input: Write a python function to remove outliers from a dataset where outliers are defined as values that are more than 3 standard deviations below the mean. Use the provided dataset and calculate the mean and standard deviation of the third column.
  assert remove_outliers([[1, 15, 10], [2, 17, 20], [3, 19, 30], [4, 20, 40], [5, 22, 50], [6, 24, 60], [7, 26, -100], [8, 28, 80], [9, 30, 90]]) == [[1, 15, 10], [2, 17, 20], [3, 19, 30], [4, 20, 40], [5, 22, 50], [6, 24, 60], [8, 28, 80], [9, 30, 90]]
  assert remove_outliers([[1, 15, 5], [2, 17, 15], [3, 19, 25], [4, 20, 35], [5, 22, 45], [6, 24, 55], [7, 26, -200], [8, 28, 75], [9, 30, 85]]) == [[1, 15, 5], [2, 17, 15], [3, 19, 25], [4, 20, 35], [5, 22, 45], [6, 24, 55], [8, 28, 75], [9, 30, 85]]
  assert remove_outliers([[1, 15, 100], [2, 17, 200], [3, 19, 300], [4, 20, 400], [5, 22, 500], [6, 24, 600], [7, 26, 700], [8, 28, 800], [9, 30, 900]]) == [[1, 15, 100], [2, 17, 200], [3, 19, 300], [4, 20, 400], [5, 22, 500], [6, 24, 600], [7, 26, 700], [8, 28, 800], [9, 30, 900]]
output: def remove_outliers(data):
      import statistics
      
      third_column = [row[2] for row in data]
      mean = statistics.mean(third_column)
      stdev = statistics.stdev(third_column)
      
      threshold = mean - 3 * stdev
      filtered_data = [row for row in data if row[2] >= threshold]
      
      return filtered_data