통계데이터분석 - 일반선형모델 - 로지스틱회귀모델 유의성검정 🔑 logistic regression | significance | residual deviance | 이탈도

[Statistical Data Analysis using R] The significance of a logistic regression model is tested based on the difference between the null deviance, which is the fit of the null model to the data, and the residual deviance, which is the fit of the proposed model to the data. (This difference is also called the -2 log-likelihood (LL).) A small value of the deviance indicates that the model explains the data well. If the difference between the null deviance and the residual deviance is non-zero, this indicates a difference in fit between the proposed model and the null model. Therefore, we can conclude that the current logistic regression model, with the added predictor variables, fits the data well and is statistically significant. This section includes descriptions of the following functions: glm(), summary(), and pchisq(). 📢 For instructions on installing R and RStudio, please refer to the "R Programming / R Basics - Installation" course (   • R 프로그래밍 / R 기초 - 설치 🔑 CRAN | RStudio | Rco...  ). 📚 The video lectures on the "Kwak Ki-young" channel are based on the following books: 💕 "R Basics and Applications" (Kwak Ki-young, Chungram Publishing) "Statistical Data Analysis using R" (Kwak Ki-young, Chungram Publishing) "Machine Learning and Text Mining using R" (Kwak Ki-young, Chungram Publishing) "Web Scraping and Data Analysis using R" (Kwak Ki-young, Chungram Publishing) "Statistical Data Analysis using SPSS" (Kwak Ki-young, Chungram Publishing) "Social Network Analysis" (Kwak Ki-young, Chungram Publishing) #RProgramming #DataAnalysis #Statistics #MachineLearning #DataAnalytics #DataScience