Life Expectancy¶
We are going to take a quick tour of machine learning by working on an example dataset. The mushroom dataset categorizes mushrooms as 'poisonous' or 'edible' and collects several descriptive properties of each mushroom example.
In [92]:
import pandas as pd
import os
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
In [93]:
pd.options.display.max_rows = 20 # Shows 20 rows
pd.options.display.max_columns = 20 # Shows 20 columns
Loading the dataset¶
In [94]:
# These lines would load the data locally
data_root = "./"
filename = "Life_Expectancy_Data.csv"
filepath = os.path.join(data_root, filename)
# We'll fetch it directly from the web
# data_url = "https://aet-cs.github.io/white/ML/lessons/mushroom.csv"
df = pd.read_csv(filepath)
df
Out[94]:
| Country | Year | Status | Life expectancy | Adult Mortality | infant deaths | Alcohol | percentage expenditure | Hepatitis B | Measles | ... | Polio | Total expenditure | Diphtheria | HIV/AIDS | GDP | Population | thinness 1-19 years | thinness 5-9 years | Income composition of resources | Schooling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | 2015 | Developing | 65.0 | 263.0 | 62 | 0.01 | 71.279624 | 65.0 | 1154 | ... | 6.0 | 8.16 | 65.0 | 0.1 | 584.259210 | 33736494.0 | 17.2 | 17.3 | 0.479 | 10.1 |
| 1 | Afghanistan | 2014 | Developing | 59.9 | 271.0 | 64 | 0.01 | 73.523582 | 62.0 | 492 | ... | 58.0 | 8.18 | 62.0 | 0.1 | 612.696514 | 327582.0 | 17.5 | 17.5 | 0.476 | 10.0 |
| 2 | Afghanistan | 2013 | Developing | 59.9 | 268.0 | 66 | 0.01 | 73.219243 | 64.0 | 430 | ... | 62.0 | 8.13 | 64.0 | 0.1 | 631.744976 | 31731688.0 | 17.7 | 17.7 | 0.470 | 9.9 |
| 3 | Afghanistan | 2012 | Developing | 59.5 | 272.0 | 69 | 0.01 | 78.184215 | 67.0 | 2787 | ... | 67.0 | 8.52 | 67.0 | 0.1 | 669.959000 | 3696958.0 | 17.9 | 18.0 | 0.463 | 9.8 |
| 4 | Afghanistan | 2011 | Developing | 59.2 | 275.0 | 71 | 0.01 | 7.097109 | 68.0 | 3013 | ... | 68.0 | 7.87 | 68.0 | 0.1 | 63.537231 | 2978599.0 | 18.2 | 18.2 | 0.454 | 9.5 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2933 | Zimbabwe | 2004 | Developing | 44.3 | 723.0 | 27 | 4.36 | 0.000000 | 68.0 | 31 | ... | 67.0 | 7.13 | 65.0 | 33.6 | 454.366654 | 12777511.0 | 9.4 | 9.4 | 0.407 | 9.2 |
| 2934 | Zimbabwe | 2003 | Developing | 44.5 | 715.0 | 26 | 4.06 | 0.000000 | 7.0 | 998 | ... | 7.0 | 6.52 | 68.0 | 36.7 | 453.351155 | 12633897.0 | 9.8 | 9.9 | 0.418 | 9.5 |
| 2935 | Zimbabwe | 2002 | Developing | 44.8 | 73.0 | 25 | 4.43 | 0.000000 | 73.0 | 304 | ... | 73.0 | 6.53 | 71.0 | 39.8 | 57.348340 | 125525.0 | 1.2 | 1.3 | 0.427 | 10.0 |
| 2936 | Zimbabwe | 2001 | Developing | 45.3 | 686.0 | 25 | 1.72 | 0.000000 | 76.0 | 529 | ... | 76.0 | 6.16 | 75.0 | 42.1 | 548.587312 | 12366165.0 | 1.6 | 1.7 | 0.427 | 9.8 |
| 2937 | Zimbabwe | 2000 | Developing | 46.0 | 665.0 | 24 | 1.68 | 0.000000 | 79.0 | 1483 | ... | 78.0 | 7.10 | 78.0 | 43.5 | 547.358878 | 12222251.0 | 11.0 | 11.2 | 0.434 | 9.8 |
2938 rows × 22 columns
describe gives a quick overview of each feature
In [95]:
df.describe()
Out[95]:
| Year | Life expectancy | Adult Mortality | infant deaths | Alcohol | percentage expenditure | Hepatitis B | Measles | BMI | under-five deaths | Polio | Total expenditure | Diphtheria | HIV/AIDS | GDP | Population | thinness 1-19 years | thinness 5-9 years | Income composition of resources | Schooling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 2938.000000 | 2928.000000 | 2928.000000 | 2938.000000 | 2744.000000 | 2938.000000 | 2385.000000 | 2938.000000 | 2904.000000 | 2938.000000 | 2919.000000 | 2712.00000 | 2919.000000 | 2938.000000 | 2490.000000 | 2.286000e+03 | 2904.000000 | 2904.000000 | 2771.000000 | 2775.000000 |
| mean | 2007.518720 | 69.224932 | 164.796448 | 30.303948 | 4.602861 | 738.251295 | 80.940461 | 2419.592240 | 38.321247 | 42.035739 | 82.550188 | 5.93819 | 82.324084 | 1.742103 | 7483.158469 | 1.275338e+07 | 4.839704 | 4.870317 | 0.627551 | 11.992793 |
| std | 4.613841 | 9.523867 | 124.292079 | 117.926501 | 4.052413 | 1987.914858 | 25.070016 | 11467.272489 | 20.044034 | 160.445548 | 23.428046 | 2.49832 | 23.716912 | 5.077785 | 14270.169342 | 6.101210e+07 | 4.420195 | 4.508882 | 0.210904 | 3.358920 |
| min | 2000.000000 | 36.300000 | 1.000000 | 0.000000 | 0.010000 | 0.000000 | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 3.000000 | 0.37000 | 2.000000 | 0.100000 | 1.681350 | 3.400000e+01 | 0.100000 | 0.100000 | 0.000000 | 0.000000 |
| 25% | 2004.000000 | 63.100000 | 74.000000 | 0.000000 | 0.877500 | 4.685343 | 77.000000 | 0.000000 | 19.300000 | 0.000000 | 78.000000 | 4.26000 | 78.000000 | 0.100000 | 463.935626 | 1.957932e+05 | 1.600000 | 1.500000 | 0.493000 | 10.100000 |
| 50% | 2008.000000 | 72.100000 | 144.000000 | 3.000000 | 3.755000 | 64.912906 | 92.000000 | 17.000000 | 43.500000 | 4.000000 | 93.000000 | 5.75500 | 93.000000 | 0.100000 | 1766.947595 | 1.386542e+06 | 3.300000 | 3.300000 | 0.677000 | 12.300000 |
| 75% | 2012.000000 | 75.700000 | 228.000000 | 22.000000 | 7.702500 | 441.534144 | 97.000000 | 360.250000 | 56.200000 | 28.000000 | 97.000000 | 7.49250 | 97.000000 | 0.800000 | 5910.806335 | 7.420359e+06 | 7.200000 | 7.200000 | 0.779000 | 14.300000 |
| max | 2015.000000 | 89.000000 | 723.000000 | 1800.000000 | 17.870000 | 19479.911610 | 99.000000 | 212183.000000 | 87.300000 | 2500.000000 | 99.000000 | 17.60000 | 99.000000 | 50.600000 | 119172.741800 | 1.293859e+09 | 27.700000 | 28.600000 | 0.948000 | 20.700000 |
Data Exploration¶
Show all the columns. Target is 'Life expectancy'
In [96]:
df.columns
Out[96]:
