run a logistic regression in r

Logistic regression is a popular statistical model used to analyze data that has binary outcomes (i.e., two possible values such as 0/1, yes/no). It is commonly used in various fields such as healthcare, marketing, and finance.

Here is an example of how to run a logistic regression in R using the built-in dataset called mtcars:

# Load the dataset
data(mtcars)

# Fit the logistic regression model
model <- glm(vs ~ mpg + hp + wt, data = mtcars, family = binomial)

# Summarize the model
summary(model)
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In this example, we fit a logistic regression model to predict the vs variable (engine type, V-shaped or straight) using the mpg (miles per gallon), hp (horsepower), and wt (weight) variables from the mtcars dataset. We specify the family argument as binomial since logistic regression is a type of binomial regression.

The summary() function can be used to obtain a summary of the model parameters, including the coefficients and their standard errors, the deviance of the model, and goodness-of-fit measures such as the AIC and BIC.