# Dimas Rinarso Putro | [email protected] # # No.3d # ############################################## import argparse,csv,sys,os import numpy as np import pandas as pd from matplotlib import pyplot as plt from pandas.tools.plotting import scatter_matrix as scatter from pandas.io.stata import read_stata as rd_stata import statsmodels.api as sm from statsmodels.sandbox.regression.predstd import wls_prediction_std #Read data filename = 'union.dta' df = rd_stata(filename) #for creating model df_sliced2 = df[df['year'] >=70] df_sliced = df_sliced2[df['year'] <=78] #for creating model df_sample2 = df[df['year'] >=80] df_sample = df_sample2[df['year'] <=88] #===========get the data into parameters for model fit x= df_sliced[['year','age','grade','south','black','smsa']] y = df_sliced.union X = sm.add_constant(x) #===========Least square regression model_linear = sm.OLS(y, X)
# Dimas Rinarso Putro | [email protected] # # No.3d # ############################################## import argparse, csv, sys, os import numpy as np import pandas as pd from matplotlib import pyplot as plt from pandas.tools.plotting import scatter_matrix as scatter from pandas.io.stata import read_stata as rd_stata import statsmodels.api as sm from statsmodels.sandbox.regression.predstd import wls_prediction_std #Read data filename = 'union.dta' df = rd_stata(filename) #for creating model df_sliced2 = df[df['year'] >= 70] df_sliced = df_sliced2[df['year'] <= 78] #for creating model df_sample2 = df[df['year'] >= 80] df_sample = df_sample2[df['year'] <= 88] #===========get the data into parameters for model fit x = df_sliced[['year', 'age', 'grade', 'south', 'black', 'smsa']] y = df_sliced.union X = sm.add_constant(x) #===========Least square regression #============print the summary for each column