Index(['Country', 'Year', 'Status', 'Life expectancy', 'Adult Mortality',
'infant deaths', 'Alcohol', 'percentage expenditure', 'Hepatitis B',
'Measles', 'BMI', 'under-five deaths', 'Polio', 'Total expenditure',
'Diphtheria', 'HIV/AIDS', 'GDP', 'Population', 'thinness 1-19 years',
'thinness 5-9 years', 'Income composition of resources', 'Schooling'],
dtype='object')
Get the size of the dataframe. Shape returns (rows, cols)
In [97]:
df.shape
Out[97]:
(2938, 22)
Let's get all the data types
In [98]:
df.dtypes
Out[98]:
Country object
Year int64
Status object
Life expectancy float64
Adult Mortality float64
...
Population float64
thinness 1-19 years float64
thinness 5-9 years float64
Income composition of resources float64
Schooling float64
Length: 22, dtype: object
In [99]:
df.hist(target)
Out[99]:
array([[<Axes: title={'center': 'Life expectancy'}>]], dtype=object)
Correlation matrix heat map¶
Let's get a quick visual representation of the relationshop between features in this dataset.
In [100]:
df_heat = df.drop(["Country", "Status"], axis = 1)
In [101]:
corr_matrix = df_heat.corr()
In [104]:
plt.figure(figsize=(16, 6))
# define the mask to set the values in the upper triangle to True
mask = np.triu(np.ones_like(corr_matrix, dtype=np.bool))
heatmap = sns.heatmap(corr_matrix, mask=mask, vmin=-1, vmax=1, annot=True, cmap='BrBG')
heatmap.set_title('Life Expectancy Correlation Heatmap', fontdict={'fontsize':14}, pad=16);
plt.show()
Which features seem to be important?
In [107]:
row_filter = abs(corr_matrix[target])>0.1
top_features = pd.DataFrame(corr_matrix[target][row_filter])
top_features.sort_values(by=target)
Out[107]:
| Life expectancy | |
|---|---|
| Adult Mortality | -0.696359 |
| HIV/AIDS | -0.556556 |
| thinness 1-19 years | -0.477183 |
| thinness 5-9 years | -0.471584 |
| under-five deaths | -0.222529 |
| infant deaths | -0.196557 |
| Measles | -0.157586 |
| Year | 0.170033 |
| Total expenditure | 0.218086 |
| Hepatitis B | 0.256762 |
| percentage expenditure | 0.381864 |
| Alcohol | 0.404877 |
| GDP | 0.461455 |
| Polio | 0.465556 |
| Diphtheria | 0.479495 |
| BMI | 0.567694 |
| Income composition of resources | 0.724776 |
| Schooling | 0.751975 |
| Life expectancy | 1.000000 |
Data Modeling¶
We're finally ready to do some data modeling using scikit-learn. In this cell we import some methods we'll use, reload the data frame (just to be safe), re-pre-process-it, and one-hot-encode all the categorical variables.
In [108]:
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score, classification_report
Linear Regression¶
In [109]:
def get_data(filename):
df = pd.read_csv(filename)
return df
In [110]:
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OrdinalEncoder
def pre_process_data(df, one_hot_encode = False):
target = "Life expectancy"
simple_median = SimpleImputer(strategy='median')
simple_most_freq = SimpleImputer(strategy='most_frequent')
num_cols = df.select_dtypes(include=np.number).columns
cat_cols = df.select_dtypes(include=object).columns
df[num_cols] = simple_median.fit_transform(df[num_cols])
df[cat_cols] = simple_most_freq.fit_transform(df[cat_cols])
# O_encoder = OrdinalEncoder()
# df[cat_cols]= O_encoder.fit_transform(df[cat_cols])
if one_hot_encode:
df = pd.get_dummies(df, dtype=int)
return df
In [111]:
def get_test_train(df, test_size = 0.2, random_state = 42):
target = "Life expectancy"
X = df.drop(target, axis=1)
y = df[target]
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=test_size, random_state=random_state)
return X_train, X_test, y_train, y_test
In [112]:
df = get_data(filename)
df = pre_process_data(df, one_hot_encode = True)
X_train, X_test, y_train, y_test = get_test_train(df)
In [113]:
clf = LinearRegression()
model = clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
print(f"Train R-squared = {r2_score(clf.predict(X_train), y_train):5.3}")
print(f"Test R-squared = {r2_score(y_pred, y_test):5.3}")
Train R-squared = 0.962 Test R-squared = 0.964
In [114]:
plt.scatter(y_test, y_pred);
plt.plot([40,90],[40,90],color='red')
plt.title("Predicted LE vs. Real LE")
plt.xlabel("True Life Expectancy")
plt.ylabel("Predicted LE")
Out[114]:
Text(0, 0.5, 'Predicted LE')
Advanced Techniques¶
In [115]:
import matplotlib.pyplot as plt
import pandas as pd
# Assuming `df` is your DataFrame with 20 numerical features and 'LifeExpectancy' as the target variable
target = 'Life expectancy'
df = get_data(filename)
df = pre_process_data(df)
features = df.drop(target, axis=1).select_dtypes(include=['number']).columns
# Set up a grid of subplots
num_features = len(features)
num_cols = 5 # Adjust this to your preference
num_rows = (num_features + num_cols - 1) // num_cols # Calculates rows needed
plt.figure(figsize=(15, num_rows * 3))
for i, feature in enumerate(features, 1):
plt.subplot(num_rows, num_cols, i)
plt.scatter(df[feature], df[target], alpha=0.5)
plt.title(f'{feature} vs. LE')
plt.xlabel(feature)
plt.ylabel(target)
plt.tight_layout()
plt.show()
In [116]:
df.hist(figsize=(15,15));
Pre-processing¶
In [117]:
import statsmodels.api as sm
# Add a constant to the model (intercept)
# Fit the OLS model
model = sm.OLS(y_train, X_train).fit()
# Get a summary of the regression
print(model.summary())
OLS Regression Results
==============================================================================
Dep. Variable: Life expectancy R-squared: 0.963
Model: OLS Adj. R-squared: 0.960
Method: Least Squares F-statistic: 267.9
Date: Wed, 09 Oct 2024 Prob (F-statistic): 0.00
Time: 13:15:47 Log-Likelihood: -4758.9
No. Observations: 2350 AIC: 9938.
