ISYE 6414 MSA
HW3 Solutions
We first load the data and the required packages for homework 3.
library(ellipse)
library(faraway)
data(cheddar)
data(prostate)
data(snail)
data(teengamb)
Problem 1 (Chap. 5, Ex. 1)
Fit linear models for the teengamb data with
ISYE 6414 MSA
HW4 Solutions
We first load the data and the required packages for homework 4.
library(faraway)
library(MASS)
data(cheddar)
data(pipeline)
data(prostate)
data(trees)
data(ozone)
Problem 1 (Chap. 7, Ex. 5)
(b)
Check the correlations between a
ISYE 6414 MSA: Regression Analysis
Fall 2015
Instructor
Time/Location
Office
Email
Phone
Office Hours
TA
: Dr. Roshan Joseph Vengazhiyil
: 12:05-01:25 pm MW in IC 109
: 337 Groseclose Building
: roshan@gatech.edu
: 404 894 0056
: M 01:30-02:30 pm or by ap
ISyE 6414B: Regression Analysis
HW#3 (due in class on Friday, October 3, 2014)
(Please feel free to use use R or other statistical softwares for Problem #1 of this homework.)
1. (20 points, Solution Concentration (Modied from Problem 3.15 & 3.16 on page 1
ISyE 6414B: Regression Analysis
Week 7 & 8: Linear Regression
Review of multiple linear regression so far
Inference of multiple linear regression
Diagnostics of multiple linear regression
Weighted least squares in multiple linear regression
Can we do
Civil and Environmental Engineering
Fall 2014
Subject: ISyE 6414
Statistical Modeling and Regression
Enrichment Project: (1)
Submitted to: Prof. Jye-Chyi Lu
Submitted by: Shehab Sherif Wissa Agaiby
GT ID # 902846606
Date : September 2014
Fall2014ShehabAga
ISyE 6414: Regression Analysis
Week 10
Classification: linear regression and KNN
Logistic Regression: Binary Responses Data
(read Ch 14 of our text)
Reminder:
Both HW#5 and the Project proposal are due
Friday, Oct 24
Statistics is not an end in itself
ISyE 6414: Regression Analysis
Multiple Linear Regression
Multi-Collinearity (Ch 10.5 & 11.2)
Penalized Least Squares: Ridge Regression and LASSO
Principal component regression (PCR)
Partial Least Squares (PLS)
Remarks:
In your homework & final report, d
ISyE 6414: Regression Analysis
HW#1 (due in class on Friday, September 5, 2014)
There are 4 questions, and please look at both sides. Total points = 50 pts.
1. (10 points). Grade Point Average (Modied from Problem 1.19 on page 35 of
our text). The directo
ISyE 6414B: Regression Analysis
HW#2 (due in class on Wednesday, September 17, 2014)
(Please feel free to use R or other statistical softwares in this homework.)
In the context of the problem 1 of HW#1 on Y = freshman GPA and x = ACT test score, the
direc
ISyE 6414B: Regression Analysis
HW#5 (due in class on Wednesday, October 22, 2014)
(Please look at both sides, and please feel free to use R or other statistical softwares.)
Consider the data set fat in the faraway library of R. The data is also available
ISyE 6414B: Regression Analysis
HW#4 (due in class on Friday, October 10, 2014)
There are 2 questions, and please look at both sides. Total points = 30 + 20 = 50 pts.
(It is OK to use R or other statistical softwares for this homework.)
1. Brand Preferenc
ISyE 6414B: Regression Analysis
Midterm I
2:05-2:55pm on Wed, Sep 24.
Three (two-sided) notes are allowed.
Closed-books, individual work!
Calculators are allowed and
encouraged, but no statistical software
such as R.
Simple Linear Regression
Problem F
Regression Analysis
Homework 1
This homework is due Wednesday, September 14th, in class BEFORE class starts. Late
papers will not be accepted.
Please remember to staple if you turn in more than one page.
