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library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.3     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.3     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(ggthemes)
library(ggrepel)
library(broom)
library(lindia)
datas <- read.csv("C:\\Users\\karth\\Downloads\\Child Growth and Malnutrition.csv")
view(datas)

Response Variable - JME..Y.N. Explanatory Variables - Wasting, Overweight, Stunting, Underweight

datas$Wasting = as.numeric(datas$Wasting)
## Warning: NAs introduced by coercion
datas$Overweight = as.numeric(datas$Overweight)
## Warning: NAs introduced by coercion
datas1 <- datas
datas1$JME..Y.N. <- ifelse(datas1$JME..Y.N. == "Selected for JME",1,0)
view(datas1)
model <- glm(JME..Y.N. ~ Wasting + Overweight + Stunting + Underweight, data = datas1,
             family = binomial(link = 'logit'))

model$coefficients
##  (Intercept)      Wasting   Overweight     Stunting  Underweight 
## -3.451969858 -0.017774437 -0.003021626 -0.003190176  0.002083612

This means that for every unit change in each of the variables, the odds of being selected for JME goes up in different amounts:

Wasting - \(e^{-0.017774437} = 0.9824\)

Overweight - \(e^{-0.0030321626} = 0.997\)

Stunting - \(e^{-0.003190176} = 0.997\)

Underweight - \(e^{0.002083612} = 1.0021\)

plot(model)

Logistic Regression makes no assumptions about the distribution of the explanatory variables. In such a case, we do not need to transform any of the explanatory variables for the regression model purposes.