Homework 3
STAT W4315: Linear Regression Models
Simple Linear Regression Model
DUE: Monday, October, 19, 12:00 noon
(1) Please sign your home work with your name and UNI number.
(2) Homework must be submitted into the Statistics Homework Boxes room 904 on
Homework 3 (with solutions)
STAT W4315: Linear Regression Models
Simple Linear Regression Model
DUE: Monday, October, 19, 12:00 noon
(1) Please sign your home work with your name and UNI number.
(2) Homework must be submitted into the Statistics Homework
Linear Regression Models
Statistics W4315 Fall 2015
Assignment 1
Reading:
By Friday, September 11, read Appendix A and Chapter 1 of Applied Linear Regression Models by
Kutner, Nachtsheim and Neter (KNN).
For September 18, read Sections 2.12.5 of KNN.
For
Linear Regression Models
Statistics W4315 Fall 2015
Homework 4
Solutions:
1. (Problem 3.13 in KNN) Continue with the Copier maintenance data.
> filename <- "~/Documents/Regression/Data/copier_maintenance.txt"
> Data <- read.table(file=filename, header=T);
Linear Regression Models
Statistics W4315 Fall 2015
Assignment 1
Reading:
By Thursday, September 10, read Appendix A and Chapter 1 of Applied Linear Regression Models
by Kutner, Nachtsheim and Neter (KNN).
For Thursday, September 17, read Chapter 2 of KNN
Homework 3 Solutions, Stat 4315, Fall 2009 Problems: 3.19, 3.20, 3.24, 4.10, 4.21, 4.25, 5.23, 5.29, 5.30
Problem 3.19 The plot against the fitted values is preferable. If there isn't a relationship between e_i and the fitted values it implies there isn't
Linear Regression Models
Statistics W4315 Fall 2015
Assignment 6
Solutions:
1. (Project 8.38 in KNN) Return to the SENIC Project data, and consider the regression relating
number of nurses (Y ) to available facilities and services (X).
> filename <- "~/Do
Linear Regression Models
Statistics W4315 Fall 2015
Assignment 4
Reading:
For Friday, October 9, read Chapter 5 of Applied Linear Regression Models by Kutner, Nachtsheim
and Neter (KNN).
For Friday, October 16, read Chapter 6 and Section 10.1 of KNN.
Home
Homework 1 (with solutions)
STAT W4315: Linear Regression Models
Simple Linear Regression Model
DUE: Wednesday, September, 23, 12:00 noon
(1) Please sign your home work with your name and UNI number.
(2) Homework must be submitted into the Statistics Home
Homework 1
STAT W4315: Linear Regression Models
Simple Linear Regression Model
DUE: Wednesday, September, 23, 12:00 noon
(1) Please sign your home work with your name and UNI number.
(2) Homework must be submitted into the Statistics Homework Boxes room 9
Linear Regression Models
Statistics W4315 Fall 2015
Assignment 2
Reading:
By Thursday, September 17, read Appendix A and Chapters 12 of Applied Linear Regression
Models by Kutner, Nachtsheim and Neter (KNN).
For Thursday, September 24, read Chapter 3 of K
Linear Regression Models
Statistics W4315 Fall 2015
Assignment 1
Reading:
By Thursday, September 10, read Appendix A and Chapter 1 of Applied Linear Regression Models
by Kutner, Nachtsheim and Neter (KNN).
For Thursday, September 17, read Chapter 2 of KNN
Linear Regression Models
Statistics W4315 Fall 2015
Assignment 6
Reading:
For Thursday, November 12, read Sections 7.6 & 10.5 and 9.19.6 of Applied Linear Regression
Models by Kutner, Nachtsheim and Neter (KNN).
For Thursday, November 19, read Sections 3.
Linear Regression Models
Statistics W4315 Fall 2015
Assignment 2
Reading:
By Friday, September 18, read Appendix A and Chapters 12 of Applied Linear Regression Models
by Kutner, Nachtsheim and Neter (KNN).
For Friday, September 25, read Chapter 3 of KNN.
Linear Regression Models
Statistics W4315 Fall 2015
Assignment 4
Reading:
For Thursday, October 15, read Chapter 5 of Applied Linear Regression Models by Kutner, Nachtsheim and Neter (KNN).
For Thursday, October 22, read Chapter 6 and Section 10.1 of KNN.
