hamannlab

Andreas Hamann's Video Channel for Statistics and Data Science
University of Alberta, Department of Renewable Resources

Faculty website: http://www.ualberta.ca/~ahamann
Applied statistics course websites: http://tinyurl.com/rr480
Multivariate statistics course website: http://tinyurl.com/mv690

Contact info:
Office: 733 GSB (open door policy)
Email: [email protected]
Phone: (780) 492-6429
Fax: (780) 492-4323

Building a Deep Neural Network (DNN) core with 20 lines of base-R code from scratch
Building a Deep Neural Network (DNN) core with 20 lines of base-R code from scratch
Installing Tensorflow-GPU and Keras for R (and Python) on Windows in 2024
Installing Tensorflow-GPU and Keras for R (and Python) on Windows in 2024
11.5. Graphical Tools for Multivariate Statistics (mv690, graphics)
11.5. Graphical Tools for Multivariate Statistics (mv690, graphics)
2.2. R Setup for Win/Mac and Working Efficiently with Scripts (mv690, r-efficient)
2.2. R Setup for Win/Mac and Working Efficiently with Scripts (mv690, r-efficient)
2.1b. An Introduction to the R Programming Environment (mv690, r-intro)
2.1b. An Introduction to the R Programming Environment (mv690, r-intro)
2.1. An Introduction to the R Programming Environment (rr480, r-intro)
2.1. An Introduction to the R Programming Environment (rr480, r-intro)
3.1. The Convention of Standard Data Tables (rr480, datatables)
3.1. The Convention of Standard Data Tables (rr480, datatables)
3.3. Data Table Concepts and Variable Types (rr480, tableconcepts)
3.3. Data Table Concepts and Variable Types (rr480, tableconcepts)
1.3. Your Univariate Statistical Toolbox (rr480, toolbox)
1.3. Your Univariate Statistical Toolbox (rr480, toolbox)
1.2. Acid Tests for Real Hypotheses: Science 1, Statistics 0 (rr480, hypotheses)
1.2. Acid Tests for Real Hypotheses: Science 1, Statistics 0 (rr480, hypotheses)
1.1. Applied Statistics for the Life & Environmental Sciences (rr480, intro)
1.1. Applied Statistics for the Life & Environmental Sciences (rr480, intro)
14.4. Lab walk trough: Interpretation of CART, MRT, randomForest examples (mv690, cart2)
14.4. Lab walk trough: Interpretation of CART, MRT, randomForest examples (mv690, cart2)
14.3. Multivariate Classification and Regression Trees: CART, MRT (mv690, cart1)
14.3. Multivariate Classification and Regression Trees: CART, MRT (mv690, cart1)
14.2. Gradient Analysis Lab: Interpretation of CanCor, CCA, RDA Examples (mv690, gradient2)
14.2. Gradient Analysis Lab: Interpretation of CanCor, CCA, RDA Examples (mv690, gradient2)
14.1. Multivariate Gradient Analysis: CanCor, CCA, RDA (mv690, gradient1)
14.1. Multivariate Gradient Analysis: CanCor, CCA, RDA (mv690, gradient1)
13.2. Multivariate Statistical Testing - MRPP, perMANOVA (mv690, permanova)
13.2. Multivariate Statistical Testing - MRPP, perMANOVA (mv690, permanova)
13.1. Multivariate Inferential Statistics, MANOVA, MRPP, perMANOVA (mv690, inferential)
13.1. Multivariate Inferential Statistics, MANOVA, MRPP, perMANOVA (mv690, inferential)
12.3. Multivariate Clustering Techniques (mv690, cluster)
12.3. Multivariate Clustering Techniques (mv690, cluster)
12.2 Distance-based Ordinations - NMDS, MDS, PCoA, PO (mv690, ordination)
12.2 Distance-based Ordinations - NMDS, MDS, PCoA, PO (mv690, ordination)
12.1. Distance-based Multivariate Methods (mv690, distance)
12.1. Distance-based Multivariate Methods (mv690, distance)
11.7. Assumptions of Classical Multivariate Methods (mv690, assumptions)
11.7. Assumptions of Classical Multivariate Methods (mv690, assumptions)
11.6. Discriminant Analysis, MANOVA (mv690, discrim)
11.6. Discriminant Analysis, MANOVA (mv690, discrim)
11.4. Factor Analysis (mv690, factor)
11.4. Factor Analysis (mv690, factor)
11.2. PCA continued, Matrix Rotation (mv690, pca2)
11.2. PCA continued, Matrix Rotation (mv690, pca2)
11.3. Lab walk-through: PCA Interpretation (mv690, pca3)
11.3. Lab walk-through: PCA Interpretation (mv690, pca3)
11.1. The Concept of Coordinate Rotation & PCA (mv690, pca1)
11.1. The Concept of Coordinate Rotation & PCA (mv690, pca1)
Project support - Touch-up of R graphics in Powerpoint
Project support - Touch-up of R graphics in Powerpoint
Project support - Graphics plots with log scales in R
Project support - Graphics plots with log scales in R
10.3. Tests for Binomial Data (rr480, binomial)
10.3. Tests for Binomial Data (rr480, binomial)
10.2. Non-Parametric Rank Sum Tests (rr480, ranksum)
10.2. Non-Parametric Rank Sum Tests (rr480, ranksum)