TileStats

Understanding derivatives - the basics

PCA : the math - step-by-step with a simple example

Meta-analysis | The inverse variance method | Forest plot in R

Statistical power - Parametric vs Nonparametric test

Expected value vs mean

Multinomial logistic regression | softmax regression | explained

Eulers method | Numerial methods

PERMANOVA and permutation tests - explained

How to identify and deal with outliers | The 1.5 IQR rule | Boxplots

The SIR model | the math of epidemics - explained with a simple example

Understanding exponential growth | discrete vs continuous growth

How to compute a p value and extract a critical value in R

SPSS for beginners in 22 minutes - enter data, plot and t-test

Support Vector Machines (SVM) - the basics | simply explained

Artificial neural networks (ANN) - explained super simple

Logistic regression : the basics - simply explained

Linear regression | the R-squared value

K nearest neighbors (KNN) - explained | validation

Assumptions in Linear Regression - explained | residual analysis

Validation techniques - explained with simple examples (Hold-out, cross-validation, LOOCV)

Paired vs unpaired t-test

Bootstrap confidence intervals - explained

Nonlinear regression - comparing models with F test and AIC | parameter correlation

Newton's method - explained | Newton-Raphson method | find roots and minimum value

Nonlinear regression - how to fit a dose-response curve in R

k-means clustering - explained

Two-way ANOVA by hand

Two-way ANOVA - the basics | interaction | two-way vs one-way

Stochastic gradient descent (SGD) vs mini-batch GD | iterations vs epochs - Explained

Autoencoders - simply explained

Convolutional Neural Network (CNN) – explained simply

The probability of making a type 1 error

How do AI and neural networks work?

Deep learning - explained simply | Early Stopping & Dropout

PCA vs PCoA (Multidimensional scaling) - explained

Euclidean distance and the Mahalanobis distance (and the error ellipse)

Standard deviation | how to calculate the SD and variance

Mahalanobis distance for classification | Machine Learning

Zero-inflated Poisson (ZIP) regression

Logistic regression : how to use it as a classifier

How to calculate the likelihood ratio

Understanding exponential decay and half-life with examples

The weighted mean - explained

The repeated-measures ANOVA | explained with a simple example

Range, interquartile range (IQR) and box plots

Population vs Sample

PCA : the basics - explained super simple

The one-sample t-test and p-values

How to check normal distribution | The normality assumption

Why we divide by n-1 when calculating the sample variance – the proof | unbiased estimator

Understanding derivatives - the basics