Regression Analysis 70-208
Prof. Maria Marta Ferreyra
Spring 2014
Problem Set 4
(due 02.24.14 at 5:00p)
Total number of points: 80 points
A note before you begin
In this problem set you will explore statistical inference using Least Squares.
If you need t
Regression Analysis 70-208
Prof. Maria Marta Ferreyra
Spring 2014
Problem Set 1
(due 01.27.14 at 5:00pm)
Total number of points: 75 points
A note before you begin
In this problem set you will explore inference about the population mean. You will also
exam
Regression Analysis 70-208
Prof. Maria Marta Ferreyra
Spring 2014
Problem Set 3
(due 02.17.14 at 5:00p)
Total number of points: 75 points
A note before you begin
In this problem set you will explore issues related to the goodness of fit of a regression,
a
Regression Analysis 70-208
Prof. Maria Marta Ferreyra
Spring 2014
Problem Set 2
(due 02.03.14 at 5:00p)
Total number of points: 135 points
A note before you begin
In this problem set you will explore conditional distributions, the conditional expectation
Lecture 12
Multivariate Regression
(sections 4.1 and 4.3.1.)
By the end of this class, you will be able to:
1. Understand what is captured by a covariance and a regression coefficient
2. Figure out why the coefficient on one regressor may change as you in
Lecture 11
Prediction and Fit in the Classical Regression Model (cont.)
(sections 3.3.1, 3.3.2, 3.5.1 and 3.5.2)
By the end of this class, you should be able to:
1. Understand the implications of the Gauss-Markov theorem
2. Determine the reliability of a
Lecture 10
Classical Regression Model
Prediction and Fit in the Classical Regression Model
(sections 3.3.1, 3.3.2, 3.5.1 and 3.5.2)
By the end of this class, you should be able to:
1. Apply your knowledge of the sampling distribution of the Least Squares
Lecture 9
Classical Regression Model
(sections 3.3.1 and 3.3.2)
By the end of this class, you should be able to:
1. View Least Squares as a mechanism to estimate the parameters of the
Classical Regression Model
2. Apply your knowledge of the sampling dist
Lecture 5
Multivariate Distributions (cont.)
By the end of todays class, you will be able to:
1. Derive conditional distributions from joint distributions
2. Use conditional distributions to compute conditional means and variances
3. Construct a condition