University of California, Los Angeles
Department of Statistics
Statistics 100C
Instructor: Nicolas Christou
The residuals and their variance-covariance matrix
We have seen that the variance-covariance matrix of the residuals can be expressed as follows:
c
Lecture 13
Fall 2016
STATS 130 Getting Up to Speed with SPSS, Stata, SAS, and R
Maria Cha
Fall 2016 Maria Cha
Topics
Fall 2016 Maria Cha
Regression Analysis
Linear regression is designed to analyze
association among numerical variables (scale).
However,
Fall 2016 Review Problems
Question 1. In this question, we consider a large food processing center that needs to be able to
switch from one type of package to another quickly to react to changes in order patterns.
Consultants have developed a new method f
1
Chapter 4:
Continuing with Heteroskedasticity and Weighted Least Squares
Outline
1) What is it?
2) What are the consequences for our Least Squares estimator when
we have heteroskedasticity
3) How do we test for heteroskedasticity?
4) How do we correct a
1. Only an effective collaboration between filmmakers and art historians can
create films that will enhance viewers' perceptions of art. Filmmakers need
to resist the impulse to move the camera quickly from detail to detail for fear
of boring the viewer
5
Extended debate concerning the exact point of origin of individual folktales told
by Afro-American slaves has unfortunately taken precedence over analysis of
the tales meaning and function. Cultural continuities with Africa were not
dependent on import
GRE AIO
Passage1: Although social learning (the acquisition of specific behaviors by
observing other individuals exhibiting those behaviors) is well documented
among fish, few studies have investigated social learning within a
developmental context in th
Lecture 11
Fall 2016
STATS 130 Getting Up to Speed with SPSS, Stata, SAS, and R
Maria Cha
Fall 2016 Maria Cha
Subsetting Data
Subset the data by sampling cases (observations)
2. Sampling cases
Data > Select Cases
Fall 2016 Maria Cha
Subsetting Data
Fal
University of California, Los Angeles
Department of Statistics
Statistics 100C
Instructor: Nicolas Christou
Exam 1
25 April 2014
Name:
Problem 1 (25 points)
Consider the simple regression model
yi = 10 + 1 xi +
i
with E( i ) = 0, var( i ) = 2 , and cov( i
University of California, Los Angeles
Department of Statistics
Statistics 100C
Instructor: Nicolas Christou
The tted values and their variance-covariance matrix
The variance-covarince matrix of the tted values can be expressed as follows:
cov(Y) = cov(X)
University of California, Los Angeles
Department of Statistics
Statistics 100C
Instructor: Nicolas Christou
Example of simple linear regression in matrix form
An auto part is manufactured by a company once a month in lots that vary in size as demand uctua
University of California, Los Angeles
Department of Statistics
Statistics 100C
Instructor: Nicolas Christou
The bivariate normal distribution
Simple regression when X is random
The bivariate normal distribution for two normally distributed random variable
University of California, Los Angeles
Department of Statistics
Statistics 100C
Instructor: Nicolas Christou
Homework 5
Exercise 1
Please refer to homework 4, exercise 4.
a. Test the overall signicance of the model. The easiest way to do this is to nd rst
University of California, Los Angeles
Department of Statistics
Statistics 100C
Instructor: Nicolas Christou
Homework 4
Exercise 1
Consider the following simple regression model yi = 0 + 1 xi + i , for which E( i ) = 0, E(
i = j, and var( i ) = 2 . The nor
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, in order to run a regression of y on x. You are given
University of California, Los Angeles
Department of Statistics
Statistics 100C
Instructor: Nicolas Christou
Homework 2
EXERCISE 1
A new prot-sharing plan was introduced at an automobile parts manufacturing plant last year. Both management and union
repres
University of California, Los Angeles
Department of Statistics
Statistics 100C
Instructor: Nicolas Christou
Homework 1
Exercise 1
Suppose that we want to test the following two hypotheses:
H0 : 1 2 = 0
Ha : 1 2 > 0
To perform this test a sample of n obser