Df Residuals: 2140 BIC: 1.115e+04
Df Model: 209
Covariance Type: nonrobust
================================================================================================================================
coef std err t P>|t| [0.025 0.975]
--------------------------------------------------------------------------------------------------------------------------------
Year 0.2628 0.012 21.916 0.000 0.239 0.286
Adult Mortality -0.0023 0.001 -4.404 0.000 -0.003 -0.001
infant deaths 0.0780 0.012 6.412 0.000 0.054 0.102
Alcohol -0.0677 0.027 -2.542 0.011 -0.120 -0.015
percentage expenditure 0.0001 5.68e-05 2.340 0.019 2.15e-05 0.000
Hepatitis B -0.0058 0.002 -2.601 0.009 -0.010 -0.001
Measles -9.721e-06 4.84e-06 -2.008 0.045 -1.92e-05 -2.26e-07
BMI -0.0051 0.003 -1.513 0.130 -0.012 0.002
under-five deaths -0.0572 0.008 -6.785 0.000 -0.074 -0.041
Polio 0.0031 0.003 1.203 0.229 -0.002 0.008
Total expenditure -0.0454 0.025 -1.811 0.070 -0.095 0.004
Diphtheria 0.0078 0.003 2.866 0.004 0.002 0.013
HIV/AIDS -0.3209 0.017 -18.573 0.000 -0.355 -0.287
GDP -1.317e-05 8.83e-06 -1.492 0.136 -3.05e-05 4.14e-06
Population -1.166e-09 1.02e-09 -1.138 0.255 -3.18e-09 8.43e-10
thinness 1-19 years 0.0204 0.032 0.645 0.519 -0.042 0.082
thinness 5-9 years 0.0319 0.032 1.012 0.312 -0.030 0.094
Income composition of resources -0.1747 0.511 -0.342 0.733 -1.177 0.828
Schooling 0.1478 0.046 3.234 0.001 0.058 0.237
Country_Afghanistan -12.5602 0.654 -19.206 0.000 -13.843 -11.278
Country_Albania 4.1251 0.546 7.562 0.000 3.055 5.195
Country_Algeria 1.9191 0.572 3.356 0.001 0.798 3.040
Country_Angola -19.1966 0.634 -30.267 0.000 -20.440 -17.953
Country_Antigua and Barbuda 4.8363 0.613 7.896 0.000 3.635 6.038
Country_Argentina 3.8199 0.590 6.479 0.000 2.664 4.976
Country_Armenia 2.4163 0.601 4.018 0.000 1.237 3.596
Country_Australia -11.7984 0.828 -14.243 0.000 -13.423 -10.174
Country_Austria -11.4436 0.922 -12.409 0.000 -13.252 -9.635
Country_Azerbaijan -0.5232 0.536 -0.977 0.329 -1.574 0.527
Country_Bahamas 3.6949 0.627 5.896 0.000 2.466 4.924
Country_Bahrain 4.0994 0.599 6.841 0.000 2.924 5.275
Country_Bangladesh -2.7625 0.704 -3.922 0.000 -4.144 -1.381
Country_Barbados 3.3191 0.612 5.427 0.000 2.120 4.518
Country_Belarus -0.4641 0.634 -0.732 0.465 -1.708 0.780
Country_Belgium -12.1375 0.887 -13.676 0.000 -13.878 -10.397
Country_Belize -1.3670 0.576 -2.373 0.018 -2.497 -0.237
Country_Benin -12.3161 0.594 -20.723 0.000 -13.482 -11.151
Country_Bhutan -5.6748 0.721 -7.868 0.000 -7.089 -4.260
Country_Bolivia (Plurinational State of) -3.0289 0.580 -5.225 0.000 -4.166 -1.892
Country_Bosnia and Herzegovina 5.1239 0.611 8.389 0.000 3.926 6.322
Country_Botswana -9.2538 0.582 -15.888 0.000 -10.396 -8.112
Country_Brazil 1.5147 0.631 2.402 0.016 0.278 2.751
Country_Brunei Darussalam 4.6145 0.537 8.597 0.000 3.562 5.667
Country_Bulgaria -19.4348 0.925 -21.002 0.000 -21.250 -17.620
Country_Burkina Faso -12.7039 0.750 -16.938 0.000 -14.175 -11.233
Country_Burundi -13.3554 0.638 -20.919 0.000 -14.607 -12.103
Country_Cabo Verde 1.3837 0.581 2.381 0.017 0.244 2.523
Country_Cambodia -6.2931 0.614 -10.243 0.000 -7.498 -5.088
Country_Cameroon -13.7371 0.599 -22.928 0.000 -14.912 -12.562
Country_Canada 10.3186 0.646 15.966 0.000 9.051 11.586
Country_Central African Republic -18.2718 0.665 -27.468 0.000 -19.576 -16.967
Country_Chad -17.1495 0.699 -24.536 0.000 -18.520 -15.779
Country_Chile 8.0832 0.595 13.578 0.000 6.916 9.251
Country_China 0.6775 1.005 0.674 0.500 -1.293 2.648
Country_Colombia 2.3495 0.518 4.536 0.000 1.334 3.365
Country_Comoros -8.9342 0.638 -13.996 0.000 -10.186 -7.682
Country_Congo -10.2120 0.578 -17.680 0.000 -11.345 -9.079
Country_Cook Islands -0.3522 1.929 -0.183 0.855 -4.135 3.430
Country_Costa Rica 7.5549 0.510 14.817 0.000 6.555 8.555
Country_Croatia -16.2245 0.946 -17.148 0.000 -18.080 -14.369
Country_Cuba 6.3740 0.523 12.186 0.000 5.348 7.400
Country_Cyprus -12.6044 0.931 -13.539 0.000 -14.430 -10.779
Country_Czechia -15.3735 0.987 -15.578 0.000 -17.309 -13.438
Country_Côte d'Ivoire -18.7114 0.621 -30.153 0.000 -19.928 -17.494
Country_Democratic People's Republic of Korea -2.3606 0.531 -4.446 0.000 -3.402 -1.319
Country_Democratic Republic of the Congo -14.0841 0.704 -20.001 0.000 -15.465 -12.703
Country_Denmark -13.7976 0.859 -16.069 0.000 -15.482 -12.114
Country_Djibouti -8.6067 0.672 -12.802 0.000 -9.925 -7.288
Country_Dominica -0.5753 1.920 -0.300 0.765 -4.341 3.191
Country_Dominican Republic 1.6690 0.540 3.091 0.002 0.610 2.728
Country_Ecuador 3.6705 0.518 7.080 0.000 2.654 4.687