You must SHOW ALL WORK. If you do not show your w
R Notes for
Simple Linear Regression
Example: Election 2000
Background: In 2000 Bush and Gore were the main candidates for President. Buchanan, a
strongly conservative candidate, was also on the ballot. In the state of Florida, Bush and Gore
essentially t
Regression Analysis
Homework 3: Multiple Linear Regression
This homework is due Wednesday, October 19th, in class BEFORE class starts. Late papers will
not be accepted.
Please remember to staple if you turn in more than one page.
You must SHOW ALL WORK.
James Bailey
ISyE 6414
September 22nd, 2014
Oh and I discussed stuff with Amelia Musselman
Question 1 - LSE: Consider the SLR model with two regression coefficients, 0
and 1 . Find the optimal LSE.
Algebra:
min f (0 , 1 ) =
n
X
2
yi (0 + 1 xi )
i=1
n
X
d
Amelia Musselman
ISyE 6414
9/22/14
Enrichment Project 1
*I worked with James Bailey
Question 1 - LSE
1. We will show that 0 = y 1 x and 1 =
squares estimate given by minimizing,
Q(0 , 1 ) =
n
X
Sxy
Sxx
is the optimal solution to the least
[Yi (0 + 1 xi )]
ISyE 6414 - Enrichment Project 1
Yifan Liu
September 9, 2014
1. The least square objective function is
Q(0 , 1 ) =
n
X
2
[Yi (0 + 1 Xi )]
i=1
Take the partial differentiation with respect to 0 and 1 :
n
X
Q(0 , 1 )
(Yi 0 1 Xi )
= 2
0
i=1
n
X
Q(0 , 1 )
Xi
Enrichment Project #2
ISYE6414
Professor Jye-Chyi (JC) Lu
Group members:
Hemochen An
Yue Peng(903068630)
Dongxia Dong (903033174)
Jingjing Ni
Xinyu Wang (903013540)
Task Distribution
1. Stepwise regression: Xinyu Wang
2. Best subset regression: Xinyu Wang
ISyE 6414: Introduction to simple linear
regression models (EP #1)
Robert Chen
rchen87@gatech.edu
Erik Reinertsen
ereinerts@gmail.com
Li Tong
ltong@gatech.edu
Jing Wang
yuyuzaoa@gatech.edu
September 21, 2015
Abstract
Here we review statistical concepts an
ISyE 6414 - Enrichment Project 2
Adrien Ciclet, Yifan Liu, Andrew Lotz, Helin Zhu
November 16, 2014
1
1.1
Stepwise Regression
Test Statistic in Forward Selection
In each step of the forward selection, we compare two models
Y = X1 1 + , Y = X2 2 +
where X
ISYE 6414
Computer Project 1
Team Members:
Evan Bush
Lingxiao Chen
Yu Han
Chang Liu
Andrew Schrader
Simeng Zhang
Fall 2016
Table of Contents
1. INTRODUCTION . 4
2. WORKLOAD DISTRIBUTION . 4
3. SOFTWARE FUNCTIONALITY COMPARISON . 5
a. Menu-Driven 5
i. Exce
Regression Analysis
Homework 1: Solutions
0 = cX.
Then we also
1 a Denote cXi = Xi0 . If we replace Xi by cXi the new sample mean is X
2
have that SX 0 X 0 = c SXX and SX 0 Y = cSXY . Based on these formulas, we can further
derive the formulas of the ne
Question 1: Exploratory Data Analysis. Using a scatterplot describe the relationship (direction and form) between total salary of the team and the salary of the leading
quarterback in the team. Discuss the relationship with and without the two in uential
ISyE 6414 (A&Q) Agenda for 08/31/16 Lecture (Lec.4)
A) Second-pass Regression Analysis Technical Properties
Focus: Mean, Variance and Distribution of LSEs
Question: Why do we need to study these technical
properties?
B) Technical Details:
Define Ci = (xi
ISyE 6414 (A&Q) Agenda for 09/07/16 Lecture (Lec.5)
A) Distribution Properties of LSEs and Prediction
See files 3) and 3-1) for details.
B) MLR (Multiple Linear Regression):
See files 4) and 4-1) for details.
C) MLR Variable Selections
See the MLR Directo