text: # Simple Linear Regression
  
  # Importing the libraries
  import numpy
  import matplotlib.pyplot as plot
  import pandas
  
  # Importing the dataset
  dataset = pandas.read_csv('dataset.csv')
  X = dataset.iloc[:, 1:-1].values # Took all the rows(:) of all the cols except last (:-1)
  y = dataset.iloc[:, 1].values # Dependent variables, took all rows(:) of last col(3)
  
  # Splitting the dataset into Training set and Test set
  from sklearn.model_selection import train_test_split 
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
  
  # Feature Scaling
  ## (put all values on the same scale)
  
  from sklearn.preprocessing import StandardScaler
  sc_X = StandardScaler()
  X_train = sc_X.fit_transform(X_train)
  X_test = sc_X.transform(X_test)
  
  
  # # Fitting Simple Linear Regression to the Training set
  # from sklearn.linear_model import LinearRegression
  # regressor = LinearRegression()
  # regressor.fit(X_train, y_train)
  
  # # Predicting Test set
  # y_pred = regressor.predict(X_test)
  
  print(regressor.coef_)
  print(regressor.intercept_)
  
  import statsmodels.api as sm
  ols = sm.OLS(y_train, X_train)
  ols_result = ols.fit()
  ols_result.summary()
  