STAT 3032: Homework 5
Adam Maidman
November 16, 2015
Problem 7.7
1
Adam Maidman
STAT 3032: Homework 5
Problem 7.8 (1-3)
2
November 16, 2015
Adam Maidman
STAT 3032: Homework 5
3
November 16, 2015
Adam Maidman
STAT 3032: Homework 5
Problem 8.1
4
November 16
STAT 3032: Homework 1
Adam Maidman
September 22, 2015
Problem 2.1
1
Adam Maidman
STAT 3032: Homework 1
2
September 22, 2015
Adam Maidman
STAT 3032: Homework 1
Problem 2.13
3
September 22, 2015
Adam Maidman
STAT 3032: Homework 1
Problem 2.16
4
September 22
STAT 3032: Homework 2
Adam Maidman
September 30, 2015
1
Adam Maidman
STAT 3032: Homework 2
Problem 2.14
2
September 30, 2015
Adam Maidman
STAT 3032: Homework 2
Problem3.1
3
September 30, 2015
Adam Maidman
STAT 3032: Homework 2
Problem 3.2
4
September 30,
STAT 3032: Homework 3
Adam Maidman
October 19, 2015
1
Adam Maidman
STAT 3032: Homework 3
Problem 4.1
2
October 19, 2015
Adam Maidman
STAT 3032: Homework 3
3
October 19, 2015
Adam Maidman
STAT 3032: Homework 3
Problem 5.3
4
October 19, 2015
Adam Maidman
ST
STAT 3032: Practice Exam 2
Xuetong Sun
April 5, 2016
Problem 2
(1) The intercept when group is oecd is 82.4. The test is H0 :
H1 : 0 6= 0, other 0 s arbitrary.
0
= 0, other
0s
arbitrary vs
The dierence between the means for oecd and other is
0, other 0 s
STAT 3032: Practice Exam 2
Adam Maidman
October 29, 2015
Problem 2
(1) The intercept when group is oecd is 82.4. The test is H0 : 0 = 0, other s arbitrary vs
H1 : 0 = 0, other s arbitrary.
The dierence between the means for oecd and other is 7.1.
0, other
STAT 3032: Homework 6
Adam Maidman
December 1, 2015
Problem 9.8
1
Adam Maidman
STAT 3032: Homework 6
2
December 1, 2015
Adam Maidman
STAT 3032: Homework 6
Problem 9.10
3
December 1, 2015
Adam Maidman
STAT 3032: Homework 6
Problem 9.11
Problem 9.16
4
Decem
STAT 3032
Chenxiang Ji
5131867
STAT 3032 HW4
Lab Sec 002(10:10 11:05)
10/27/2015
#Problem 6.1
Solution:
Analysis of Variance Table
Model 1: lifeExpF ~ 1
Model 2: lifeExpF ~ group
Res.Df
RSS Df Sum of Sq
1
196 7730.2 2
Pr(>F)
198 20293.2
2
F
12563 159.27 <
STAT 3032 Regression and Correlated Data
Chapter 5
Ding Xiang
School of Statistics
University of Minnesota
Modified from Prof. Gongjun Xus slides
February 13, 2017
Ding Xiang (University of Minnesota)
STAT 3032 Regression and Correlated Data
February 13,
STAT 3032 Regression and Correlated Data
Chapter 2
Ding Xiang
School of Statistics
University of Minnesota
Modified from Prof. Gongjun Xus slides
January 20, 2017
Ding Xiang (University of Minnesota)
STAT 3032 Regression and Correlated Data
January 20, 20
STAT 3032 Lab3
Yuanchen Su
Feb 7, 2017
Question 3.1
# Question 3.1
library(alr4)
#
#
#
#
#
#
#
#
Loading required package: car
Warning: package car was built under R version 3.3.2
Loading required package: effects
Attaching package: effects
The following
Lab 1: R installation and some basic operations
Yuanchen Su
January 24, 2017
1) Install R and R Studio. There is a separate file with step-by-step instructions on how to intall R and R
Studio on your own computer attached on Moodle. If you do not have you
STAT 3032 Regression and Correlated Data
Chapter 10
Adam Maidman
School of Statistics
University of Minnesota
November 21, 2016
Adam Maidman (University of Minnesota) STAT 3032 Regression and Correlated Data
November 21, 2016
1 / 26
Ch10. Variable Selecti
STAT 3032 Regression and Correlated Data
Chapter 7
Adam Maidman
School of Statistics
University of Minnesota
October 31 , 2016
Adam Maidman (University of Minnesota) STAT 3032 Regression and Correlated Data
October 31 , 2016
1 / 11
Chapter 7 Variances
In
