Simple Linear Regression
Lecture 7
EFB 222
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Outline
Assumptions
Gauss-Markov Theorem
Sampling distribution of OLS estimators
Example
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Ordinary Least Squares (OLS)
When can we use OLS?
Why
Simple Linear Regression
Lecture 8
EFB 222
1 / 43
Outline
Condence intervals
t Test
p value approach to testing
The mechanics of testing
Assessing the model
Examples
From walking to ying.
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Ordin
Sampling and Sampling Distribution
Lecture 2
EFB 222
1 / 38
Outline
Sampling, parameters, estimators and estimates
Sampling distribution of the sample mean X
Central Limit Theorem
Normal distribution
Multiple Linear Regression
Lecture 9
EFB 222
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Outline
The Multiple Linear Regression Model
Interpretation
Assumptions
Gauss-Markov Theorem
Estimation
Example
Sampling distribution of OLS estimat
Derivation of Ordinary Least Squares Estimators
where is a dependent variable, is an independent right-hand side (RHS) variable, is the
error term (unobservable), are coefficients. The ordinary least
Question 2.8
A function Y = (X) is said to be linear in X if:
1. X appears with a power of 1 only, therefore exponentials or roots of X are
excluded; and
2. X is not multiplied or divided by another v
The Wonders of Econometrics
Lecture 1
EFB 222
1 / 54
Outline
Plan for the unit
Sequences
The BIG Sigma
Experiments, possible outcomes, and events
Random Variables
Probability Distributions
Descriptive
Gauss-Markov Theorem Explained
The Gauss-Markov Theorem is essentially a claim about the ability of regression to
assess the relationship between a dependent variable and one or more independent
varia
Statistical Inference - part I
Lecture 3
EFB 222
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Outline
Point estimate and interval estimate
Properties of point estimators
2 / 19
Point estimate and interval
estimate
3 / 19
Point estimate
Po
Statistical Inference - part II
Lecture 4
EFB 222
1 / 27
Outline
Hypothesis testing
Signicance level, type I and II errors
Statistical tables
Data and Variables
2 / 27
Hypothesis testing
3 / 27
Starti
Question 2.8
A function Y = (X) is said to be linear in X if:
1. X appears with a power of 1 only, therefore exponentials or roots of X are
excluded; and
2. X is not multiplied or divided by another v
EFB222 - Tutorial 1
Question 1
Please refer to page 430 of your text and complete sections A.8 and A.9.
Question 2
A racing car valued at $200 000 has the probability of being a total loss estimated a