Country_Egypt -0.0465 0.604 -0.077 0.939 -1.232 1.139
Country_El Salvador 0.6517 0.536 1.216 0.224 -0.400 1.703
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Country_Eritrea -9.2433 0.669 -13.826 0.000 -10.554 -7.932
Country_Estonia 3.7999 0.638 5.960 0.000 2.550 5.050
Country_Ethiopia -7.9965 0.686 -11.649 0.000 -9.343 -6.650
Country_Fiji -2.9123 0.576 -5.053 0.000 -4.043 -1.782
Country_Finland 9.4469 0.584 16.165 0.000 8.301 10.593
Country_France 10.8405 0.672 16.124 0.000 9.522 12.159
Country_Gabon -5.7206 0.571 -10.017 0.000 -6.841 -4.601
Country_Gambia -10.2851 0.564 -18.248 0.000 -11.390 -9.180
Country_Georgia 2.5948 0.535 4.848 0.000 1.545 3.644
Country_Germany -11.6530 0.891 -13.086 0.000 -13.399 -9.907
Country_Ghana -8.8819 0.622 -14.290 0.000 -10.101 -7.663
Country_Greece 10.6086 0.662 16.018 0.000 9.310 11.907
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Country_Iceland -10.9866 0.830 -13.242 0.000 -12.614 -9.359
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Country_Indonesia -4.3764 0.720 -6.076 0.000 -5.789 -2.964
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Country_Ireland -13.0366 0.854 -15.262 0.000 -14.712 -11.361
Country_Israel 9.8839 0.542 18.237 0.000 8.821 10.947
Country_Italy -10.2338 0.897 -11.414 0.000 -11.992 -8.476
Country_Jamaica 3.7132 0.519 7.154 0.000 2.695 4.731
Country_Japan -10.7261 0.915 -11.719 0.000 -12.521 -8.931
Country_Jordan 1.6096 0.628 2.565 0.010 0.379 2.840
Country_Kazakhstan -4.1239 0.530 -7.783 0.000 -5.163 -3.085
Country_Kenya -9.8681 0.701 -14.078 0.000 -11.243 -8.494
Country_Kiribati -5.8018 0.560 -10.359 0.000 -6.900 -4.703
Country_Kuwait 2.3244 0.572 4.060 0.000 1.202 3.447
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Country_Lao People's Democratic Republic -8.6250 0.652 -13.220 0.000 -9.904 -7.346
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Country_Lebanon 2.7358 0.551 4.968 0.000 1.656 3.816
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Country_Liberia -11.8216 0.637 -18.548 0.000 -13.071 -10.572
Country_Libya 0.8550 0.561 1.524 0.128 -0.245 1.955
Country_Lithuania -19.8510 0.910 -21.818 0.000 -21.635 -18.067
Country_Luxembourg -12.1310 0.936 -12.957 0.000 -13.967 -10.295
Country_Madagascar -7.3647 0.611 -12.063 0.000 -8.562 -6.167
Country_Malawi -14.5938 0.624 -23.372 0.000 -15.818 -13.369
Country_Malaysia 2.0359 0.544 3.741 0.000 0.969 3.103
Country_Maldives 3.8868 0.708 5.489 0.000 2.498 5.276
Country_Mali -13.7600 0.614 -22.421 0.000 -14.964 -12.556
Country_Malta -12.2713 0.879 -13.955 0.000 -13.996 -10.547
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Country_Paraguay 2.3642 0.539 4.387 0.000 1.307 3.421
Country_Peru 2.5897 0.538 4.809 0.000 1.534 3.646
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Country_Portugal -12.5562 0.908 -13.834 0.000 -14.336 -10.776
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Country_Republic of Korea 9.5900 0.650 14.751 0.000 8.315 10.865
Country_Republic of Moldova 0.0469 0.605 0.077 0.938 -1.140 1.234
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Country_Russian Federation -2.7774 0.559 -4.967 0.000 -3.874 -1.681
Country_Rwanda -9.7279 0.563 -17.272 0.000 -10.832 -8.623
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Country_San Marino 1.281e-15 3.25e-16 3.945 0.000 6.44e-16 1.92e-15
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Country_Ukraine -0.7919 0.523 -1.514 0.130 -1.818 0.234
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Country_United States of America -14.2563 0.974 -14.634 0.000 -16.167 -12.346
Country_Uruguay 5.1690 0.553 9.350 0.000 4.085 6.253
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Country_Vanuatu 0.6602 0.568 1.162 0.245 -0.454 1.774
Country_Venezuela (Bolivarian Republic of) 2.8068 0.589 4.764 0.000 1.651 3.962
Country_Viet Nam 2.7917 0.609 4.583 0.000 1.597 3.986
Country_Yemen -7.2082 0.655 -11.003 0.000 -8.493 -5.924
Country_Zambia -12.2124 0.573 -21.315 0.000 -13.336 -11.089
Country_Zimbabwe -12.9092 0.694 -18.610 0.000 -14.270 -11.549
Status_Developed -436.0261 22.964 -18.987 0.000 -481.060 -390.992
Status_Developing -457.7453 23.650 -19.355 0.000 -504.125 -411.365
==============================================================================
Omnibus: 570.909 Durbin-Watson: 1.911
Prob(Omnibus): 0.000 Jarque-Bera (JB): 16400.405
Skew: 0.503 Prob(JB): 0.00
Kurtosis: 15.903 Cond. No. 8.21e+24
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 8.56e-32. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.
