Канал: TileStats

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

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

Multinomial logistic regression | softmax regression | explained

Multinomial logistic regression | softmax regression | explained

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

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

Expected value vs mean

Expected value vs mean

Bootstrap confidence intervals - explained

Bootstrap confidence intervals - explained

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

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

Bayesian statistics - the basics

Bayesian statistics - the basics

How to check normal distribution | The normality assumption

How to check normal distribution | The normality assumption

PERMANOVA and permutation tests - explained

PERMANOVA and permutation tests - explained

Statistical power - Parametric vs Nonparametric test

Statistical power - Parametric vs Nonparametric test

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

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

The Mantel-Haenszel method - clearly explained | deal with confounding

The Mantel-Haenszel method - clearly explained | deal with confounding

Understanding the odds ratio (OR) and the rare disease assumption | OR = RR?

Understanding the odds ratio (OR) and the rare disease assumption | OR = RR?

Relative risk - how to calculate and interpret | 95% CI

Relative risk - how to calculate and interpret | 95% CI

Odds vs Probability - explained

Odds vs Probability - explained

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

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

Receptor ligand kinetics | mathematical modeling

Receptor ligand kinetics | mathematical modeling

How to build a system of differential equations (ODEs)

How to build a system of differential equations (ODEs)

How to select a multivariate analysis or machine learning method

How to select a multivariate analysis or machine learning method

How to solve ordinary differential equations (ODEs) in R (deSolve)

How to solve ordinary differential equations (ODEs) in R (deSolve)

Euler's method | Numerial methods

Euler's method | Numerial methods

Understanding ordinary differential equations (ODE) - super simple example

Understanding ordinary differential equations (ODE) - super simple example

The Cox proportional hazards model explained

The Cox proportional hazards model explained

Comparing Kaplan-Meier curves - the Log-rank test

Comparing Kaplan-Meier curves - the Log-rank test

Kaplan Meier curve – explained

Kaplan Meier curve – explained

Nonlinear mixed effects models (NLME) - explained

Nonlinear mixed effects models (NLME) - explained

Nonlinear regression - how to fit a logistic growth model to data

Nonlinear regression - how to fit a logistic growth model to data

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

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

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

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

The Gauss Newton Method - explained with a simple example

The Gauss Newton Method - explained with a simple example

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

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

Neural networks with continuous output | ANN vs Regression

Neural networks with continuous output | ANN vs Regression

Gradient descent - with a simple example

Gradient descent - with a simple example

Nonlinear regression - the basics

Nonlinear regression - the basics

Image classification with machine learning - explained | CNN ANN Logistic regression Decision trees

Image classification with machine learning - explained | CNN ANN Logistic regression Decision trees

Understanding exponential decay and half-life with examples

Understanding exponential decay and half-life with examples

Doubling time formulas with examples

Doubling time formulas with examples

Understanding exponential growth | discrete vs continuous growth

Understanding exponential growth | discrete vs continuous growth

Why do we use Euler's number e?

Why do we use Euler's number e?

Numerical differentiation - simply explained

Numerical differentiation - simply explained

Artificial neural networks (ANN) - explained super simple

Artificial neural networks (ANN) - explained super simple

Second derivative and partial derivative - the basics

Second derivative and partial derivative - the basics

Understanding derivatives - the basics

Understanding derivatives - the basics

Understanding logarithms

Understanding logarithms

Understanding the equation of a straight line | find equation | find line

Understanding the equation of a straight line | find equation | find line

Assumptions in Linear Regression - explained | residual analysis

Assumptions in Linear Regression - explained | residual analysis

Gaussian naive Bayes - explained with a simple example

Gaussian naive Bayes - explained with a simple example

Regression vs ANOVA and t-test

Regression vs ANOVA and t-test

The weighted mean - explained

The weighted mean - explained

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

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

Lasso regression - explained

Lasso regression - explained

Forward and backward selection and best subset selection

Forward and backward selection and best subset selection

Model selection with AIC and AICc

Model selection with AIC and AICc

MLE vs OLS | Maximum likelihood vs least squares in linear regression

MLE vs OLS | Maximum likelihood vs least squares in linear regression

Probability vs Likelihood - Explained

Probability vs Likelihood - Explained

Linear mixed effects models - random slopes and interactions | R and SPSS

Linear mixed effects models - random slopes and interactions | R and SPSS

Linear mixed effects models - the basics

Linear mixed effects models - the basics

Cellular automata tutorial - how to implement a CA in R

Cellular automata tutorial - how to implement a CA in R

Cellular automata tutorial - applications (epidemic and movements)

