!pip install statsmodels
# Import modules for API calls
import requests
import io
import pandas as pd
import requests
import json
from datetime import datetime
# Import module for plotting
import seaborn as sns
# Import module for regressions
import statsmodels.api as sm
from statsmodels import regression
import statsmodels.formula.api as smf
from statsmodels.iolib.summary2 import summary_col
## JHU Vaccination Rates (Taken From: https://github.com/owid/covid-19-data/tree/master/public/data)
url = 'https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/owid-covid-data.csv'
download = requests.get(url).content
covid = pd.read_csv(io.StringIO(download.decode('utf-8')), parse_dates=['date'])
covid.tail()
Collecting statsmodels
Downloading statsmodels-0.13.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.8 MB)
|████████████████████████████████| 9.8 MB 20.6 MB/s
Requirement already satisfied: scipy>=1.3 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from statsmodels) (1.7.1)
Requirement already satisfied: pandas>=0.25 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from statsmodels) (1.2.5)
Collecting patsy>=0.5.2
Downloading patsy-0.5.2-py2.py3-none-any.whl (233 kB)
|████████████████████████████████| 233 kB 45.7 MB/s
Requirement already satisfied: numpy>=1.17 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from statsmodels) (1.19.5)
Requirement already satisfied: python-dateutil>=2.7.3 in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from pandas>=0.25->statsmodels) (2.8.2)
Requirement already satisfied: pytz>=2017.3 in /shared-libs/python3.7/py/lib/python3.7/site-packages (from pandas>=0.25->statsmodels) (2021.3)
Requirement already satisfied: six in /shared-libs/python3.7/py-core/lib/python3.7/site-packages (from patsy>=0.5.2->statsmodels) (1.16.0)
Installing collected packages: patsy, statsmodels
Successfully installed patsy-0.5.2 statsmodels-0.13.0
covid.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 121741 entries, 0 to 121740
Data columns (total 65 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 iso_code 121741 non-null object
1 continent 116199 non-null object
2 location 121741 non-null object
3 date 121741 non-null datetime64[ns]
4 total_cases 115518 non-null float64
5 new_cases 115514 non-null float64
6 new_cases_smoothed 114500 non-null float64
7 total_deaths 104708 non-null float64
8 new_deaths 104862 non-null float64
9 new_deaths_smoothed 114500 non-null float64
10 total_cases_per_million 114910 non-null float64
11 new_cases_per_million 114906 non-null float64
12 new_cases_smoothed_per_million 113897 non-null float64
13 total_deaths_per_million 104113 non-null float64
14 new_deaths_per_million 104267 non-null float64
15 new_deaths_smoothed_per_million 113897 non-null float64
16 reproduction_rate 97948 non-null float64
17 icu_patients 13707 non-null float64
18 icu_patients_per_million 13707 non-null float64
19 hosp_patients 15751 non-null float64
20 hosp_patients_per_million 15751 non-null float64
21 weekly_icu_admissions 1254 non-null float64
22 weekly_icu_admissions_per_million 1254 non-null float64
23 weekly_hosp_admissions 2155 non-null float64
24 weekly_hosp_admissions_per_million 2155 non-null float64
25 new_tests 52248 non-null float64
26 total_tests 52352 non-null float64
27 total_tests_per_thousand 52352 non-null float64
28 new_tests_per_thousand 52248 non-null float64
29 new_tests_smoothed 62816 non-null float64
30 new_tests_smoothed_per_thousand 62816 non-null float64
31 positive_rate 58959 non-null float64
32 tests_per_case 58319 non-null float64
33 tests_units 64746 non-null object
34 total_vaccinations 27984 non-null float64
35 people_vaccinated 26620 non-null float64
36 people_fully_vaccinated 23589 non-null float64
37 total_boosters 3028 non-null float64
38 new_vaccinations 23184 non-null float64
39 new_vaccinations_smoothed 49977 non-null float64
40 total_vaccinations_per_hundred 27984 non-null float64
41 people_vaccinated_per_hundred 26620 non-null float64
42 people_fully_vaccinated_per_hundred 23589 non-null float64
43 total_boosters_per_hundred 3028 non-null float64
44 new_vaccinations_smoothed_per_million 49977 non-null float64
45 stringency_index 101767 non-null float64
