Chapter 4
Simultaneous Inferences and Other Topics in Regression
Analysis
4.1 Joint Estimation of 0 and 1
In chapter 2 we saw how to construct 95% confidence
intervals for 0 and 1. These are correct if we are
considering them separately, but they are not
Chapter 12
Autocorrelation in Time Series Data
This is a modified version of a blog I found on the web
written by Jeffrey S. Rosenthal, 2005. The first paragraph is
mine and I modified the second partially. Any errors are
entirely mine.
Up to this point o
Chapter 6
Multiple Regression I
6.1 Multiple Regression Models
First-Order Model with Two Predictor Variables
1
Meaning of Regression Coefficients
These are best thought of as the expected value of Y, Ecfw_Y,
when all other values are held constant.
0 - T
Chapter 1
Linear Regression with One Predictor Variable
Regression is analysis is used for description and
prediction. It is based on the relation between two or more
quantitative variables.
1.1 Relations between Variables
Functional Relation between Two
Chapter 10
Building the Regression Model II: Diagnostics
10.1 Model Adequacy for a Predictor Variable:
Added-Variable Plots
These are also called partial regression plots or adjusted
variable plots. They provide graphic information about the
marginal impo
Chapter 9
Building the Regression Model I:
Model Selection and Validation
9.1 Overview of Model-Building Process
1
Data Collection and Preparation
Types of Studies
Controlled Experiments
Controlled Experiments with Covariates
Confirmatory Observational St
Chapter 8
Regression Models for
Quantitative and Qualitative Predictors
8.1 Polynomial Regression Models
These are used when either the true functional form is a
polynomial (a rare event) or when the polynomial is a
reasonable nonparametric approximation
Chapter 2
Inferences in Regression and Correlation Analysis
where
Y i is the value of the response variable in the ith trial
0 and 1are the parameters of the intercept and slope
Xi is a known constant, namely, the value of the predictor
variable in the it
Chapter 5
Matrix Approach to Simple Linear Regression Analysis
5.1 Matrices
elements - aij where i=1,.r; j=1,.c
dimension - rows X columns. A is a 2X3 or 2A 3
1
Square Matrix - equal number of rows and columns
Vector - either a single row (row vector) or
Chapter 11
Building the Regression Model III:
Remedial Measures
11.1 Unequal Error Variances Remedial Measures Weighted Least Squares
Earlier we talked about transformation of Y in order to
tackle problems with unequal variances of the error terms.
An oft