MA684 Homework from Class 2 2016
Simple Linear Regression
Material from this class is covered in Chapter 5 of KKM and N.
1. (Based on an example from the Chapter 5 exercises in KKM and N) Do better students get
better jobs? 30 recent graduates from a part
MA684
Homework from Class 3 Solutions
Read about PIs and CIs in Chapter 5 of KKM and N, and R2 and the ANOVA for the regression
in Chapter 7.
1. Some results from the regression predicting height (in inches) from femur length (in inches),
from last weeks
MA684 Homework from Class 2
Simple Linear Regression
Material from this class is covered in Chapter 5 of KKM and N.
1. (Based on an example from the Chapter 5 exercises in KKM and N) A study was conducted
to evaluate the relationship between driving speed
MA 684
Regression and Multivariate Analysis
Spring 2016
Instructor:
Recommended
Text:
Requirements:
Tim Heeren
Medical Campus: Crosstown Building, Room 309, 617 638-5177
Charles River (Monday): 64 Cummington St., Room 235
Office hours at Charles River Cam
MA684 Class 11 Homework
Principal Components / Factor Analysis
(From an on-line data set from Professor James Sidanius, UCLA) The Faculty
Evaluation data set contains data on course evaluations from 400 students, covering a
number of courses and instructo
MA684 Class 10 Homework Solutions
Logistic Regression II
Question 1. (No computer work necessary, other than finding p-values for chi-square
statistics) From the voting study in last weeks assignment. Last week you ran a multiple
logistic regression predi
MA684
1
Some Review Problems
Class
1.
Based on Problem 13, Ch. 3. The following gives weight loss (in
pounds) over a three week period for people initiating two different weightloss programs:
Diet A: 21 , 33 , 35 , 42 , 36 , 34 , 13, 39, 40
Diet B: 24 , 2
MA684_homework_06
Sample Solution
October 5, 2016
Introduction
The most important part of homework 5 is reading.
If you have questions our office hours are
Masanao: Tuesday 3:00PM to 4:30PM
Yitong: Thursday 1:00PM to 2:00PM
Please come see us we are her
MA684 Homework from Class 2
Simple Linear Regression
Material from this class is covered in Chapter 5 of KKM and N.
1. (Based on an example from the Chapter 5 exercises in KKM and N) A study was conducted
to evaluate the relationship between driving speed
MA684 Homework from Class 4
Multiple linear regression
This material is covered in Chapter 8 and 9 of KKM and N. Sections 8.1 8.5 give
background information on the theory of multiple regression. Sections 8.6 and 8.7 give
more practical examples of multip
MA684 HW from Class 5 Solutions
Linear Regression with Categorical Predictors
1. (No compter work needed) Based on a study of maternal behavior during pregnancy,
headed by Dr. Debbie Frank. Were interested in the association between maternal alcohol use
d
HW 6 R commands and output
Not part of the assignment, but to set up population-average
coding for dummy variables used in Question 1, Model B. I used
the table() commands to check on the creation of the dummy
variables.
>
> table(alcexp)
alcexp
0
1
2
184
MA 684 Class 6
Categorical Predictors
Interaction variables
Stepwise Regression
From last class:
Categorical Predictors
Voting Survey: Factors associated with political
awareness
Regression with categorical predictors:
Programming issues
Using standard re
MA684
( TO BE MA678)
CLASS 1
WELCOME TO MA684
Instructor: Masanao Yajima (yajima[at]bu[dot]edu )
TA: Iris (Shiyi) Yang( syyang[at]bu[dot]edu )
OVERVIEW OF 684/685 SEQUENCE
This is the methodological leg of the program.
Understanding and DOING statisti
Linear Regression
Math of Estimation
Masanao Yajima
September 8, 2017
Estimation
Properties of Estimators
Introduction
In this lecture note we will go through the math of Maximum Likelihood Estimation. It is # Vocaburalies
for properties of general estima
Welcome to MA684
Masanao Yajima
Department of Mathematics and Statistics
09/05/2017
Class 1
Welcome to MA684 (will be MA678)
Welcome to MA684
Instructor: Masanao Yajima ( yajima[at]bu[dot]edu )
TA: Iris (Shiyi) Yang (syyang[at]bu[dot]edu)
Textbooks
Dat
Agenda
Confidence Interval
P-value
Hypothesis testing
Multiple Comparison
Logistic Regression
Confidence Interval = region of uncertainty
From the random sampling argument we are indifferent about the
values inside of the confidence interval.
1- l
MA684 HW from Class 6
Linear Regression with Categorical Predictors
SOLUTION
1. (No computer work needed - based on a study of maternal behavior during pregnancy,
headed by Dr. Debbie Frank at Boston Medical Center). Were interested in the association
bet
MA684 Discussion
September 06, 2017
= Regression analysis =
Data: Munich Rent Index
The goal of a regression analysis for this data is to model the impact of explanatory variables (living area,
year of construction, location, etc.) on the response variabl
Ludwig Fahrmeir
Thomas Kneib
Stefan Lang
Brian Marx
Regression
Models, Methods
and Applications
Regression
Ludwig Fahrmeir
Brian Marx
Thomas Kneib
Stefan Lang
Regression
Models, Methods and Applications
123
Ludwig Fahrmeir
Department of Statistics
Univers
Interpreting Regression Models
Masanao Yajima
Department of Mathematics and Statistics
September 7, 2017
Recap of the last class
Regression is comparison
Baseline
Measurement unit
Uncertainty and variability
Assumptions
Substantive context is important
Prediction Intervals and Confidence
Intervals for Predicted Values from
Multiple Linear Regression
Intervals for predicted values
Concept parallel to simple linear regression
Predicted value from a multiple regression can
be viewed in 2 ways:
Predicted
Example: Multiple regression to control for
confounding
Example: Multiple regression to control for
confounding
Example: Multiple regression to
control for confounding
Linear regression predicting sleeptime from sexfemale
Analysis of Variance
Source
DF
Su
MA684 Class 9
Logistic Regression II
Outline
Test, description of the overall model
A little theory likelihood estimation,
testing
Multiple partial tests for logistic
regression
Goodness-of-Fit for the model
Programming considerations for
logistic re
MA684 Class 10
Principal Components and Factor
Analysis
Goals:
Understand when and why principal components or
factor analysis is appropriate
Understand theoretical distinction between factor
analysis and principal components analysis
Interpret the result
MA684 Class 2
Simple Regression
Presenting Question: CSI statisticians! (from the Forensic Pathology Program, BUMC). What
can we say about a person based on partial skeletal remains? In particular, we want to estimate
a persons height, based on their femu
Two Examples
1. Voter survey
Interested in factors associated with political
awareness (score), whether or not people vote
Predictors: age, sex, education, income, political
party (democrat, republican, independent)
2. Framingham Heart Study
Predict
MA684 Class 9
Multiple Logistic Regression 2
1. Introduction/review from Class 8
Linear regression (and multiple linear regression) is used to model associations with a
measurement dependent (outcome) variable. Logistic regression (and multiple logistic
r
Homework 02
yourname
Septemeber 21, 2017
Introduction
In homework 2 you will fit many regression models. You are welcome to explore beyond what the question is
asking you.
Please come see us we are here to help.
Data analysis
Analysis of earnings and heig