46 population 120877 non-null float64
47 population_density 112498 non-null float64
48 median_age 107427 non-null float64
49 aged_65_older 106233 non-null float64
50 aged_70_older 106838 non-null float64
51 gdp_per_capita 108059 non-null float64
52 extreme_poverty 72480 non-null float64
53 cardiovasc_death_rate 107701 non-null float64
54 diabetes_prevalence 111066 non-null float64
55 female_smokers 84084 non-null float64
56 male_smokers 82864 non-null float64
57 handwashing_facilities 54111 non-null float64
58 hospital_beds_per_thousand 97917 non-null float64
59 life_expectancy 115455 non-null float64
60 human_development_index 107796 non-null float64
61 excess_mortality_cumulative_absolute 4322 non-null float64
62 excess_mortality_cumulative 4322 non-null float64
63 excess_mortality 4322 non-null float64
64 excess_mortality_cumulative_per_million 4322 non-null float64
dtypes: datetime64[ns](1), float64(60), object(4)
memory usage: 60.4+ MB
#Filtering by date. A shorter time period was chosen for recency in the analysis of government stringency vs covid severity
covid1 = covid[covid['date'] >= '2021-07-01T00:00:00.000000']
covid1.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 22158 entries, 493 to 121740
Data columns (total 65 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 iso_code 22158 non-null object
1 continent 21276 non-null object
2 location 22158 non-null object
3 date 22158 non-null datetime64[ns]
4 total_cases 19744 non-null float64
5 new_cases 19743 non-null float64
6 new_cases_smoothed 19739 non-null float64
7 total_deaths 19058 non-null float64
8 new_deaths 19056 non-null float64
9 new_deaths_smoothed 19739 non-null float64
10 total_cases_per_million 19646 non-null float64
11 new_cases_per_million 19645 non-null float64
12 new_cases_smoothed_per_million 19641 non-null float64
13 total_deaths_per_million 18960 non-null float64
14 new_deaths_per_million 18958 non-null float64
15 new_deaths_smoothed_per_million 19641 non-null float64
16 reproduction_rate 17575 non-null float64
17 icu_patients 2290 non-null float64
18 icu_patients_per_million 2290 non-null float64
19 hosp_patients 2592 non-null float64
20 hosp_patients_per_million 2592 non-null float64
21 weekly_icu_admissions 213 non-null float64
22 weekly_icu_admissions_per_million 213 non-null float64
23 weekly_hosp_admissions 352 non-null float64
24 weekly_hosp_admissions_per_million 352 non-null float64
25 new_tests 7747 non-null float64
26 total_tests 8061 non-null float64
27 total_tests_per_thousand 8061 non-null float64
28 new_tests_per_thousand 7747 non-null float64
29 new_tests_smoothed 10596 non-null float64
30 new_tests_smoothed_per_thousand 10596 non-null float64
31 positive_rate 10158 non-null float64
32 tests_per_case 10147 non-null float64
33 tests_units 10875 non-null object
34 total_vaccinations 11126 non-null float64
35 people_vaccinated 10746 non-null float64
36 people_fully_vaccinated 10577 non-null float64
37 total_boosters 1720 non-null float64
38 new_vaccinations 9026 non-null float64
39 new_vaccinations_smoothed 20446 non-null float64
40 total_vaccinations_per_hundred 11126 non-null float64
41 people_vaccinated_per_hundred 10746 non-null float64
42 people_fully_vaccinated_per_hundred 10577 non-null float64
43 total_boosters_per_hundred 1720 non-null float64
44 new_vaccinations_smoothed_per_million 20446 non-null float64
45 stringency_index 16543 non-null float64
46 population 21972 non-null float64
47 population_density 20121 non-null float64
48 median_age 18612 non-null float64
49 aged_65_older 18416 non-null float64
50 aged_70_older 18514 non-null float64
51 gdp_per_capita 18882 non-null float64
52 extreme_poverty 12322 non-null float64
53 cardiovasc_death_rate 18515 non-null float64
54 diabetes_prevalence 19567 non-null float64
55 female_smokers 14317 non-null float64
56 male_smokers 14121 non-null float64
57 handwashing_facilities 9310 non-null float64
58 hospital_beds_per_thousand 16669 non-null float64
59 life_expectancy 20931 non-null float64
60 human_development_index 18469 non-null float64
61 excess_mortality_cumulative_absolute 418 non-null float64
62 excess_mortality_cumulative 418 non-null float64
63 excess_mortality 418 non-null float64