Cellular automata tutorial - applications (epidemic and movements)

Cellular automata tutorial - the basics

Cellular automata tutorial - the basics

Gene set analysis - GSEA and Fisher's exact test

Gene set analysis - GSEA and Fisher's exact test

Fisher's test and how to calculate the exact p-value

Fisher's test and how to calculate the exact p-value

Permutations Combinations and the Hypergeometric distribution

Permutations Combinations and the Hypergeometric distribution

Canonical correlation analysis - explained

Canonical correlation analysis - explained

Partial least squares regression (PLSR) - explained

Partial least squares regression (PLSR) - explained

Principal component regression (PCR) - explained

Principal component regression (PCR) - explained

PLS-DA

PLS-DA

Random forest classification - simply explained

Random forest classification - simply explained

Decision trees for classification - explained

Decision trees for classification - explained

k-means clustering - explained

k-means clustering - explained

Understanding cluster heat maps

Understanding cluster heat maps

Hierarchical clustering - explained

Hierarchical clustering - explained

p-values - a deeper understanding | alpha | t-statistics

p-values - a deeper understanding | alpha | t-statistics

FDR - Storey's method and q values

FDR - Storey's method and q values

FDR - Benjamini-Hochberg explained

FDR - Benjamini-Hochberg explained

False discovery rate (FDR) - explained | vs FWER

False discovery rate (FDR) - explained | vs FWER

Mahalanobis distance for classification | Machine Learning

Mahalanobis distance for classification | Machine Learning

K nearest neighbors (KNN) - explained | validation

K nearest neighbors (KNN) - explained | validation

How to calculate the likelihood ratio

How to calculate the likelihood ratio

How to select a post hoc test

How to select a post hoc test

Post hoc test | Tukey's test and Dunnett's test – Explained

Post hoc test | Tukey's test and Dunnett's test – Explained

Post hoc test | Holm - Explained

Post hoc test | Holm - Explained

Post hoc test | Bonferroni - explained

Post hoc test | Bonferroni - explained

Post hoc test | Fisher's LSD – explained

Post hoc test | Fisher's LSD – explained

Familywise error rate (FWER) - explained

Familywise error rate (FWER) - explained

Two-way ANOVA by hand

Two-way ANOVA by hand

Two-way ANOVA - Within-Within, Between-Between, Mixed, type I, II, III models

Two-way ANOVA - Within-Within, Between-Between, Mixed, type I, II, III models

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

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

How to choose an appropriate statistical test

How to choose an appropriate statistical test

Multiple linear regression - explained with two simple examples

Multiple linear regression - explained with two simple examples

Linear regression | the R-squared value

Linear regression | the R-squared value

Linear regression | hypothesis testing

Linear regression | hypothesis testing

Least squares - explained with a simple numeric example

Least squares - explained with a simple numeric example

Linear regression | the basics - for beginners

Linear regression | the basics - for beginners

Spearman's rank correlation | Pearson VS Spearman

Spearman's rank correlation | Pearson VS Spearman

Correlation | hypothesis testing | assumptions

Correlation | hypothesis testing | assumptions

Correlation - the basics | Pearson correlation

Correlation - the basics | Pearson correlation

Statistical power and sample size calculations

Statistical power and sample size calculations

The probability of making a type 2 error | explained with a simple example (part 2/2)

The probability of making a type 2 error | explained with a simple example (part 2/2)

The probability of making a type 2 error | explained with a simple example (part 1/2)

The probability of making a type 2 error | explained with a simple example (part 1/2)

The probability of making a type 1 error

The probability of making a type 1 error

The basics of type 1 and 2 errors | explained with a simple example

The basics of type 1 and 2 errors | explained with a simple example

The Wilcoxon signed-rank test & the sign test

The Wilcoxon signed-rank test & the sign test

The Mann Whitney U test (Wilcoxon Mann Whitney test) part 2/2 | exact p-value

The Mann Whitney U test (Wilcoxon Mann Whitney test) part 2/2 | exact p-value

The Mann Whitney U test (Wilcoxon Mann Whitney test) part 1/2

The Mann Whitney U test (Wilcoxon Mann Whitney test) part 1/2

The McNemar test

The McNemar test

The Chi-square test of independence VS homogeneity and goodness of fit

The Chi-square test of independence VS homogeneity and goodness of fit

The two proportion z-test and the Chi-square test of homogeneity

The two proportion z-test and the Chi-square test of homogeneity

The Chi-square goodness of fit test | and the difference to the one-proportion Z-test

