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Module XI Geostatistical Data Analysis
This RMarkdown document provides a clear, step-by-step explanation of the concepts, uses the provided example data, includes example code chunks, and clearly outlines the homework exercise. Remember to render the RMarkdown document to see the results, and feel free to ask if you need any further modifications.
Module 7 Spatial Autocorrelation
Module 7 for "Spatial Analysis and Disease Mapping Course"
Primer for Spatial Analysis in R
This course on Spatial Analysis with R provides participants with a comprehensive introduction to spatial analysis techniques using the R programming language. Participants will learn how to analyze different types of spatial data, including areal data, point patterns, and geostatistical datasets, and gain insights into spatial patterns, relationships, and variations. The course emphasizes reproducible research practices and covers topics such as data manipulation, visualization, spatial interpolation, clustering, regression, and modeling. By the end of the course, participants will have the skills and knowledge to conduct spatial analysis, document their workflows, and apply spatial analysis techniques to real-world problems in various fields.
Bayesian Survival Modelling of Sine-G Family of Distributions A Baseline Hazard Approach for Proportional Hazard Regression Models using R and STAN with Application to Right-Censored Survival Datasets2
Section 2 cover the Sine-Gompertz PH and Sine-Exponentiated-Exponential PH Regression Models
Bayesian Survival Modelling of Sine-G Family of Distributions: A Baseline Hazard Approach for Proportional Hazard Regression Models using R and STAN with Application to Right-Censored Survival Datasets
Section 1 covers Sine-Weibull-PH and Sine-Lomax-PH Regression Models
Spatial Analysis with R
This course on Spatial Analysis with R provides participants with a comprehensive introduction to spatial analysis techniques using the R programming language. Participants will learn how to analyze different types of spatial data, including areal data, point patterns, and geostatistical datasets, and gain insights into spatial patterns, relationships, and variations. The course emphasizes reproducible research practices and covers topics such as data manipulation, visualization, spatial interpolation, clustering, regression, and modeling. By the end of the course, participants will have the skills and knowledge to conduct spatial analysis, document their workflows, and apply spatial analysis techniques to real-world problems in various fields.
Researcher's Toolkit: A Comprehensive Tutorial on 40 Regression Models using R, Python, and Stata
This comprehensive tutorial paper serves as a valuable resource for researchers looking to enhance their analytical capabilities through regression modeling. By utilizing popular software tools such as R, Python, and Stata, the tutorial covers 40 different regression models, enabling researchers to effectively analyze dependent variables of varying scales. The paper provides step-by-step guidance on implementing these models, offering practical insights and code examples. By leveraging the power of R, Python, and Stata, researchers can unlock the full potential of their data and drive impactful research outcomes. This tutorial is an essential toolkit for researchers seeking to advance their research methodology and maximize the insights derived from their data.
A Primer on Bayesian Regression Models using Reproducible R, STATA, Stan, INLA, JAGS and BUGS Softwares
Bayesian Statistics Course
A Primer on Factor Analysis in Resear using Reproducible R Software
Factor Analysis in Research
A Primer on Probability Distributions using Reproducible R Software
This is one of the chapters of the hypothesis testing course
A Primer on Hypothesis Testing Using Reproducible R Software
This primer provides an overview of 25 different hypothesis testing methods, including parametric and non-parametric tests, using reproducible R software. The tests are categorized based on research questions, including analysis of effects, analysis of association, analysis of difference, and analysis of dependency.
A Primer on Regression Modelling using Reproducible R Software
Course: Analysis of Prediction
Module 7 Statistical Hypothesis Testing
This is one of the modules in the course of "Hypothesis Testing"
A Primer on Time Series Modelling using Reproducible R Software
This is a short tutorial note that we covered more than 10 time series models using reproducible R software
Module IV: Multiple Linear Regression Model using R,Python, STATA and SPSS
This is Module IV for the "Analysis of Prediction Course", School of Postgraduate Studies and Research (SPGSR), Amoud University
The Arctan-X Family of Distributions
Arctan-X Family of distributions and one of its special cases
The Secant-Weibull Distribution
Secant-G family of distributions
The Generalized Log-logistic Distribution
Different Hazard rate shapes for the Generalized Log-logistic (GLL) distribution