64 excess_mortality_cumulative_per_million 418 non-null float64
dtypes: datetime64[ns](1), float64(60), object(4)
memory usage: 11.2+ MB
#Grouping the rows by country and taking the mean of all values so that each row represents one country
covid1 = covid1.groupby('location').mean()
covid1.info()
<class 'pandas.core.frame.DataFrame'>
Index: 230 entries, Afghanistan to Zimbabwe
Data columns (total 60 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 total_cases 202 non-null float64
1 new_cases 202 non-null float64
2 new_cases_smoothed 202 non-null float64
3 total_deaths 195 non-null float64
4 new_deaths 195 non-null float64
5 new_deaths_smoothed 202 non-null float64
6 total_cases_per_million 201 non-null float64
7 new_cases_per_million 201 non-null float64
8 new_cases_smoothed_per_million 201 non-null float64
9 total_deaths_per_million 194 non-null float64
10 new_deaths_per_million 194 non-null float64
11 new_deaths_smoothed_per_million 201 non-null float64
12 reproduction_rate 185 non-null float64
13 icu_patients 26 non-null float64
14 icu_patients_per_million 26 non-null float64
15 hosp_patients 30 non-null float64
16 hosp_patients_per_million 30 non-null float64
17 weekly_icu_admissions 17 non-null float64
18 weekly_icu_admissions_per_million 17 non-null float64
19 weekly_hosp_admissions 28 non-null float64
20 weekly_hosp_admissions_per_million 28 non-null float64
21 new_tests 115 non-null float64
22 total_tests 120 non-null float64
23 total_tests_per_thousand 120 non-null float64
24 new_tests_per_thousand 115 non-null float64
25 new_tests_smoothed 123 non-null float64
26 new_tests_smoothed_per_thousand 123 non-null float64
27 positive_rate 121 non-null float64
28 tests_per_case 121 non-null float64
29 total_vaccinations 223 non-null float64
30 people_vaccinated 222 non-null float64
31 people_fully_vaccinated 221 non-null float64
32 total_boosters 31 non-null float64
33 new_vaccinations 153 non-null float64
34 new_vaccinations_smoothed 223 non-null float64
35 total_vaccinations_per_hundred 223 non-null float64
36 people_vaccinated_per_hundred 222 non-null float64
37 people_fully_vaccinated_per_hundred 221 non-null float64
38 total_boosters_per_hundred 31 non-null float64
39 new_vaccinations_smoothed_per_million 223 non-null float64
40 stringency_index 181 non-null float64
41 population 228 non-null float64
42 population_density 207 non-null float64
43 median_age 190 non-null float64
44 aged_65_older 188 non-null float64
45 aged_70_older 189 non-null float64
46 gdp_per_capita 194 non-null float64
47 extreme_poverty 126 non-null float64
48 cardiovasc_death_rate 189 non-null float64
49 diabetes_prevalence 201 non-null float64
50 female_smokers 147 non-null float64
51 male_smokers 145 non-null float64
52 handwashing_facilities 95 non-null float64
53 hospital_beds_per_thousand 171 non-null float64
54 life_expectancy 217 non-null float64
55 human_development_index 189 non-null float64
56 excess_mortality_cumulative_absolute 62 non-null float64
57 excess_mortality_cumulative 62 non-null float64
58 excess_mortality 62 non-null float64
59 excess_mortality_cumulative_per_million 62 non-null float64
dtypes: float64(60)
memory usage: 109.6+ KB
covid1.head()
covid1.info()
<class 'pandas.core.frame.DataFrame'>
Index: 230 entries, Afghanistan to Zimbabwe
Data columns (total 60 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 total_cases 202 non-null float64
1 new_cases 202 non-null float64
2 new_cases_smoothed 202 non-null float64
3 total_deaths 195 non-null float64
4 new_deaths 195 non-null float64
5 new_deaths_smoothed 202 non-null float64
6 total_cases_per_million 201 non-null float64
7 new_cases_per_million 201 non-null float64
8 new_cases_smoothed_per_million 201 non-null float64
9 total_deaths_per_million 194 non-null float64
10 new_deaths_per_million 194 non-null float64
11 new_deaths_smoothed_per_million 201 non-null float64
12 reproduction_rate 185 non-null float64
13 icu_patients 26 non-null float64
14 icu_patients_per_million 26 non-null float64
15 hosp_patients 30 non-null float64
16 hosp_patients_per_million 30 non-null float64
17 weekly_icu_admissions 17 non-null float64
18 weekly_icu_admissions_per_million 17 non-null float64
