
Regression Analysis
and
Ordinary Least Squares Estimators
BUEC 333
Regression Analysis and Ordinary Least Squares Estimators
BUEC 333
1 / 12
Population regression line
Y = 0 + 1 X

Y is the dependent variable (or regressand)
X is the explanatory variable
Hypothesis Testing: Examples
BUEC 333
Hypothesis Testing: Examples
BUEC 333
1/8
Example 1: new production process
The production process manager of Northern Windows Inc. has asked
you to evaluate a proposed new procedure for producing its Regal line
of do
Hypothesis Testing: Examples
BUEC 333
Hypothesis Testing: Examples
BUEC 333
1 / 12
Example 1: new production process
The production process manager of Northern Windows Inc. has asked
you to evaluate a proposed new procedure for producing its Regal line
of
Hypothesis Testing: Examples
BUEC 333
Hypothesis Testing: Examples
BUEC 333
1/9
Example 1: new production process
The production process manager of Northern Windows Inc. has asked
you to evaluate a proposed new procedure for producing its Regal line
of do
Hypothesis Tests and Confidence Intervals
in Multiple Regression
(SW Chapter 7)
Outline:
1.
2.
3.
4.
Hypothesis tests and confidence intervals for one coefficient
Joint hypothesis tests on multiple coefficients
Other types of hypotheses involving multiple
Nonlinear Regression Functions
(SW Chapter 8)
Outline
1. Nonlinear regression functions general comments
2. Nonlinear functions of one variable
3. Nonlinear functions of two variables: interactions
4. Application to the California Test Score data set
BUEC
ASSIGNMENT #2  DUE APRIL 11, 2016
NO LATE ASSIGNMENTS ACCEPTED; NO ASSIGNMENTS SENT VIA EMAIL ACCEPTED.
DATA ANALYSIS USING STATA
The following questions use the Excel data, gravity.xlsx, posted on the course website.
Your job is to use the data provided
ASSIGNMENT #1  DUE MARCH 2, 2016
NO LATE ASSIGNMENTS ACCEPTED; NO ASSIGNMENTS SENT VIA EMAIL ACCEPTED.
DATA ANALYSIS USING STATA
The following questions use the Excel data, NHL 1601.xlsx, posted on the course website.
Your job is to use the data provided
Given name:_
Student #:_
Family name:_
Section #:_
BUEC 333 MIDTERM
Multiple Choice (2 points each)
1.) The GaussMarkov Theorem says that when the 6 classical assumptions are satisfied:
a.) The least squares estimator is unbiased
b.) The least squares es
Given name:_
Student #:_
Family name:_
Section #:_
BUEC 333 MIDTERM
Multiple Choice (2 points each)
1) If the covariance between two random variables X and Y is zero then
a) X and Y are not necessarily independent
b) knowing the value of X provides no inf
1.) Which of the following is not an assumption of the CLRM?
a.) The model is correctly specified
b.) The independent variables are exogenous
c.) The errors are normally distributed
d.) The errors have mean zero
e.) The errors have constant variance
2.) F