Using Linear Regression

import transportation_tutorials as tt
import pandas as pd
import numpy as np
import statsmodels.api as sm

Question

Construct an ordinary least squares linear regression model to predict the given value of time for each individual in the Jupiter study area data as a function of: - age, - gender, - full-time employment status, and - household income.

Evaluate this model to answer the questions:

1. What are the coefficients on this model?
2. Do any of these factors appear to actually be not relevant in determining an individual’s value of time? (Hint: Gender)
3. If other variables from the household and person datasets could also be included in the OLS model specificiation, are there any that are also significant? (Hint: Yes, there is at least one other relevant factor in this data.)

Data

To answer the questions, use the following data files:

per = pd.read_csv(tt.data('SERPM8-BASE2015-PERSONS'))
hh = pd.read_csv(tt.data('SERPM8-BASE2015-HOUSEHOLDS'))

per.head()

	hh_id	person_id	person_num	age	gender	type	value_of_time	activity_pattern	imf_choice	inmf_choice	fp_choice	reimb_pct	wrkr_type
0	1690841	4502948	1	46	m	Full-time worker	5.072472	M	1	1	-1	0.0	0
1	1690841	4502949	2	47	f	Part-time worker	5.072472	M	2	37	-1	0.0	0
2	1690841	4502950	3	11	f	Student of non-driving age	3.381665	M	3	1	-1	0.0	0
3	1690841	4502951	4	8	m	Student of non-driving age	3.381665	M	3	1	-1	0.0	0
4	1690961	4503286	1	52	m	Part-time worker	2.447870	M	1	2	-1	0.0	0

hh.head()

	Unnamed: 0	hh_id	home_mgra	income	autos	transponder	cdap_pattern	jtf_choice	autotech	tncmemb
0	426629	1690841	7736	512000	2	1	MMMM0	0	0	0
1	426630	1690961	7736	27500	1	0	MNMM0	0	0	0
2	426631	1690866	7736	150000	2	0	HMM0	0	0	0
3	426632	1690895	7736	104000	2	1	MMMM0	0	0	0
4	426633	1690933	7736	95000	2	1	MNM0	0	0	0