Student Solutions Manual to accompany Applied Linear Regression Models Fourth Edition
Michael H. Kutner Emory University Christopher J. Nachtsheim University of Minnesota John Neter University of Georgia
2004 McGraw-Hill/Irwin Chicago, IL Boston, MA
PREFA
Linear Regression Models SAS Homework 6 14.9 a) Maximum likelihood estimate of 0: -10.3089 Maximum likelihood estimate of 1: 0.0189 Fitted response function: phat = exp(-10.3089+0.0189X)/(1+exp(-10.3089+0.0189X) b) The fitted logistic response function ap
Diagnostics and Remedial Measures
Yang Feng
http:/www.stat.columbia.edu/~yangfeng
Yang Feng (Columbia University)
Diagnostics and Remedial Measures
http:/www.stat.columbia.edu/~yangfeng
/ 76
Remedial Measures
How do we know that the regression function is
Part
Simple Linear Regression
I
Chapter
1
Linear Regression with One Predictor Variable
Regression analysis is a statistical methodology that utilizes the relation between two or more quantitative variables so that a response or outcome variable can be pr
Chapter
2
Yi = 0 + 1 X i + i (2.1)
Inferences in Regression and Correlation Analysis
In this chapter, we first take up inferences concerning the regression parameters 0 and 1 , considering both interval estimation of these parameters and tests about them.
Solutions to Homework 2, Stat 4315, Fall 2009
2.3 Two points, 1) Look at the p-value is there evidence that the slope is significantly different than zero? What does that say about the interpretation of the statement given? 2) Who plays interactive market
The Simple Linear Regression Model
Statistics W4315
Columbia University
Fall 2015
September 18, 2015
1
The simple linear regression model
Suppose our data consist of (X1, Y1), (X2, Y2), . . . , (Xn, Yn).
Under the simple linear regression (SLR) model, the
Testing for lack of t
Statistics W4315
Columbia University
Fall 2015
October 2, 2015
1
Announcements
1. Instructors Friday o
ce hour:
1:303:00pm 10th oor SSW 1:002:30pm 703 Hamilton
2. Homework 2 is due today!
October 5.
Submit by noon on Monday,
3. Homew
Prediction intervals; the analysis of variance
Statistics W4315
Columbia University
Fall 2015
September 25, 2015
1
Review (Sec 2.4)
Let xh denote a xed x-value
A point estimate of
E(Y |X = xh)
is
b 0 + b 1 xh = Y h
How about a 1
condence interval for
0 +
The Simple Linear Regression Model
Statistics W4315
Columbia University
Fall 2015
September 18, 2015
1
The simple linear regression model
Suppose our data consist of (X1, Y1), (X2, Y2), . . . , (Xn, Yn).
Under the simple linear regression (SLR) model, the
Introduction to Regression
Statistics W4315
Columbia University
Fall 2015
September 11, 2015
1
Welcome!
Instructor:
Ron Neath, [email protected]
O
ce hours: Friday 1:303:00pm, 10th oor of SSW
Teaching assistant:
Yuanjun Gao, [email protected]
O
c
Diagnostics
Statistics W4315
Columbia University
Fall 2015
September 25, 2015
1
Course stu
Homework 1 is due today!
Submit your paper by 12noon (NYC time) on Monday!
Section 003: submit paper copy to mailbox in Room 904 SSW.
Section D04: submit pdf throug
Testing for lack of t
Statistics W4315
Columbia University
Fall 2015
October 2, 2015
1
Announcements
1. Instructors Friday o
ce hour:
1:303:00pm 10th oor SSW 1:002:30pm 703 Hamilton
2. Homework 2 is due today!
October 5.
Submit by noon on Monday,
3. Homew
Introduction to Time Series and Forecasting
(Chapter 1)
1.1 Examples of time series
Ex 1.1.1 (Australian red wine sales; WINE.TSM)
xt = monthly sales of red wine (in kilolitres)
t = (Jan, 1980), (Feb, 1980), . . . , (Oct, 1991)
or
t=1, 2, . . . , 142.
1
0
Stationary Processes (Chapter 2)
2.1
2.1Basic
BasicProperties
Properties
cfw_Xt stationary time series
Mean: = Xt for all t.
ACVF: (h) = cov(Xt+h, Xt), h=0, +1, . . .
( h)
ACF: (h) = ( 0)
1
DEFINITION. cfw_Xt is a Gaussian time series if
all of its joint