The Chi-square goodness of fit test | and the difference to the one-proportion Z-test

One-proportion Z-test and the corresponding confidence interval

One-proportion Z-test and the corresponding confidence interval

The repeated-measures ANOVA | explained with a simple example

The repeated-measures ANOVA | explained with a simple example

Paired vs unpaired t-test

Paired vs unpaired t-test

The paired t-test | explained with a simple example

The paired t-test | explained with a simple example

One-way ANOVA: the calculations - step-by-step

One-way ANOVA: the calculations - step-by-step

One-way ANOVA: the basics

One-way ANOVA: the basics

The unpaired t-test | Independent samples t-test

The unpaired t-test | Independent samples t-test

The basic steps of hypothesis testing

The basic steps of hypothesis testing

The degrees of freedom - explained with a simple example

The degrees of freedom - explained with a simple example

t-test VS confidence intervals

t-test VS confidence intervals

The one-sample t-test and p-values

The one-sample t-test and p-values

The t-distribution - why we need it | explained with confidence intervals

The t-distribution - why we need it | explained with confidence intervals

Confidence intervals - simply explained

Confidence intervals - simply explained

The standard error of the mean (SEM)| how to calculate and interpret | SE vs SD

The standard error of the mean (SEM)| how to calculate and interpret | SE vs SD

The central limit theorem | Explained with a simple example

The central limit theorem | Explained with a simple example

The normal distribution | how to interpret and use it

The normal distribution | how to interpret and use it

Why do we divide by n-1 and not n? | shown with a simple example | variance and sd

Why do we divide by n-1 and not n? | shown with a simple example | variance and sd

Standard deviation | how to calculate the SD and variance

Standard deviation | how to calculate the SD and variance

Range, interquartile range (IQR) and box plots

Range, interquartile range (IQR) and box plots

Variables and scales in statistics

Variables and scales in statistics

The geometric mean

The geometric mean

Mean, median and mode

Mean, median and mode

Population vs Sample

Population vs Sample

Zero-inflated Poisson (ZIP) regression

Zero-inflated Poisson (ZIP) regression

Logistic regression | Likelihood ratio test and AIC

Logistic regression | Likelihood ratio test and AIC

Logistic regression | Likelihood and deviance

Logistic regression | Likelihood and deviance

Quasi-Poisson and negative binomial regression models

Quasi-Poisson and negative binomial regression models

Poisson regression - Likelihood ratio test (LRT) and AIC

Poisson regression - Likelihood ratio test (LRT) and AIC

Poisson regression - How to calculate likelihood and deviance

Poisson regression - How to calculate likelihood and deviance

Poisson regression - categorical variable

Poisson regression - categorical variable

Poisson regression - rates and the offset

Poisson regression - rates and the offset

Poisson regression - clearly explained

Poisson regression - clearly explained

The Poisson distribution vs the normal distribution

The Poisson distribution vs the normal distribution

Logistic regression : how to use it as a classifier

Logistic regression : how to use it as a classifier

Logistic regression : the basics - simply explained

Logistic regression : the basics - simply explained

Linear discriminant analysis (LDA) - how to use it as a classifier

Linear discriminant analysis (LDA) - how to use it as a classifier

Hotelling's T-square - explained with a simple example

Hotelling's T-square - explained with a simple example

MANOVA - explained with a simple example

MANOVA - explained with a simple example

Linear discriminant analysis (LDA) - simply explained

Linear discriminant analysis (LDA) - simply explained

PCA : how to interpret the weights/loadings and Varimax rotation

PCA : how to interpret the weights/loadings and Varimax rotation

PCA : standardization and how to extract components

PCA : standardization and how to extract components

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

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

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

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

ROC curve - explained

ROC curve - explained

The positive and negative predictive values (PPV and NPV) - simply explained

The positive and negative predictive values (PPV and NPV) - simply explained

Sensitivity and specificity - clearly explained

Sensitivity and specificity - clearly explained

PCA : the basics - simply explained

PCA : the basics - simply explained

Covariance and the correlation matrix - with simple examples

Covariance and the correlation matrix - with simple examples

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

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

Eigenvectors and eigenvalues - the math step-by-step

Eigenvectors and eigenvalues - the math step-by-step

Eigenvectors and eigenvalues - simply explained

Eigenvectors and eigenvalues - simply explained

Matrices and matrix operations for beginners 2/2

Matrices and matrix operations for beginners 2/2

Matrices and matrix operations for beginners 1/2

Matrices and matrix operations for beginners 1/2