Oneway ANOVA Example
Published:
This post provides a simple example of a oneway ANOVA using the ToothGrowth dataset in R. More detailed information about the oneway ANOVA model and how it works can be found here.
Posts by Tags
analysis of variance
An Introduction to Analysis of Variance (ANOVA)
Published:
This post gives a brief introduction to the basics of analysis of variance and how it works. An overview of the oneway analysis of variance model is provided along with additional details regarding sums of squares. A simple example of analysis of variance can be found here.
anomaly detection
Common Multivariate Control Charts
Published:
This post introduces basic overviews and examples of two of the most common multivariate statistical process monitoring (MSPM) methods: the $T^2$ and MEWMA control charts.
Multivariate Fault Detection with MSPM
Published:
This post introduces multivariate fault detection with multivariate statistical process monitoring (MSPM) and discusses its benefits over univariate methods.
Fault Detection with Statistical Process Control
Published:
This post gives a basic introduction to fault detection with statistical process control (SPC), also referred to as statistical process monitoring (SPM).
categorical variables
Oneway ANOVA Example
Published:
This post provides a simple example of a oneway ANOVA using the ToothGrowth dataset in R. More detailed information about the oneway ANOVA model and how it works can be found here.
An Introduction to Analysis of Variance (ANOVA)
Published:
This post gives a brief introduction to the basics of analysis of variance and how it works. An overview of the oneway analysis of variance model is provided along with additional details regarding sums of squares. A simple example of analysis of variance can be found here.
classification
Analysis Walkthrough: Supervised Classification with Bank Churn Data
Published:
This post provides a walkthrough demonstrating how to use the sklearn package in Python to tune and evaluate multiple supervised classification methods, such as logistic regression and extreme gradient boosting (XGBoost) to predict whether bank customers will close their account. The dataset comes from a past Kaggle competition and contains several variables, including credit score, gender, and age.
fault detection
Common Multivariate Control Charts
Published:
This post introduces basic overviews and examples of two of the most common multivariate statistical process monitoring (MSPM) methods: the $T^2$ and MEWMA control charts.
Multivariate Fault Detection with MSPM
Published:
This post introduces multivariate fault detection with multivariate statistical process monitoring (MSPM) and discusses its benefits over univariate methods.
Fault Detection with Statistical Process Control
Published:
This post gives a basic introduction to fault detection with statistical process control (SPC), also referred to as statistical process monitoring (SPM).
hypothesis testing
Hypothesis Testing: Error Types and Power
Published:
This post discusses type I and type II errors, along with power. Basic background knowledge regarding hypothesis testing and $p$-values is assumed in this post.
Basics of Hypothesis Testing
Published:
This post explains the basics of hypothesis testing and provides a simple hypothetical pharmaceutical example of testing whether a new drug is better than an existing drug.
machine learning
Neural Networks with torch: Revisiting the Abalone Data
Published:
This post demonstrates building and fitting a neural network using the torch package in R. In this post, I revisit the abalone Kaggle competition, which is a supervised regression problem described and analyzed in a previous blog post using tidymodels.
Analysis Walkthrough: Supervised Classification with Bank Churn Data
Published:
This post provides a walkthrough demonstrating how to use the sklearn package in Python to tune and evaluate multiple supervised classification methods, such as logistic regression and extreme gradient boosting (XGBoost) to predict whether bank customers will close their account. The dataset comes from a past Kaggle competition and contains several variables, including credit score, gender, and age.
Analysis Walkthrough: Supervised Regression with Abalone Data
Published:
This post provides a complete walkthrough of analyzing Abalone data from Kaggle and applying supervised machine learning (ML) regression methods in R using the tidymodels package. The best model is selected from a suite of candidate models, including random forests and extreme gradient boosting (XGBoost).
multivariate statistics
Common Multivariate Control Charts
Published:
This post introduces basic overviews and examples of two of the most common multivariate statistical process monitoring (MSPM) methods: the $T^2$ and MEWMA control charts.
regression
Neural Networks with torch: Revisiting the Abalone Data
Published:
This post demonstrates building and fitting a neural network using the torch package in R. In this post, I revisit the abalone Kaggle competition, which is a supervised regression problem described and analyzed in a previous blog post using tidymodels.
Analysis Walkthrough: Supervised Regression with Abalone Data
Published:
This post provides a complete walkthrough of analyzing Abalone data from Kaggle and applying supervised machine learning (ML) regression methods in R using the tidymodels package. The best model is selected from a suite of candidate models, including random forests and extreme gradient boosting (XGBoost).
shiny
Central Limit Theorem and Normal Approximations
Published:
This post discusses the classical Central Limit Theorem and demonstrates its usage through the Normal approximation of the Binomial distribution with a Shiny app.
statistical process control
Common Multivariate Control Charts
Published:
This post introduces basic overviews and examples of two of the most common multivariate statistical process monitoring (MSPM) methods: the $T^2$ and MEWMA control charts.
Multivariate Fault Detection with MSPM
Published:
This post introduces multivariate fault detection with multivariate statistical process monitoring (MSPM) and discusses its benefits over univariate methods.
Fault Detection with Statistical Process Control
Published:
This post gives a basic introduction to fault detection with statistical process control (SPC), also referred to as statistical process monitoring (SPM).
statistics
Neural Networks with torch: Revisiting the Abalone Data
Published:
This post demonstrates building and fitting a neural network using the torch package in R. In this post, I revisit the abalone Kaggle competition, which is a supervised regression problem described and analyzed in a previous blog post using tidymodels.
Analysis Walkthrough: Supervised Classification with Bank Churn Data
Published:
This post provides a walkthrough demonstrating how to use the sklearn package in Python to tune and evaluate multiple supervised classification methods, such as logistic regression and extreme gradient boosting (XGBoost) to predict whether bank customers will close their account. The dataset comes from a past Kaggle competition and contains several variables, including credit score, gender, and age.
Central Limit Theorem and Normal Approximations
Published:
This post discusses the classical Central Limit Theorem and demonstrates its usage through the Normal approximation of the Binomial distribution with a Shiny app.
Hypothesis Testing: Error Types and Power
Published:
This post discusses type I and type II errors, along with power. Basic background knowledge regarding hypothesis testing and $p$-values is assumed in this post.
Basics of Hypothesis Testing
Published:
This post explains the basics of hypothesis testing and provides a simple hypothetical pharmaceutical example of testing whether a new drug is better than an existing drug.
Analysis Walkthrough: Supervised Regression with Abalone Data
Published:
This post provides a complete walkthrough of analyzing Abalone data from Kaggle and applying supervised machine learning (ML) regression methods in R using the tidymodels package. The best model is selected from a suite of candidate models, including random forests and extreme gradient boosting (XGBoost).
Multivariate Fault Detection with MSPM
Published:
This post introduces multivariate fault detection with multivariate statistical process monitoring (MSPM) and discusses its benefits over univariate methods.
Fault Detection with Statistical Process Control
Published:
This post gives a basic introduction to fault detection with statistical process control (SPC), also referred to as statistical process monitoring (SPM).
Oneway ANOVA Example
Published:
This post provides a simple example of a oneway ANOVA using the ToothGrowth dataset in R. More detailed information about the oneway ANOVA model and how it works can be found here.
An Introduction to Analysis of Variance (ANOVA)
Published:
This post gives a brief introduction to the basics of analysis of variance and how it works. An overview of the oneway analysis of variance model is provided along with additional details regarding sums of squares. A simple example of analysis of variance can be found here.