Answer Posted / Nalini Johari

To build and evaluate a linear regression model in R, follow these steps: n1. Load your data into R. n2. Check for missing values and remove them if necessary. n3. Preprocess the data as needed (e.g., convert factors to numeric). n4. Split the data into training and testing sets. n5. Fit a linear regression model using the `lm()` function: `linear_model <- lm(dependent_variable ~ independent_variables, data = training_data)`. n6. Evaluate the performance of the model on the test set using appropriate metrics (e.g., R-squared, mean squared error).

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