Introduction Linear versus logistic regression Univariate versus multivariate logistic regression Data Binary logistic regression in R Univariate binary logistic regression Quantitative independent variable Qualitative independent variable Multivariate binary logistic regression Interaction Model selection Quality of a model Validity of the predictions Accuracy Sensitivity and specificity AUC and ROC curve Reporting results {gtsummary} package {finalfit} package Conditions of application Conclusion Introduction Regression is a common tool in statistics to test and quantify relationships betwee ..read more

Introduction How likely is it? For our team For teams of different sizes Conclusion Introduction Last week, I joined a team to work on a collaborative project. The team was already established for a few months, with several scientists working together on the project. For simplicity, they used to sign documents, mention colleagues in emails, etc. with their initials (the first letter of their first name followed by the first letter of their last name ..read more

Introduction Data {dplyr} package Filter observations The pipe operator Extract observations Based on their positions Based on their values Sample observations Sort observations Select variables Rename variables Create or modify variables Summarize observations Identify distinct values Connected operations Group by Number of observations Number of distinct values First, last or nth value If else Case when Conclusion and other resources Introduction In a previous post, we showed how to manipulate data in R ..read more

I am happy to announce that our paper has been accepted for publication in Nutrients (ISSN 2072-6643) (Houben et al. 2023). This study investigates the impact of a food rebalancing program associated with plant-derived food supplements on the biometric, behavioral, and biological parameters of obese subjects. Read more here. Thanks to all co-authors for the great work, and the Nutrients Editorial Office for their guidance throughout this process. We are also thankful to the two anonymous reviewers for their input that has greatly helped shape the paper ..read more

Introduction Data With ties Without ties Correlation coefficients by hand Pearson With and without ties Spearman With ties Without ties Kendall Without ties With ties Verification in R Conclusion Introduction In statistics, a correlation is used to evaluate the relationship between two variables. In a previous post, we showed how to compute a correlation and perform a correlation test in R ..read more

Introduction Scraping Yahoo search engine results with R Conclusion Note: This is a guest post by Manthan Koolwal, founder of Scrapingdog. Introduction Web scraping is the process of extracting data from websites. It is usually done in an automated manner to obtain a large amounts of data through various websites, without the need to gather data by hand. In a previous post, we introduced this method and illustrated it with a Wikipedia page ..read more

Introduction The two-way ANOVA (analysis of variance) is a statistical method that allows to evaluate the simultaneous effect of two categorical variables on a quantitative continuous variable. The two-way ANOVA is an extension of the one-way ANOVA since it allows to evaluate the effects on a numerical response of two categorical variables instead of one. The advantage of a two-way ANOVA over a one-way ANOVA is that we test the relationship between two variables, while taking into account the effect of a third variable ..read more

I am happy to announce that a paper I contributed to has been accepted for publication in Open Forum Infectious Diseases (Truong et al. 2023). This study investigates bacterial meningitis among children aged under five years in Southern Vietnam for the last 10 years. Read more here. I hope this paper will, to some extent, be helpful for your research. As always, if you have any question related to the topic covered in this paper, please add it as a comment so other readers can benefit from the discussion ..read more

Introduction What types of jobs are available? Statistician Data scientist, data/business analyst, data engineer or machine learning engineer Actuary or actuarial analyst Financial (risk) analyst, investment analyst, financial trader, financial manager or quantitative analyst Business intelligence analyst Operational researcher or quality control analyst Market or survey researcher Economist or econometrician (Freelance) consultant Teacher PhD student Conclusion This post has been written in collaboration with Daniel Williams ..read more

Introduction 1. Unmatched parentheses, curly braces, square brackets or quotes 2. Using a function that is not installed or loaded 3. Typos in function, variable, dataset, object or package names 4. Missing, incorrect or misspelled arguments in functions 5. Wrong, inappropriate or inconsistent data types 6. Forgetting the + sign in ggplot2 7. Misunderstanding between = and == 8. Undefined columns selected 9. Problem when importing or using the wrong data file 10 ..read more