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 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
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
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 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 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 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 Class 11
Principal Components and Factor
Analysis II
Outline:
1. A related topic: scale development/psychometrics
Concepts of reliability and validity
Cronbachs alpha
Item analysis via PC, alpha
2. Creating subscales/factor scores
Using factor analy
MA684 Class 11
Principal Components and Factor
Analysis II
Outline:
0. Creating subscales/factor scores
1. Scale Development/psychometrics
Reliability and validity
Cronbachs alpha
Item analysis via PC, alpha
2. Options in rotations, PC/factor analysis
Ort
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
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
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
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
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
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
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 Class 11
Principal Components and Factor
Analysis
Example:
Benefits and barriers to exercise
Two BU graduate students interested in factors
associated with:
whether or not women participate in regular exercise
what type of exercise women participate
MA684 Class 13
Analysis of longitudinal data
Example: Behavioral Intervention aimed at
improving exercise
Enroll sedentary adults
Baseline evaluation
Includes minutes/week moderate exercise
Randomize to intervention or control
Follow for 6 months
Fo
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 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
Dawei Zhang
U65343289
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 practi
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
Dawei Zhang
U65343289
MA684 Class 9 Homework
Logistic Regression II
1. From the voting study in last weeks assignment. Last week you ran a multiple logistic
regression predicting whether or not a registered voter voted, based on their age, sex, and
politi
MA684 Class 6
Interaction models
1. What is statistical interaction?
Statistical interaction refers to effect modification, where the effect of one variable on some
outcome changes, depending on the level of a third variable. Interaction can occur in a nu
Dawei Zhang
U65343289
MA684
Homework from Class 11
Factor Analysis II
A study examined a number of factors that might impact quality of life. We will focus on
depressive symptoms, as measured by the CES-D (Center for Epidemiologic Studies
Depression Scale
MA684 Homework from Class 4
Multiple linear regression Solutions
Question 1. How well does high school GPA predict performance in college? A
university admissions office found the correlation between high school GPA and the GPA
after freshman year to be r
MA684 HW 5
Multiple Regression II
Question 1. Data from Prof. Bernard Rosner, Prof. of Biostatistics across town, posted
on line. The attached data set provides data on lung function for a sample of 654 kids
between the ages of 3 and 18. We are interested
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
/*Q1*/
PROC CORR data=sbp;
var BslSBP FinalSBP;
quit;
run;
/*Q2*/
PROC ttest data=sbp;
var BslSBP;
class treat;
quit;
/*Q3*/
data decline;
set sbp;
decline=FinalSBP-BslSBP;
run;
proc sort data=decline;
by treat;
run;
proc means data=decline;
var decline;
1.a
According to correlation test, the unadjusted correlation coefficient between final SBP and
baseline SBP is 0.79548.
b. null hypotheses: the true correlation is 0
alternative hypotheses: The correlation coefficient is significantly not 0.
suppose Bsls
MA684
Homework from Class 3
Xiao Zeng UID U32757637
1D. Find these intervals using a statistical computing package (SAS and SPSS users,
note that ID=10 has a femur length of 18 inches).
95% confidence interval (60.57, 61.37)
95% prediction interval (58.58
MA684 Homework from Class 2 2017
Simple Linear Regression
Material from this class was covered in the Feb 1 lecture and is covered in Chapter 5 of the
recommended textbook (but you do not need to refer to the text book). You may use SAS,
SPSS or R to answ