Linear Algebra

Versions of spectral theorems; positive operators
Versions of spectral theorems; positive operators
Real Spectral Theorem
Real Spectral Theorem
EM algorithm and missing data part 2
EM algorithm and missing data part 2
4 12 16
4 12 16
Analysis of Discrete Data Lesson 11: Ordinal and dependent data
Analysis of Discrete Data Lesson 11: Ordinal and dependent data
Analysis of Discrete Data Lesson 11: Structural and Sampling Zeros part 3
Analysis of Discrete Data Lesson 11: Structural and Sampling Zeros part 3
Analysis of Discrete Data Lesson 11: Sampling and Structural Zeros part 2
Analysis of Discrete Data Lesson 11: Sampling and Structural Zeros part 2
Analysis of Discrete Data Lesson 11: Sampling and Structural Zeros part 1
Analysis of Discrete Data Lesson 11: Sampling and Structural Zeros part 1
Analysis of Discrete Data Lesson 9: Poisson regression and log linear models part 2
Analysis of Discrete Data Lesson 9: Poisson regression and log linear models part 2
Analysis of Discrete Data Lesson 9/10: Poisson regression and log linear models part 3
Analysis of Discrete Data Lesson 9/10: Poisson regression and log linear models part 3
Analysis of Discrete Data: Model Selection, Akaike and Bayesian information criterion
Analysis of Discrete Data: Model Selection, Akaike and Bayesian information criterion
Analysis of Discrete Data Lesson 9: Poisson Regression and Poisson GLMs part 1
Analysis of Discrete Data Lesson 9: Poisson Regression and Poisson GLMs part 1
Analysis of Discrete Data Lesson 8: Multinomial Logistic Regression Part 2
Analysis of Discrete Data Lesson 8: Multinomial Logistic Regression Part 2
Analysis of Discrete Data Lesson 8: Multinomial Logistic Regression Part 1
Analysis of Discrete Data Lesson 8: Multinomial Logistic Regression Part 1
Analysis of Discrete Data Lesson 7: Logistic regression
Analysis of Discrete Data Lesson 7: Logistic regression
Analysis of Discrete Data Lesson 6 Part 2
Analysis of Discrete Data Lesson 6 Part 2
Analysis of Discrete Data Lesson 6 part 1: generalized linear models (GLMs) and logistic regression
Analysis of Discrete Data Lesson 6 part 1: generalized linear models (GLMs) and logistic regression
Analysis of Discrete Data Lesson 5 Part 2: Three way tables
Analysis of Discrete Data Lesson 5 Part 2: Three way tables
Analysis of Discrete Data Lesson 5: Three-way tables, association and independence
Analysis of Discrete Data Lesson 5: Three-way tables, association and independence
Analysis of Discrete Data Lesson 4 Part 2: ordinal data and dependent samples in two by two tables
Analysis of Discrete Data Lesson 4 Part 2: ordinal data and dependent samples in two by two tables
Lesson 4 Two way tables with ordinal data
Lesson 4 Two way tables with ordinal data
Lesson 4 Dependent Samples in two way data
Lesson 4 Dependent Samples in two way data
Analysis of Discrete Data Lesson 4 Part 1: prospective, retrospective, I by J tables
Analysis of Discrete Data Lesson 4 Part 1: prospective, retrospective, I by J tables
Analysis of Discrete Data Lesson 3: Two-way tables, independence, sampling schemes, goodness of fit
Analysis of Discrete Data Lesson 3: Two-way tables, independence, sampling schemes, goodness of fit
Analysis of Discrete Data Lesson 2 Part 2
Analysis of Discrete Data Lesson 2 Part 2
Analysis of Discrete Data Lesson 2 Part 1
Analysis of Discrete Data Lesson 2 Part 1
Analysis of Discrete Data Lesson 1: Likelihood and maximum likelihood
Analysis of Discrete Data Lesson 1: Likelihood and maximum likelihood
What are the Bernoulli, binomial, and multinomial Distributions?
What are the Bernoulli, binomial, and multinomial Distributions?
Types of categorical data: overview
Types of categorical data: overview
Adjoints etc 3: properties, normal, complex spectral theorem
Adjoints etc 3: properties, normal, complex spectral theorem