In [56]:
# Extract the summary table as a DataFrame
summary_table = model.summary2().tables[1] # tables[1] is the coefficients table in summary2()
# Sort by p-values (for example)
sorted_summary = summary_table.sort_values(by='t')
# Set display options to prevent truncation
pd.options.display.max_rows = None # Shows all rows
pd.options.display.max_columns = None # Shows all columns
sorted_summary
Out[56]:
| Coef. | Std.Err. | t | P>|t| | [0.025 | 0.975] | |
|---|---|---|---|---|---|---|
| Country_Sierra Leone | -2.384615e+01 | 7.024424e-01 | -33.947488 | 1.836603e-202 | -2.522370e+01 | -2.246861e+01 |
| Country_Lesotho | -2.098876e+01 | 6.818324e-01 | -30.782881 | 1.390266e-172 | -2.232589e+01 | -1.965164e+01 |
| Country_Swaziland | -1.954167e+01 | 6.639749e-01 | -29.431346 | 3.709886e-160 | -2.084378e+01 | -1.823957e+01 |
| Country_Malawi | -1.955037e+01 | 6.836621e-01 | -28.596539 | 1.340917e-152 | -2.089108e+01 | -1.820966e+01 |
| Country_Zimbabwe | -1.953323e+01 | 7.054177e-01 | -27.690310 | 1.665289e-144 | -2.091661e+01 | -1.814986e+01 |
| Country_Central African Republic | -2.110985e+01 | 7.715096e-01 | -27.361745 | 1.336742e-141 | -2.262284e+01 | -1.959686e+01 |
| Country_Côte d'Ivoire | -1.929140e+01 | 7.193652e-01 | -26.817258 | 7.989231e-137 | -2.070213e+01 | -1.788067e+01 |
| Country_Angola | -1.965137e+01 | 7.382675e-01 | -26.618226 | 4.332100e-135 | -2.109917e+01 | -1.820357e+01 |
| Country_Zambia | -1.577182e+01 | 6.545813e-01 | -24.094511 | 1.076908e-113 | -1.705550e+01 | -1.448814e+01 |
| Country_Lithuania | -2.320593e+01 | 9.969035e-01 | -23.278007 | 4.992191e-107 | -2.516093e+01 | -2.125093e+01 |
| Country_Latvia | -2.308448e+01 | 9.966439e-01 | -23.162217 | 4.292374e-106 | -2.503897e+01 | -2.112999e+01 |
| Country_Chad | -1.885356e+01 | 8.140584e-01 | -23.159955 | 4.476320e-106 | -2.044998e+01 | -1.725713e+01 |
| Country_Hungary | -2.195012e+01 | 9.568583e-01 | -22.939778 | 2.629772e-104 | -2.382659e+01 | -2.007365e+01 |
| Country_Somalia | -1.595300e+01 | 6.996920e-01 | -22.800030 | 3.447001e-103 | -1.732515e+01 | -1.458085e+01 |
| Country_Bulgaria | -2.292869e+01 | 1.009367e+00 | -22.715916 | 1.614692e-102 | -2.490813e+01 | -2.094925e+01 |
| Country_Botswana | -1.399261e+01 | 6.287431e-01 | -22.254894 | 7.191876e-99 | -1.522562e+01 | -1.275960e+01 |
| Country_Cameroon | -1.554462e+01 | 7.000790e-01 | -22.204089 | 1.803475e-98 | -1.691752e+01 | -1.417171e+01 |
| Status_Developing | -5.726251e+02 | 2.611777e+01 | -21.924733 | 2.762853e-96 | -6.238439e+02 | -5.214062e+02 |
| Country_Mozambique | -1.698469e+01 | 7.816731e-01 | -21.728638 | 9.223281e-95 | -1.851761e+01 | -1.545177e+01 |
| Status_Developed | -5.474589e+02 | 2.533915e+01 | -21.605259 | 8.298853e-94 | -5.971509e+02 | -4.977670e+02 |
| Country_Romania | -2.201775e+01 | 1.039419e+00 | -21.182735 | 1.445750e-90 | -2.405612e+01 | -1.997937e+01 |
| Country_Poland | -2.043647e+01 | 9.699842e-01 | -21.068873 | 1.062702e-89 | -2.233868e+01 | -1.853426e+01 |
| Country_Slovakia | -2.053319e+01 | 9.943115e-01 | -20.650659 | 1.520857e-86 | -2.248310e+01 | -1.858327e+01 |
| Country_Guinea-Bissau | -1.553999e+01 | 7.674100e-01 | -20.249917 | 1.467067e-83 | -1.704494e+01 | -1.403504e+01 |
| Country_Mali | -1.456148e+01 | 7.210360e-01 | -20.195226 | 3.721311e-83 | -1.597549e+01 | -1.314748e+01 |
| Country_Togo | -1.408157e+01 | 7.092691e-01 | -19.853632 | 1.198843e-80 | -1.547250e+01 | -1.269064e+01 |
| Country_United Republic of Tanzania | -1.350178e+01 | 6.803865e-01 | -19.844282 | 1.402816e-80 | -1.483607e+01 | -1.216749e+01 |
| Country_Croatia | -1.973008e+01 | 1.031898e+00 | -19.120184 | 2.312525e-75 | -2.175371e+01 | -1.770645e+01 |
| Country_Equatorial Guinea | -1.418284e+01 | 7.418940e-01 | -19.117065 | 2.433683e-75 | -1.563774e+01 | -1.272793e+01 |
| Country_Burundi | -1.430045e+01 | 7.505235e-01 | -19.053966 | 6.829927e-75 | -1.577228e+01 | -1.282862e+01 |
| Country_Benin | -1.286127e+01 | 6.992442e-01 | -18.393099 | 2.915920e-70 | -1.423254e+01 | -1.149000e+01 |
| Country_Denmark | -1.719045e+01 | 9.420532e-01 | -18.247856 | 2.928719e-69 | -1.903789e+01 | -1.534302e+01 |
| Country_Guinea | -1.396273e+01 | 7.769852e-01 | -17.970394 | 2.315181e-67 | -1.548646e+01 | -1.243901e+01 |
| Country_Democratic Republic of the Congo | -1.404247e+01 | 7.991430e-01 | -17.571908 | 1.129680e-64 | -1.560964e+01 | -1.247529e+01 |
