Update
July 24, 2022
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"Under
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Lecture
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descriptive
information
about each
lecture are
likely to
change
(sometimes
daily) until
all content is
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Lecture 1,
Part I. This is the first of
a two part lecture on a brief overview of
essentials of matrices and linear algebra as
a prerequisite for the course. Part I
includes the nature of matrices and vectors,
operations (e.g., addition, subtraction,
multiplication), transpose, matrix trace
matrix inverses, etc.
Length of Lecture:
53 minutes, 57 seconds.
Lecture 1, Part II. The
second part of the lecture on Fox's
appendix continues with concepts of linear
algebra, including determinants, vector
geometry, linear combinations, linear
independence (and dependence), vector
subspaces, matrix rank, eigenvalues and
eigenvectors.
Length of Lecture: 56 minutes, 48
seconds.
Lecture 2, Part I: This
is the first lecture of the course from An
Introduction to Statistical Learning.
Since Chapter 1 is simply an overview of
the book, the lecture begins with Chapter
2. Part I discusses what statistical
learning is, reviews ideas of functions
and regression, the nature of theory vs.
empirical observations, training vs.
testing data, among other topics.
Length of Lecture: 1 hour, 24
minutes, 10 seconds.
Lecture 2, Part II: This
is the second part of the lecture,
covering more of Chapter 2 and then into
Chapter 3. Topics covered include the
bias-variance trade-off, classification,
review of regression analysis, standard
errors, p-values, among other topics.
Length of Lecture: 54 minutes, 30
seconds.
Lecture 3, Part I. This
first part of the lecture continues with
Chapter 3 discussing such topics as the
issue of deciding on important variables
for regression, confidence and prediction
intervals, qualitative predictors and
indicator coding, additive vs.
non-additive models (e.g., interactions),
non-linear models (e.g., quadratic),
collinarity and variance inflation factor
(VIF), etc.
Length of Lecture: 1 hour, 7
minutes.
Lecture 3, Part II. We
begin chapter 4 of the book on
classification. Topics include why linear
regression is not sufficient for binary or
polytomous response variables, logistic
regression, interpreting odds in logistic
regression, logit, multiple logistic
regression, introduction to linear
discriminant analysis.
Length of Lecture: 46 minutes, 51
seconds.
Lecture 4, Part I. This
lecture includes a discussion of
classification, including Bayes
classifier, KNN, discriminant analysis,
and other topics.
Length of Lecture: 55
minutes, 19 seconds.
Lecture 4, Part II.
Multivariate analysis of variance (MANOVA)
is discussed. For this, material from Applied
Multivariate Statistics for the Social
Sciences (Pituch & Stevens) was
used.
Length of Lecture: 55 minutes, 39
seconds.
Lecture 5, Part I. More
on the multivariate analysis of variance
(MANOVA), drawing again on material from Applied
Multivariate Statistics for the Social
Sciences (Pituch & Stevens).
Length of Lecture: 55 minutes, 37
seconds.
Lecture 5, Part II. Back
into the main text, Chapter 5 on
resampling methods such as
cross-validation, etc. are discussed, with
a brief introduction to the
bootstrap.
Length of Lecture: 53 minutes, 21
seconds.
Lecture 6, Part I. Overview
of the bootstrap, Chapter 5 of the book.
Length of Lecture: 42 minutes, 53
seconds.
Lecture 6, Part II. Chapter
6 on linear models and regularization.
Length of Lecture: 53 minutes, 27
seconds.
Lecture 7, Part I. Shrinkage
methods.
Length of Lecture: 52 minutes, 24
seconds.
Lecture 7, Part II. More
on shrinkage, lasso regression, etc.
Length of Lecture: 56 minutes, 24
seconds.
Lecture 8, Part I. First
lecture on unsupervised learning.
Length of Lecture: 52 minutes, 30
seconds.
Lecture 8, Part II. More
on unsupervised learning (principal
components, etc.)
Length of Lecture: 59 minutes, 25
seconds.
Lecture 9, Part I. More
on unsupervised learning (exploratory
factor analysis, etc.)
Length of Lecture: 49 minutes, 39
seconds.
Lecture 9, Part II. Continuation
of discussion of exploratory factor
analysis.
Length of Lecture: 57 minutes, 42
seconds.
Lecture 10, Part I. More
in depth coverage of exploratory factor
analysis.
Length of Lecture: 49 minutes, 22
seconds.
Lecture 10, Part II. More
on exploratory factor analysis.
Length of Lecture: 1 hour, 4
minutes, 46 seconds.
Lecture 11, Part I. Concluding
comments on factor analysis and
introduction to structural equation
modeling. Parts I and II of this lecture
drew on material fromModern
Psychometrics with R(Mair,
P., Springer).
Length of Lecture: 53 minutes, 42
seconds.
Lecture 11, Part II. Discussion
of structural equation modeling, drawing
again on material from Modern
Psychometrics with R(Mair,
P., Springer).
Length of Lecture: 1 hour, 2
minutes, 59 seconds.
Lecture 12. Discussion
of structural equation modeling.
