Lecture 11
Residual Analysis (III)
-Assessing Independence
Assessing the Assumption That the
Disturbances are Independent
If the disturbances are independent, the
residuals should not display any pat
Lecture 15
One-Way Analysis of Variance
ANOVA
Analysis of variance was a term used in
regression to describe how we split the
variation in our sample into "explained"
and "unexplained" parts.
In thi
Lecture 5
Multiple Regression Analysis (Part II)
Comparing Two Regression Model
Comparing Two Regression Models
So far we have looked at two types of
hypothesis tests. One was about the
overall fit:
H
Lecture 14
Variable Selection
Introduction
One of the primary tasks in this course is
to choose which variables to include in the
regression equation: The F test for overall
fit, the partial F test ,
Tmath 110 Autumn 2016
Name: EQg
Final Exam
1. We are interested in the population mean weight of a certain type of egg. A sample of
55 eggs yields a sample mean weight of 1.76 ounces. For this problem
Autumn 2016
JSIS 200A. States and Capitalism:
The Origins of the Modern Global System
Anand A. Yang ([email protected])
Tel. 206-543-4902, 308C Smith
Office Hours: M 11-12.15
and by appointment
Teaching Assi
Chapter 3
Simple Regression Analysis
(Part 1)
Using Simple Regression to Describe a
Relationship
Regression analysis is a statistical technique
used to describe relationships among variables.
The si
Lecture 13
Forecasting Methods for Time Series Data
Introduction
Time-series data are data gathered on a single
unit (person, firm, etc.) over a sequence of time
periods.
These time periods may be y
Lecture 8
Residual Analysis (I)
-Assessing Linearity Assumption-
The multiple linear regression model is
yi = 0 + 1 x1i + 2 x2i + L + k xki + ei
Certain assumptions were made about how
the errors ei b
Lecture 12
Regression with Indicator Variables
Using and Interpreting Indicator Variables
Indicator or dummy variables are a special type of variable
Indicator variables take on two values: one and ze
Lecture 7
Fitting Curves to Data
Background
Multiple linear regression model was presented as:
yi = 0 + 1 x1i + 2 x2i + L + k xki + ei
where we assumed linear relationships between
y and the x variabl
Lecture 3
Simple Regression Analysis
(Part 2)
Estimating the Conditional Mean of y
Given x.
At xm = 40 , the quantity we are estimating is:
y| x = 40 = 0 + 1 40
Our best guess of this is just the poi
Lecture 9
Residual Analysis (II)
-Assessing Constant Variance-
Constant variance
Assumptions states that the errors i should
have the same variance everywhere.
This implies that if residuals are plo
Lecture 4
Multiple Regression Analysis
(Part 1)
Multiple Regression Analysis
For two independent variables, the general
form of the multiple regression equation is:
Yi = 0 + 1 X1i + 2 X2i + i
X1i an
Two-Way ANOVA
In this situation, there are two factors or
explanatory variables.
For example, suppose a company is going
to experiment with two price levels and
three types of advertising.
Now a "t
Lecture 10
Residual Analysis (III)
-Assessing Normality
-Influential Observations
Assessing the Assumption That the
Disturbances are Normally Distributed
There are many tools available to check the
as
Lecture 6
Time Series Regression
-Lagged Explanatory Variables
Lagged Variables as Explanatory Variables
in Time-Series Regression
When using time series data in a regression, the
relationship betwee
Family of regression models
Outcome variable determines choice of model
Outcome
Continuous
Survival
Categorical
Model
Linear regression
Cox model
Logistic regression
Uses
Estimate force of associa
Lecture 13
Regression with Interaction Variable
Some important facts about Indicator variables:
Always pick one reference category first.
The coefficients of indicator variables are not really slopes