Instruction: Explain how to implement a logistic regression model in R and interpret its summary output.
Context: The candidate should describe the steps to carry out logistic regression in R using a dataset (e.g., mtcars or iris). They should discuss data preparation, model fitting with the 'glm' function, and interpretation of the summary results, focusing on coefficients and their significance. The discussion should also cover how to evaluate the model's performance using appropriate metrics. This question assesses the candidate's grasp of machine learning concepts, proficiency in using R for statistical modeling, and ability to translate statistical output into business insights.
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Let's consider we're working with the mtcars dataset for this example. Our objective is to predict whether a car has an automatic or manual transmission (am variable), using the car's features, such as miles per gallon (mpg), and horsepower (hp). It's a binary classification problem perfectly suited for logistic regression.
Step 1: Data Preparation First, we ensure our data is clean and formatted correctly for logistic regression. This involves checking for missing values, ensuring categorical variables are correctly encoded, and potentially normalizing continuous variables if necessary. For simplicity, assume our data is ready for modeling....