19 weekly_hosp_admissions 28 non-null float64
20 weekly_hosp_admissions_per_million 28 non-null float64
21 new_tests 115 non-null float64
22 total_tests 120 non-null float64
23 total_tests_per_thousand 120 non-null float64
24 new_tests_per_thousand 115 non-null float64
25 new_tests_smoothed 123 non-null float64
26 new_tests_smoothed_per_thousand 123 non-null float64
27 positive_rate 121 non-null float64
28 tests_per_case 121 non-null float64
29 total_vaccinations 223 non-null float64
30 people_vaccinated 222 non-null float64
31 people_fully_vaccinated 221 non-null float64
32 total_boosters 31 non-null float64
33 new_vaccinations 153 non-null float64
34 new_vaccinations_smoothed 223 non-null float64
35 total_vaccinations_per_hundred 223 non-null float64
36 people_vaccinated_per_hundred 222 non-null float64
37 people_fully_vaccinated_per_hundred 221 non-null float64
38 total_boosters_per_hundred 31 non-null float64
39 new_vaccinations_smoothed_per_million 223 non-null float64
40 stringency_index 181 non-null float64
41 population 228 non-null float64
42 population_density 207 non-null float64
43 median_age 190 non-null float64
44 aged_65_older 188 non-null float64
45 aged_70_older 189 non-null float64
46 gdp_per_capita 194 non-null float64
47 extreme_poverty 126 non-null float64
48 cardiovasc_death_rate 189 non-null float64
49 diabetes_prevalence 201 non-null float64
50 female_smokers 147 non-null float64
51 male_smokers 145 non-null float64
52 handwashing_facilities 95 non-null float64
53 hospital_beds_per_thousand 171 non-null float64
54 life_expectancy 217 non-null float64
55 human_development_index 189 non-null float64
56 excess_mortality_cumulative_absolute 62 non-null float64
57 excess_mortality_cumulative 62 non-null float64
58 excess_mortality 62 non-null float64
59 excess_mortality_cumulative_per_million 62 non-null float64
dtypes: float64(60)
memory usage: 109.6+ KB
sns.regplot('stringency_index','reproduction_rate',data=covid1)
/shared-libs/python3.7/py/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
FutureWarning
sns.regplot('stringency_index','new_cases_per_million',data=covid1)
/shared-libs/python3.7/py/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
FutureWarning
sns.regplot('stringency_index','new_deaths_per_million',data=covid1)
/shared-libs/python3.7/py/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
FutureWarning
sns.regplot('stringency_index','weekly_icu_admissions_per_million',data=covid1)
/shared-libs/python3.7/py/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
FutureWarning
sns.regplot('stringency_index','weekly_hosp_admissions_per_million',data=covid1)
/shared-libs/python3.7/py/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
FutureWarning
mod = smf.ols('reproduction_rate ~ stringency_index', data=covid1)
res = mod.fit()
print(res.summary())
OLS Regression Results
==============================================================================
Dep. Variable: reproduction_rate R-squared: 0.022
Model: OLS Adj. R-squared: 0.016
Method: Least Squares F-statistic: 3.756
Date: Thu, 07 Oct 2021 Prob (F-statistic): 0.0543
Time: 07:58:17 Log-Likelihood: 8.2482
No. Observations: 171 AIC: -12.50
Df Residuals: 169 BIC: -6.213
Df Model: 1
Covariance Type: nonrobust
====================================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------------
Intercept 0.8881 0.060 14.902 0.000 0.770 1.006
stringency_index 0.0022 0.001 1.938 0.054 -4.01e-05 0.004
==============================================================================
Omnibus: 41.085 Durbin-Watson: 1.857
Prob(Omnibus): 0.000 Jarque-Bera (JB): 73.969
Skew: -1.166 Prob(JB): 8.67e-17
Kurtosis: 5.224 Cond. No. 179.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
mod1 = smf.ols('new_cases_per_million ~ stringency_index', data=covid1)
res1 = mod1.fit()
print(res1.summary())
OLS Regression Results
=================================================================================
Dep. Variable: new_cases_per_million R-squared: 0.056
Model: OLS Adj. R-squared: 0.051
Method: Least Squares F-statistic: 10.26
Date: Thu, 07 Oct 2021 Prob (F-statistic): 0.00162
Time: 07:58:17 Log-Likelihood: -1110.5
No. Observations: 175 AIC: 2225.
Df Residuals: 173 BIC: 2231.
