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*Simple and Multiple Linear Regression.
*Gauss Markov assumptions and theory.
*Finite Sample Properties of Ordinary Least Squares (OLS) Method (including proof of unbiasedness).
*Hypothesis Testing (including Normal, t, F and Chi-squared tests).
*Variable Misspecification (excluding a relevant variable, including an irrelevant variable).
*Homoskedasticity and Heteroskedasticity.
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SECTION 1: SIMPLE LINEAR REGRESSION
1
Using mathematics to get a straight line of best fit 05:17
2
Intuition of Hypothesis Testing & OLS Formula Part A 09:38
3
Intuition of Hypothesis Testing & OLS Formula Part B 11:40
4
Estimator Bias 13:01
5
Causes of Bias 09:34
6
Estimator Variance 09:04
7
OLS Decomposition Derivation 11:53
8
OLS Estimators are Unbiased Part A 13:03
9
OLS Estimators are Unbiased Part B 05:02
10
Gauss Markov Theorem & Assumptions Part A 13:09
11
Gauss Markov Theorem & Assumptions Part B 04:55
12
OLS Estimator Variance 06:41
SECTION 2: MULTIPLE LINEAR REGRESSION
13
Matrix Notation Part A 13:40
14
Matrix Notation Part B 04:25
15
Gauss-Markov Assumptions 07:11
16
OLS is Unbiased 06:05
17
OLS Estimator Variance 05:47
SECTION 3: HYPOTHESIS TESTING
18
Intuition of the Two Methods for Hypothesis Testing 10:07
19
Notation 05:37
20
RSS Method 11:18
21
Wald Method 07:56
22
Single Linear Restriction 05:06
SECTION 4: GAUSS-MARKOV ASSUMPTIONS NOT HOLDING...
23
Variable Misspecification Introduction 07:31
24
Variable Misspecification Matrix Notation 05:01
25
Variable Misspecification: Exclusion of a Relevant Variable 02:49
26
Variable Misspecifcation: Inclusion of an Irrelevant Variable 02:55
27
Multicollinearity 10:51
28
Heteroskedasticity 08:52
SECTION 5: SECTION 5: THE END!
29
Concluding comments
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