| Country_Ireland | -1.641810e+01 | 9.391121e-01 | -17.482573 | 4.462015e-64 | -1.825976e+01 | -1.457643e+01 |
| Country_South Sudan | -1.473396e+01 | 8.460699e-01 | -17.414593 | 1.264681e-63 | -1.639317e+01 | -1.307476e+01 |
| Country_Czechia | -1.830798e+01 | 1.054105e+00 | -17.368271 | 2.567612e-63 | -2.037516e+01 | -1.624081e+01 |
| Country_Afghanistan | -1.244918e+01 | 7.229727e-01 | -17.219437 | 2.474866e-62 | -1.386699e+01 | -1.103138e+01 |
| Country_United States of America | -1.765947e+01 | 1.035835e+00 | -17.048538 | 3.278035e-61 | -1.969081e+01 | -1.562812e+01 |
| Country_South Africa | -1.228523e+01 | 7.221881e-01 | -17.011122 | 5.756352e-61 | -1.370149e+01 | -1.086897e+01 |
| Country_Liberia | -1.251050e+01 | 7.471688e-01 | -16.743867 | 3.125821e-59 | -1.397575e+01 | -1.104524e+01 |
| Country_Uganda | -1.321571e+01 | 7.893853e-01 | -16.741772 | 3.224623e-59 | -1.476375e+01 | -1.166767e+01 |
| Country_Malta | -1.612846e+01 | 9.656338e-01 | -16.702460 | 5.779390e-59 | -1.802214e+01 | -1.423478e+01 |
| Country_Rwanda | -1.107606e+01 | 6.649750e-01 | -16.656350 | 1.144249e-58 | -1.238012e+01 | -9.771991e+00 |
| Country_Australia | -1.512439e+01 | 9.093915e-01 | -16.631334 | 1.656495e-58 | -1.690778e+01 | -1.334101e+01 |
| Country_New Zealand | -1.549884e+01 | 9.374598e-01 | -16.532805 | 7.083176e-58 | -1.733727e+01 | -1.366041e+01 |
| Country_Netherlands | -1.579157e+01 | 9.629190e-01 | -16.399687 | 4.991373e-57 | -1.767993e+01 | -1.390322e+01 |
| Country_Portugal | -1.623373e+01 | 9.933403e-01 | -16.342570 | 1.149358e-56 | -1.818175e+01 | -1.428572e+01 |
| Country_Slovenia | -1.627526e+01 | 9.980914e-01 | -16.306385 | 1.947279e-56 | -1.823259e+01 | -1.431793e+01 |
| Country_Congo | -1.101422e+01 | 6.797341e-01 | -16.203725 | 8.648265e-56 | -1.234723e+01 | -9.681216e+00 |
| Country_Belgium | -1.571360e+01 | 9.743988e-01 | -16.126459 | 2.643485e-55 | -1.762447e+01 | -1.380273e+01 |
| Country_Cyprus | -1.636576e+01 | 1.018493e+00 | -16.068600 | 6.086419e-55 | -1.836310e+01 | -1.436842e+01 |
| Country_Singapore | -1.586329e+01 | 9.929674e-01 | -15.975641 | 2.312848e-54 | -1.781057e+01 | -1.391601e+01 |
| Country_Iceland | -1.451723e+01 | 9.108647e-01 | -15.937861 | 3.972229e-54 | -1.630351e+01 | -1.273096e+01 |
| Country_Norway | -1.527585e+01 | 9.614697e-01 | -15.888021 | 8.095287e-54 | -1.716136e+01 | -1.339034e+01 |
| Country_Spain | -1.485810e+01 | 9.458400e-01 | -15.708889 | 1.031069e-52 | -1.671296e+01 | -1.300323e+01 |
| Country_Gambia | -1.047816e+01 | 6.684227e-01 | -15.675954 | 1.642058e-52 | -1.178899e+01 | -9.167338e+00 |
| Country_Burkina Faso | -1.335458e+01 | 8.560108e-01 | -15.600951 | 4.724924e-52 | -1.503328e+01 | -1.167588e+01 |
| Country_Kenya | -1.234284e+01 | 7.986110e-01 | -15.455380 | 3.633338e-51 | -1.390897e+01 | -1.077670e+01 |
| Country_Germany | -1.508492e+01 | 9.778896e-01 | -15.425994 | 5.474593e-51 | -1.700263e+01 | -1.316721e+01 |
| Country_Luxembourg | -1.555601e+01 | 1.024337e+00 | -15.186417 | 1.512976e-49 | -1.756481e+01 | -1.354721e+01 |
| Country_Sweden | -1.407837e+01 | 9.374418e-01 | -15.017857 | 1.524893e-48 | -1.591676e+01 | -1.223997e+01 |
| Country_Austria | -1.487461e+01 | 1.015020e+00 | -14.654506 | 2.068167e-46 | -1.686514e+01 | -1.288408e+01 |
| Country_Haiti | -1.047507e+01 | 7.208575e-01 | -14.531400 | 1.067751e-45 | -1.188872e+01 | -9.061415e+00 |
| Country_Japan | -1.438887e+01 | 1.002513e+00 | -14.352799 | 1.132490e-44 | -1.635488e+01 | -1.242287e+01 |
| Country_United Kingdom of Great Britain and Northern Ireland | -1.434710e+01 | 1.012838e+00 | -14.165244 | 1.317882e-43 | -1.633335e+01 | -1.236085e+01 |
| Country_Italy | -1.390844e+01 | 9.876524e-01 | -14.082323 | 3.867585e-43 | -1.584530e+01 | -1.197158e+01 |
| Country_Switzerland | -1.411181e+01 | 1.008644e+00 | -13.990877 | 1.260273e-42 | -1.608984e+01 | -1.213379e+01 |
| Country_Ghana | -9.714084e+00 | 7.231396e-01 | -13.433206 | 1.476320e-39 | -1.113221e+01 | -8.295954e+00 |
| Country_Namibia | -9.399137e+00 | 7.010576e-01 | -13.407083 | 2.043533e-39 | -1.077396e+01 | -8.024312e+00 |
| Country_Papua New Guinea | -9.037959e+00 | 6.769572e-01 | -13.350859 | 4.106926e-39 | -1.036552e+01 | -7.710397e+00 |
| Country_Comoros | -9.220819e+00 | 6.917821e-01 | -13.329079 | 5.378435e-39 | -1.057745e+01 | -7.864183e+00 |
