input: Write a function to load a CSV file and split the data into train and test sets for linear regression.
  assert len(load_and_split_data('path_to_csv')[0]) > 0
  assert len(load_and_split_data('path_to_csv')[1]) > 0
  assert len(load_and_split_data('path_to_csv')[2]) > 0
  assert len(load_and_split_data('path_to_csv')[3]) > 0
output: def load_and_split_data(csv_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
  
      # Load the CSV file
      df = pd.read_csv(csv_path)
  
      # Assuming the last column is the target variable
      X = df.iloc[:, :-1].values
      y = df.iloc[:, -1].values
  
      # Split the data into train and test sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      return X_train, X_test, y_train, y_test