Df Model: 1
Covariance Type: nonrobust
====================================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------------
Intercept 13.4405 34.861 0.386 0.700 -55.367 82.248
stringency_index 2.1012 0.656 3.203 0.002 0.806 3.396
==============================================================================
Omnibus: 74.477 Durbin-Watson: 1.869
Prob(Omnibus): 0.000 Jarque-Bera (JB): 196.199
Skew: 1.860 Prob(JB): 2.49e-43
Kurtosis: 6.616 Cond. No. 177.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
mod2 = smf.ols('new_deaths_per_million ~ stringency_index', data=covid1)
res2 = mod2.fit()
print(res2.summary())
OLS Regression Results
==================================================================================
Dep. Variable: new_deaths_per_million R-squared: 0.129
Model: OLS Adj. R-squared: 0.124
Method: Least Squares F-statistic: 25.33
Date: Thu, 07 Oct 2021 Prob (F-statistic): 1.22e-06
Time: 07:58:18 Log-Likelihood: -355.01
No. Observations: 173 AIC: 714.0
Df Residuals: 171 BIC: 720.3
Df Model: 1
Covariance Type: nonrobust
====================================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------------
Intercept -0.7308 0.481 -1.521 0.130 -1.679 0.218
stringency_index 0.0454 0.009 5.033 0.000 0.028 0.063
==============================================================================
Omnibus: 70.507 Durbin-Watson: 2.187
Prob(Omnibus): 0.000 Jarque-Bera (JB): 187.697
Skew: 1.752 Prob(JB): 1.75e-41
Kurtosis: 6.709 Cond. No. 178.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
mod3 = smf.ols('weekly_icu_admissions_per_million ~ stringency_index', data=covid1)
res3 = mod3.fit()
print(res3.summary())
OLS Regression Results
=============================================================================================
Dep. Variable: weekly_icu_admissions_per_million R-squared: 0.036
Model: OLS Adj. R-squared: -0.028
Method: Least Squares F-statistic: 0.5604
Date: Thu, 07 Oct 2021 Prob (F-statistic): 0.466
Time: 07:58:18 Log-Likelihood: -64.103
No. Observations: 17 AIC: 132.2
Df Residuals: 15 BIC: 133.9
Df Model: 1
Covariance Type: nonrobust
====================================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------------
Intercept -0.8491 13.903 -0.061 0.952 -30.482 28.784
stringency_index 0.2491 0.333 0.749 0.466 -0.460 0.958
==============================================================================
Omnibus: 22.543 Durbin-Watson: 1.691
Prob(Omnibus): 0.000 Jarque-Bera (JB): 26.896
Skew: 2.143 Prob(JB): 1.44e-06
Kurtosis: 7.428 Cond. No. 214.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
/shared-libs/python3.7/py/lib/python3.7/site-packages/scipy/stats/stats.py:1542: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=17
"anyway, n=%i" % int(n))
mod4= smf.ols('weekly_hosp_admissions_per_million ~ stringency_index', data=covid1)
res4 = mod4.fit()
print(res4.summary())
OLS Regression Results
==============================================================================================
Dep. Variable: weekly_hosp_admissions_per_million R-squared: 0.015
Model: OLS Adj. R-squared: -0.023
Method: Least Squares F-statistic: 0.3946
Date: Thu, 07 Oct 2021 Prob (F-statistic): 0.535
Time: 07:58:18 Log-Likelihood: -143.95
No. Observations: 28 AIC: 291.9
Df Residuals: 26 BIC: 294.6
Df Model: 1
Covariance Type: nonrobust
====================================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------------
Intercept 24.1641 38.612 0.626 0.537 -55.204 103.532
stringency_index 0.5428 0.864 0.628 0.535 -1.233 2.319
==============================================================================
Omnibus: 10.996 Durbin-Watson: 1.538
Prob(Omnibus): 0.004 Jarque-Bera (JB): 9.580
Skew: 1.286 Prob(JB): 0.00831
Kurtosis: 4.262 Cond. No. 213.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
# Creating the regression output table for better model comparison
dfoutput = summary_col([res,res1,res2,res3,res4],stars=True)
print(dfoutput)
====================================================================================================================================================
reproduction_rate new_cases_per_million new_deaths_per_million weekly_icu_admissions_per_million weekly_hosp_admissions_per_million
----------------------------------------------------------------------------------------------------------------------------------------------------
Intercept 0.8881*** 13.4405 -0.7308 -0.8491 24.1641
(0.0596) (34.8608) (0.4806) (13.9027) (38.6121)
stringency_index 0.0022* 2.1012*** 0.0454*** 0.2491 0.5428
(0.0011) (0.6560) (0.0090) (0.3328) (0.8640)
R-squared 0.0217 0.0560 0.1290 0.0360 0.0150
R-squared Adj. 0.0160 0.0505 0.1239 -0.0282 -0.0229
====================================================================================================================================================
Standard errors in parentheses.
* p<.1, ** p<.05, ***p<.01
input_1