University of California, Los Angeles
Department of Statistics
Statistics 100C
Instructor: Nicolas Christou
Homework 3
EXERCISE 1
Data have been collected for 19 observations of two variables, Y and x
UCLA
Department of Statistics
STAT 100C: Linear Models
Problem Set 2
Spring 2017
Due: Tuesday, April 25
Problem 2.1
Let X1 , X2 , X3 be random variables with zeromean and unit variance: E(Xi ) = 0
an
Lab Midterm Revision
Statistics 20 1A  Winter 2017
Febuary 15, 2017 23:00 pm
0. Instruction
Create one single R Markdown document to complete the tasks below.
Include all necessary lines of codes,
Lab Final
Statistics 20 1A  Winter 2017
March 15, 2017 12:00~12:50 pm
0. Instruction
Please display your Bruin ID on the table. You can use all the resources on CCLE, the internet
or your note. All m
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University of California, Los Angeles
Department of Statistics
Statistics lOOC
Finally we get:
Instructor: Nicolas Christou
Homework 1  Solutions
We can use the above t'9 random variable to constru
University of California, Los Angeles
Department of Statistics
Statistics lOOC
Instructor: Nicolas Christou
Homework 2
EXERCISE 1
A new profitsharing plan was iIitraduced at an automobile parts manu
University of California, Los Angeles
Department of Statistics
Statistics 100C
Instructor: Nicolas Christou
Homework 5
Exercise 1
Please refer to
a. Test the overall significance of the model. The eas
University of California, Los Angeles
Department of Statistics
Statistics lOOC
Instructor: Nicolas Christou
Homework 1
Exercise 1
Suppose that we want to test the following two hypotheses;
Ho: /11 /1
University of California, Los Angeles
Department of Statistics
Statistics 100C
Instructor: Nicolas Christou
Homework 4
Exercise 1
Consider the following simple regression model Yi = f30 + f31xi + Ei,
University of California, Los Angeles
Department of Statistics
Statistics 100C
Instructor: Nicolas Christou
Homework 7
\
Exercise 1
, Consider the models
\
y
= X/3 + E,
X*/3 + E",
and Y*
where E(E) =
(
\
University of California, Los Angeles
Department of Statistics
Statistics lOOC
c. The output from
Instructor: Nicolas Christou
R;
> q < laCy x)
> fHllQlll3;['y(q)
call:
Y  x)
Homework 2  Solut
University of California, Los Angeles
Department of Statistics
Statistics lOOC
Nicolas Christou
Homework 3  SolutiGns
EXERCISE 1
We are given
8y
= 10,
'V
.
C;:;,
"
1
E:!l (Yi  Yi)2 = 180.
a. The pro
University of California, Los Angeles
Department of Statistics
Statistics lOOC
Instructor: Nicolas Christou
Homework 7  Solutions
Exercise 1
We are given:
Y
Xf3+e, and Y*
where E(e) = 0, cov(ti)
orth
STATS 100C, Spring 2017
Midterm Exam
Total 100 points; Thursday, May 11, 45:15pm
Last Name
First Name
UID
Scores
Discussion (circle one) (8am) or (9am)
Instruction: This is a closedbook exam. You ca
STATS 100C: Linear Models
Spring 2017
Lecture 1: April 4
Lecturer: Arash Amini
1.1
Scribes: Eric Chuu
Review of Linear Algebra
Some topics that we should be familiar with:
Fundamental subspaces with
1
Chapter 4:
Continuing with Heteroscedasticity and Weighted Least Squares
Outline
1) What is it?
2) What are the consequences for our Least Squares estimator when
we have heteroscedasticity
3) How do
Chapter Two
Simple Linear Regression
The Model
Simple Linear Regression:
y
Where
0
Important Assumptions:
1
x
Minimize means taking derivatives or partial
derivatives with respect to the parameters of
Bond Data Example
Dr. Akram Almohalwas
Thursday, October 15, 2015
This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and
MS Word documents. For more detail
OneWay, TwoWay Anova and Ancova
Akram Almohalwas
November 2, 2015
This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and
MS Word documents. For more deta
wheat rain document
Dr. Akram Almohalwas
April 18, 2016
This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and
MS Word documents. For more details on using
1
Chapter 7: Model Selection:
First steps in model building/checking
Scatterplot matrix: examine that predictors are
linear functions of each other and the response
variable.
Scatterplot matrix: use
Chapter Two Part of the Slides
Dr. Akram Almohalwas
April 6, 2016
This is an R Markdown document. Markdown is a simple formatting syntax for authoring
HTML, PDF, and MS Word documents. For more detail
Multiple Linear Regression
A regression with two or more explanatory variables is called a multiple
regression. Rather than modeling the mean response as a straight line, as in
simple regression, it i
Stat100C
Heteroscedasticity, autocorrelation and generalized least squares. General Introduction
What is heteroscedasticity and autocorrelation?
The standard multiple regression model can be represent
Chapter 3:
A Review of Matrix Algebra and Important Results
on Random Vectors
Second approach
Starting with the last system of equations
we can approach the solution of the
system using matrix algebra
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University of Califurnia, Los Angeles
Department of Statistics
data. from homework 5, exercise 4:
a < read. table('http:/wl,l. stat.ucla,edu/ nchristo!statisticsl00C/restaurant. txt", l1eacier"'TRU
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University of California, Los Angeles
Department of Statistics
Statistics lOOC
cov(13) = ,2(X'X)1 = ,'
Instructor: Nicolas Christou
Homework 4  Solutions
Of course
Exercise 1
. (fJo)
13,
(nE'=
University of California, Los Angeles
Department of Statistics
Statistics 1000 Instructor: Nicolas Cluistou
Exam 2
15 November 2016
Name: $1ng l lQN)
Problem 1 (25 points)
Answer the followingr questi
3'
l'
[
g.
E
3
Math 131A/ Lecture 3 Fall 2017 Midterm I
Your Name:
Student ID:

Signature: J
INSTRUCTIONS: This is a closedbook test. Do all work on the sheets
provided. If you need more space for
Math 131A/ Lecture 3 Fall 2017 Midterm II
Your Name:
Student ID:
Signature:
INSTRUCTIONS: This is a. closedbook test. Do all work on the sheets 2
provided. If you need more Space for your solution, us
University of California, Los Angeles
Department of Statistics
Statistics 100C Instructor: Nicolas Christou
Homework 2  Solutions
Exercise 1
Consider the regression model y= X + s, with E(e e): 0, co