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 would we use OLS?
OLS estimators are random variables.
Simple Linear Regression
Lecture 8
EFB 222
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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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Ordinary Least Squares (OLS)
When can we use OLS?
Why would
Sampling and Sampling Distribution
Lecture 2
EFB 222
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Outline
Sampling, parameters, estimators and estimates
Sampling distribution of the sample mean X
Central Limit Theorem
Normal distribution
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Sampling, parameters,
estimators and estimates
3
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 estimators
Coefcient of determination
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The Multiple Line
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 squares procedure
minimizes the error sum of squares (S
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 variable.
For regression model purposes, we are interest
The Wonders of Econometrics
Lecture 1
EFB 222
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Outline
Plan for the unit
Sequences
The BIG Sigma
Experiments, possible outcomes, and events
Random Variables
Probability Distributions
Descriptive Statistics
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Plan for the unit
Soft landing to re
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
variables. The Gauss-Markov Theorem, however, requires that
Statistical Inference - part I
Lecture 3
EFB 222
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Outline
Point estimate and interval estimate
Properties of point estimators
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Point estimate and interval
estimate
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Point estimate
Point estimate is the single value estimate for the unkno
Statistical Inference - part II
Lecture 4
EFB 222
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Outline
Hypothesis testing
Signicance level, type I and II errors
Statistical tables
Data and Variables
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Hypothesis testing
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Starting point, research point, hypotheses
Starting point: Cu
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 variable.
For regression model purposes, we are interest
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 at 0.002, a
50% loss with probability 0.01, and a 25% lo