Multivariate statistics - a full course
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$20.00
$20.00
This product consists of 32 PDFs covering almost all the videos in my play list "Multivariate statistics - a full course" [June 2022] on youtube. Newer videos might not be included in this product. See which videos that are included below:
01_Matrices and matrix operations for beginners 1.pdf
02_Matrices and matrix operations for beginners 2.pdf
03_Eigenvectors and eigenvalues - simply explained.pdf
04_Eigenvectors and eigenvalues - the math step-by-step.pdf
05_Euclidean distance and the Mahalanobis distance (and the error ellipse).pdf
06_Covariance and the correlation matrix - with simple examples.pdf'
07_PCA 1 the basics - simply explained.pdf
08_PCA 2 the math - step-by-step with a simple example.pdf
09_PCA 3 standardization and how to extract components.pdf
10_PCA 4 how to interpret the weights or loadings and Varimax rotation.pdf
11_Linear discriminant analysis (LDA) - simply explained.pdf
12_MANOVA - explained with a simple example.pdf
13_Hotelling's T-square - explained with a simple example.pdf
14_Sensitivity and specificity - clearly explained.pdf
15_The positive and negative predictive values (PPV and NPV) - simply explained.pdf
16_ROC curve - explained.pdf
17_Validation techniques - explained with simple examples (Hold-out, cross-validation, LOOCV).pdf
18_How to calculate the likelihood ratio.pdf
19_Linear discriminant analysis (LDA) - how to use it as a classifier.pdf
20_Logistic regression 1 the basics - simply explained.pdf
21_Logistic regression how to use it as a classifier.pdf
22_K nearest neighbors (KNN) - explained validation.pdf
23_Mahalanobis distance for classification Machine Learning.pdf
24_Decision trees for classification - explained.pdf
25_Random forest classification - simply explained.pdf
26_Hierarchical clustering - explained.pdf
27_Understanding cluster heat maps.pdf
28_k-means clustering - explained.pdf
29_Principal component regression (PCR) - explained.pdf
30_Partial least squares regression (PLSR) - explained.pdf
31_PLS-DA.pdf
32_Canonical correlation analysis - explained.pdf
01_Matrices and matrix operations for beginners 1.pdf
02_Matrices and matrix operations for beginners 2.pdf
03_Eigenvectors and eigenvalues - simply explained.pdf
04_Eigenvectors and eigenvalues - the math step-by-step.pdf
05_Euclidean distance and the Mahalanobis distance (and the error ellipse).pdf
06_Covariance and the correlation matrix - with simple examples.pdf'
07_PCA 1 the basics - simply explained.pdf
08_PCA 2 the math - step-by-step with a simple example.pdf
09_PCA 3 standardization and how to extract components.pdf
10_PCA 4 how to interpret the weights or loadings and Varimax rotation.pdf
11_Linear discriminant analysis (LDA) - simply explained.pdf
12_MANOVA - explained with a simple example.pdf
13_Hotelling's T-square - explained with a simple example.pdf
14_Sensitivity and specificity - clearly explained.pdf
15_The positive and negative predictive values (PPV and NPV) - simply explained.pdf
16_ROC curve - explained.pdf
17_Validation techniques - explained with simple examples (Hold-out, cross-validation, LOOCV).pdf
18_How to calculate the likelihood ratio.pdf
19_Linear discriminant analysis (LDA) - how to use it as a classifier.pdf
20_Logistic regression 1 the basics - simply explained.pdf
21_Logistic regression how to use it as a classifier.pdf
22_K nearest neighbors (KNN) - explained validation.pdf
23_Mahalanobis distance for classification Machine Learning.pdf
24_Decision trees for classification - explained.pdf
25_Random forest classification - simply explained.pdf
26_Hierarchical clustering - explained.pdf
27_Understanding cluster heat maps.pdf
28_k-means clustering - explained.pdf
29_Principal component regression (PCR) - explained.pdf
30_Partial least squares regression (PLSR) - explained.pdf
31_PLS-DA.pdf
32_Canonical correlation analysis - explained.pdf