| Country_Djibouti | -1.000317e+01 | 7.846496e-01 | -12.748578 | 6.216133e-36 | -1.154192e+01 | -8.464412e+00 |
| Country_Niger | -1.058867e+01 | 8.399098e-01 | -12.606909 | 3.338073e-35 | -1.223579e+01 | -8.941542e+00 |
| Country_Eritrea | -9.592419e+00 | 7.668567e-01 | -12.508751 | 1.059761e-34 | -1.109628e+01 | -8.088557e+00 |
| Country_Gabon | -7.835945e+00 | 6.636000e-01 | -11.808235 | 3.221754e-31 | -9.137313e+00 | -6.534577e+00 |
| Country_Sudan | -8.065745e+00 | 6.838239e-01 | -11.795060 | 3.731982e-31 | -9.406774e+00 | -6.724716e+00 |
| Country_Lao People's Democratic Republic | -8.208108e+00 | 7.050813e-01 | -11.641364 | 2.052262e-30 | -9.590824e+00 | -6.825392e+00 |
| Country_Senegal | -7.928504e+00 | 7.017575e-01 | -11.298068 | 8.615295e-29 | -9.304702e+00 | -6.552306e+00 |
| Country_Mauritania | -7.841409e+00 | 6.956110e-01 | -11.272693 | 1.131258e-28 | -9.205553e+00 | -6.477265e+00 |
| Country_Ethiopia | -8.897927e+00 | 7.916450e-01 | -11.239795 | 1.609065e-28 | -1.045040e+01 | -7.345454e+00 |
| Country_Madagascar | -7.629826e+00 | 6.989008e-01 | -10.916894 | 4.871133e-27 | -9.000421e+00 | -6.259230e+00 |
| Country_Kiribati | -6.561295e+00 | 6.186039e-01 | -10.606618 | 1.188487e-25 | -7.774423e+00 | -5.348168e+00 |
| Country_Timor-Leste | -7.116939e+00 | 7.140851e-01 | -9.966514 | 6.683709e-23 | -8.517312e+00 | -5.716566e+00 |
| Country_Turkmenistan | -6.679133e+00 | 6.839479e-01 | -9.765559 | 4.538208e-22 | -8.020405e+00 | -5.337861e+00 |
| Country_Myanmar | -6.931763e+00 | 7.198311e-01 | -9.629707 | 1.624277e-21 | -8.343404e+00 | -5.520121e+00 |
| Country_Cambodia | -6.552976e+00 | 6.999577e-01 | -9.361961 | 1.912867e-20 | -7.925644e+00 | -5.180308e+00 |
| Country_Yemen | -6.617634e+00 | 7.158515e-01 | -9.244424 | 5.536898e-20 | -8.021472e+00 | -5.213797e+00 |
| Country_Sao Tome and Principe | -5.921592e+00 | 6.502695e-01 | -9.106366 | 1.899853e-19 | -7.196818e+00 | -4.646366e+00 |
| Country_Mongolia | -5.410840e+00 | 5.955876e-01 | -9.084877 | 2.298316e-19 | -6.578830e+00 | -4.242849e+00 |
| Country_Tajikistan | -4.906333e+00 | 5.979946e-01 | -8.204643 | 3.944642e-16 | -6.079044e+00 | -3.733621e+00 |
| Country_Pakistan | -9.270009e+00 | 1.153261e+00 | -8.038083 | 1.493826e-15 | -1.153164e+01 | -7.008379e+00 |
| under-five deaths | -6.571412e-02 | 9.046744e-03 | -7.263842 | 5.244266e-13 | -8.345544e-02 | -4.797279e-02 |
| Country_Kazakhstan | -4.162719e+00 | 5.900547e-01 | -7.054803 | 2.324707e-12 | -5.319860e+00 | -3.005579e+00 |
| Country_Nigeria | -1.012007e+01 | 1.442564e+00 | -7.015336 | 3.065596e-12 | -1.294905e+01 | -7.291098e+00 |
| Country_Nepal | -4.951988e+00 | 7.063913e-01 | -7.010262 | 3.176268e-12 | -6.337273e+00 | -3.566703e+00 |
| Country_Guyana | -4.588058e+00 | 6.678301e-01 | -6.870097 | 8.381709e-12 | -5.897722e+00 | -3.278394e+00 |
| Country_Bhutan | -5.300396e+00 | 7.860718e-01 | -6.742890 | 1.990756e-11 | -6.841940e+00 | -3.758852e+00 |
| Adult Mortality | -3.694340e-03 | 5.511215e-04 | -6.703312 | 2.597701e-11 | -4.775129e-03 | -2.613550e-03 |
| Country_San Marino | -8.029090e-10 | 1.233071e-10 | -6.511455 | 9.246181e-11 | -1.044723e-09 | -5.610946e-10 |
| Country_Indonesia | -4.874471e+00 | 7.762641e-01 | -6.279397 | 4.103791e-10 | -6.396782e+00 | -3.352160e+00 |
| Country_Uzbekistan | -3.677017e+00 | 5.998464e-01 | -6.129931 | 1.043807e-09 | -4.853360e+00 | -2.500675e+00 |
| Country_Philippines | -3.769967e+00 | 6.801129e-01 | -5.543149 | 3.337141e-08 | -5.103719e+00 | -2.436216e+00 |
| Country_Solomon Islands | -3.663782e+00 | 6.927873e-01 | -5.288465 | 1.359100e-07 | -5.022388e+00 | -2.305175e+00 |
| Country_Fiji | -3.180234e+00 | 6.378811e-01 | -4.985623 | 6.672689e-07 | -4.431166e+00 | -1.929303e+00 |
| Country_Bolivia (Plurinational State of) | -2.847983e+00 | 6.299739e-01 | -4.520796 | 6.497113e-06 | -4.083408e+00 | -1.612558e+00 |
| Country_Russian Federation | -2.629292e+00 | 6.056501e-01 | -4.341273 | 1.482625e-05 | -3.817017e+00 | -1.441568e+00 |
| Alcohol | -1.109159e-01 | 2.870888e-02 | -3.863472 | 1.151116e-04 | -1.672161e-01 | -5.461572e-02 |
| Country_Bangladesh | -2.898584e+00 | 7.765957e-01 | -3.732423 | 1.945924e-04 | -4.421545e+00 | -1.375623e+00 |
| Country_Micronesia (Federated States of) | -2.258458e+00 | 6.503075e-01 | -3.472908 | 5.250809e-04 | -3.533758e+00 | -9.831573e-01 |
