Homework 6 (Written Section)
1. Cook and Weisberg (1999) describe an experiment with turkey growth. Methionine is
an amino acid essential for normal growth in turkeys; if they have too little, the bir
Homework 4
1. Explain in words why when we create confidence intervals and prediction intervals using
a transformed response variable Y we cant simply take the inverse transformation of
the endpoints
STATISTICS 608 Linear Models -EXAM I
February 19, 2015
Students Name:
Students Email Address:
INSTRUCTIONS FOR STUDENTS:
1. There are 9 pages including this cover page.
2. You have exactly 75 minutes
Homework 2: Written Section
1. Show that V ar (Yi ) = V ar (ei ) in the simple linear regression model. (Yes, this should
be that simple.) What did you assume?
2. Define in words only the least square
+
Stat 608 Chapter 2 (and 5)
Simple Linear regression
+ Introduction: Simple Linear
Regression Models
+
Simple Linear Regression (SLR) Models
We always draw a scatterplot first to obtain an idea of th
+
Stat 608 Chapter 5
+
Multiple Linear Regression
Chapter
Multiple predictor variables
ANOVA and ANCOVA
Polynomial Regression
Assumption that model is valid
Chapter
6:
Leverage points
Transformation
+
Stat 608 Chapter 6
+
Regression Diagnostics for Multiple
Regression
1. Draw scatterplots of the data:
Standardized residual plots
Marginal model plots
Inverse response plots
Plots for constant v
+
Stat 608 Chapter 7
Variable Selection
+
Introduction
Problems with multicollinearity:
Even when the model is significant, its possible that no individual
predictors are significant.
Slopes may hav
+
Stat 608 Chapter 1
+
Theme of The Class
It makes
sense to base inferences or conclusions only on
valid models.
A
key step in any regression model, then, is to identify
and address model weaknesses.
STATISTICS 608
Examination 2 Summer 2016
Duration: 120 MINUTES
Total points available: 40 (36 points = 100%)
SHOW ALL CALCULATIONS AND EXPLANATIONS. PARTIAL CREDIT
WILL ACCRUE FOR ALL RELEVANT WORK SH
+
Chapter 8
Logistic Regression
1
+
Introduction and Setup
2
+
Linear Models?
Recall:
a linear model is one that can be written in matrix form as
That is, we can express y as a linear combination of t
Homework 3
1. State the geometric reason that for a dummy variable model with a single dummy
variable (i.e. yi = 0 + 1 xi + ei , where xi = 1 if success, 0 if failure)
that the
Psuch
5
first 5 observa
Homework 7 (Written Section)
1. In a one-way ANOVA model with k = 3 groups and 4 observations per group:
(a) Use the F-statistic in Model Reduction Method 2 to derive a statistic for testing
whether t
Homework 1
Instructions: On this and all homeworks and exams, please be sure your file has a cover page with
your name, email address, course and section number, and homework or exam number typed.
I.
Homework 5 (Written Section)
1. Suppose that for the model yi = + ei , the errors are independent with mean 0. Also
suppose that measurements are taken using one device for the first n1 measurements,
Homework 8 (Written Section)
1. Suppose we are interested in the linear model yi = 0 + 1 x1i + 2 x2i + ei . Also suppose
the columns x1 and x2 of the design matrix for this model have mean 0 and lengt
+
Stat 608 Chapter 4
+
Weighted Least Squares
In Chapter 3, we saw that it is sometimes possible to overcome
nonconstant error variance by transforming Y and/or X . In this
chapter we consider an alte
+
Chapter 9
Serially Correlated Errors
+
Serially Correlated Errors
Data are often collected over time.
Our assumption so far has been 0 correlation among the errors.
Now we use Generalized Least Squa
STATISTICS 608 - Sample Examination 1
Duration: 75 MINUTES
Total points available: 22 (20 points =100%)
EXCEPT IN QUESTION 1, SHOW ALL CALCULATIONS AND EXPLANATIONS. PARTIAL
CREDIT WILL ACCRUE FOR ALL
+
Stat 608
BLUE Notes
+
BLUE: Best Linear Unbiased Estimator
The Gauss-Markov Theorem says that our parameter estimate vector
is BLUE:
Best: Minimum Variance
Linear: A linear combination of Ys (we can
COVER PAGE
STAT 608 Homework 04, Summer 2017
Please TYPE your name and email address
below, then convert to PDF and attach as the
first page of your homework upload.
NAME:
EMAIL:
STATISTICS 608
Homewo
+
Stat 608 Chapter 5
+
Note to self
Add HW question on alternative hypothesis for model reduction
Add discussion on familywise error rate under model reduction
section
2
+
Multiple Linear Regression
+
Stat 608 Chapter 1
+
Theme of The Class
It makes
sense to base inferences or conclusions only on
valid models.
A
key step in any regression model, then, is to identify
and address model weaknesses.
+
Stat 608 Chapter 4
+
Weighted Least Squares
In Chapter 3, we saw that it is sometimes possible to overcome
nonconstant error variance by transforming Y and/or X . In this
chapter we consider an alte
+
Stat 608 Chapter 1
+
Theme of The Class
It makes sense
to base inferences or conclusions only on
valid models.
A key step in
any regression model, then, is to identify
and address model weaknesses
Stat 608 Chapter 3
Anscombes Four Data Sets
Valid Model
is the mean structure correct
is the variance structure correct
SAS Example
Is SLR a vaild model for any of these
datasets?
Using Residuals
One