STAT 3032 Regression and Correlated Data
Chapter 8
Adam Maidman
School of Statistics
University of Minnesota
November 2, 2016
Adam Maidman (University of Minnesota) STAT 3032 Regression and Correlated Data
November 2, 2016
1 / 31
Chapter 8 Transformations
STAT 3032
Regression and
Correlated Data
Lecture 31, Dec 2
STAT 3032
2. Time Series Regression and EDA
2.1 Classical Regression for Time Series
Mean function
Variance function
STAT 3032
2.1 Classical Regression for Time Series
Example 2.1 Estimating a Lin
STAT 3032 Regression and Correlated Data
Chapter 10
Ding Xiang
School of Statistics
University of Minnesota
Modified from Prof. Gongjun Xus slides
April 13, 2016
Ding Xiang (University of Minnesota)
STAT 3032 Regression and Correlated Data
April 13, 2016
STAT 3032 Regression and Correlated Data
Chapter 6
Ding Xiang
School of Statistics
University of Minnesota
Modified from Prof. Gongjun Xus slides
March 1, 2017
Ding Xiang (University of Minnesota)
STAT 3032 Regression and Correlated Data
March 1, 2017
1 /
STAT 3032 Regression and Correlated Data
Chapter 3&4
Ding Xiang
School of Statistics
University of Minnesota
Modified from Prof. Gongjun Xus slides
February 1, 2017
Ding Xiang (University of Minnesota)
STAT 3032 Regression and Correlated Data
February 1,
STAT 3032: Practice Exam 2
Yannan Pan
April 5, 2016
Problem 2
(1) The intercept when group is oecd is 82.4. The test is H0 :
H1 : 0 6= 0, other 0 s arbitrary.
0
= 0, other
0s
arbitrary vs
The dierence between the means for oecd and other is
0, other 0 s a
STAT 3032 Regression and Correlated Data
Chapter 9
Ding Xiang
School of Statistics
University of Minnesota
Modified from Prof. Gongjun Xus slides
April 7, 2017
Ding Xiang (University of Minnesota)
STAT 3032 Regression and Correlated Data
April 7, 2017
1 /
STAT 3032 Regression and Correlated Data
Chapter 7
Ding Xiang
School of Statistics
University of Minnesota
Modified from Prof. Gongjun Xus slides
March 20 , 2016
Ding Xiang (University of Minnesota)
STAT 3032 Regression and Correlated Data
March 20 , 2016
Exam III Practice Problems
1. Lecture 27-30, pages 6-7. Based on the residual plots, are there any problems with these regressions?
2. Lecture 27-30, pages 10 and 12. Explain the testing results.
3. Problem 9.10
4. Whats the difference between outliers an
STAT 3032: Practice Exam 3
Xuetong Sun
May 1, 2016
Problem 1
Residual Plots: The mean function has an assumption of constant variance. Therefore, a linear
model should have a plot that has no visible trends which we call the null plot.
Lecture 27-30, page
STAT 3032 Regression and Correlated Data
Chapter 8
Ding Xiang
School of Statistics
University of Minnesota
Modified from Prof. Gongjun Xus slides
March 24, 2017
Ding Xiang (University of Minnesota)
STAT 3032 Regression and Correlated Data
March 24, 2017
1
STAT 3032
Regression and
Correlated Data
Lecture 31, Dec 2
STAT 3032
2. Time Series Regression and EDA
2.1 Classical Regression for Time Series
Mean function
Variance function
STAT 3032
2.1 Classical Regression for Time Series
Example 2.1 Estimating a Lin
STAT 3032
Regression and
Correlated Data
Lecture 32, Dec 5
STAT 3032
3. ARIMA models
3.1 Introduction
Classical regression is insufficient for explaining all
of the interesting dynamics of a time series.
E.g., the ACF of the residuals of the simple linea
STAT 3032: Homework 3
Xinpeng Shen
October 26, 2016
Problem 5.3
1
Xinpeng Shen
STAT 3032: Homework 5
2
October 26, 2016
Xinpeng Shen
STAT 3032: Homework 5
Problem 5.4
3
October 26, 2016
Xinpeng Shen
STAT 3032: Homework 5
4
October 26, 2016
Xinpeng Shen
ST