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 s
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
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 i
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
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 wh
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
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 knowl
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 Regres
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 c