| Country_Democratic People's Republic of Korea | -1.975778e+00 | 5.928284e-01 | -3.332799 | 8.744110e-04 | -3.138358e+00 | -8.131981e-01 |
| Country_India | -9.164212e+00 | 2.856694e+00 | -3.207978 | 1.356466e-03 | -1.476640e+01 | -3.562027e+00 |
| Hepatitis B | -6.809741e-03 | 2.408427e-03 | -2.827463 | 4.735471e-03 | -1.153284e-02 | -2.086638e-03 |
| Country_Kyrgyzstan | -1.676051e+00 | 6.283511e-01 | -2.667380 | 7.702130e-03 | -2.908294e+00 | -4.438089e-01 |
| Country_Belize | -1.599300e+00 | 6.333224e-01 | -2.525254 | 1.163315e-02 | -2.841291e+00 | -3.573084e-01 |
| logGDP | -9.947509e-02 | 4.910909e-02 | -2.025594 | 4.293027e-02 | -1.957816e-01 | -3.168566e-03 |
| Country_Azerbaijan | -1.036844e+00 | 5.955634e-01 | -1.740947 | 8.183652e-02 | -2.204788e+00 | 1.310989e-01 |
| BMI | -6.160831e-03 | 3.631022e-03 | -1.696721 | 8.989482e-02 | -1.328153e-02 | 9.598678e-04 |
| Measles | -8.519964e-06 | 5.207132e-06 | -1.636211 | 1.019426e-01 | -1.873153e-05 | 1.691602e-06 |
| Country_Ukraine | -9.041591e-01 | 5.849994e-01 | -1.545573 | 1.223556e-01 | -2.051386e+00 | 2.430675e-01 |
| Population | -1.685098e-09 | 1.102843e-09 | -1.527959 | 1.266706e-01 | -3.847853e-09 | 4.776570e-10 |
| Country_Iraq | -9.245128e-01 | 6.235236e-01 | -1.482723 | 1.382952e-01 | -2.147288e+00 | 2.982625e-01 |
| Total expenditure | -3.698317e-02 | 2.695711e-02 | -1.371926 | 1.702302e-01 | -8.984804e-02 | 1.588170e-02 |
| Country_Suriname | -6.893467e-01 | 6.736866e-01 | -1.023245 | 3.063075e-01 | -2.010495e+00 | 6.318020e-01 |
| Income composition of resources | -5.059445e-01 | 5.503265e-01 | -0.919353 | 3.580145e-01 | -1.585175e+00 | 5.732861e-01 |
| Country_Dominica | -1.191247e+00 | 2.069758e+00 | -0.575549 | 5.649806e-01 | -5.250195e+00 | 2.867700e+00 |
| Country_Palau | -1.025232e+00 | 2.079517e+00 | -0.493014 | 6.220531e-01 | -5.103317e+00 | 3.052854e+00 |
| Country_Nauru | -9.229065e-01 | 2.091462e+00 | -0.441273 | 6.590597e-01 | -5.024417e+00 | 3.178604e+00 |
| Country_Monaco | -8.815249e-01 | 2.068923e+00 | -0.426079 | 6.700931e-01 | -4.938835e+00 | 3.175785e+00 |
| thinness 5-9 years | -1.334143e-02 | 3.386089e-02 | -0.394007 | 6.936150e-01 | -7.974512e-02 | 5.306225e-02 |
| Country_China | -4.095934e-01 | 1.086974e+00 | -0.376820 | 7.063449e-01 | -2.541229e+00 | 1.722042e+00 |
| Country_Cook Islands | -7.000490e-01 | 2.077959e+00 | -0.336893 | 7.362309e-01 | -4.775079e+00 | 3.374981e+00 |
| Country_Niue | -6.049927e-01 | 2.075671e+00 | -0.291468 | 7.707213e-01 | -4.675535e+00 | 3.465550e+00 |
| Country_Belarus | -1.801282e-01 | 6.971873e-01 | -0.258364 | 7.961508e-01 | -1.547364e+00 | 1.187107e+00 |
| Country_Vanuatu | -1.509672e-01 | 6.306616e-01 | -0.239379 | 8.108346e-01 | -1.387741e+00 | 1.085806e+00 |
| Country_Saint Kitts and Nevis | -1.618836e-01 | 2.080496e+00 | -0.077810 | 9.379864e-01 | -4.241888e+00 | 3.918120e+00 |
| Country_Egypt | -3.241900e-02 | 6.679046e-01 | -0.048538 | 9.612917e-01 | -1.342229e+00 | 1.277391e+00 |
| Country_Marshall Islands | -1.036425e-01 | 2.206165e+00 | -0.046979 | 9.625347e-01 | -4.430092e+00 | 4.222807e+00 |
| Country_Syrian Arab Republic | -1.121379e-02 | 6.185179e-01 | -0.018130 | 9.855368e-01 | -1.224173e+00 | 1.201745e+00 |
| thinness 1-19 years | 1.168155e-03 | 3.406010e-02 | 0.034297 | 9.726436e-01 | -6.562620e-02 | 6.796251e-02 |
| Country_El Salvador | 1.220767e-01 | 5.932056e-01 | 0.205792 | 8.369733e-01 | -1.041243e+00 | 1.285396e+00 |
| Country_Trinidad and Tobago | 1.778183e-01 | 6.293880e-01 | 0.282526 | 7.775677e-01 | -1.056458e+00 | 1.412094e+00 |
| Country_Morocco | 1.873879e-01 | 5.878484e-01 | 0.318769 | 7.499328e-01 | -9.654258e-01 | 1.340202e+00 |
| iHIV | 1.403563e-02 | 4.142533e-02 | 0.338818 | 7.347804e-01 | -6.720248e-02 | 9.527374e-02 |
| Country_Tonga | 3.967493e-01 | 6.619078e-01 | 0.599403 | 5.489678e-01 | -9.013002e-01 | 1.694799e+00 |
| Country_Republic of Moldova | 5.120735e-01 | 6.709034e-01 | 0.763260 | 4.453926e-01 | -8.036171e-01 | 1.827764e+00 |
| Country_Tuvalu | 2.592587e-08 | 2.248507e-08 | 1.153026 | 2.490285e-01 | -1.816900e-08 | 7.002074e-08 |
| Polio | 3.261042e-03 | 2.812878e-03 | 1.159326 | 2.464527e-01 | -2.255217e-03 | 8.777300e-03 |
| Country_Libya | 7.361366e-01 | 6.248871e-01 | 1.178031 | 2.389151e-01 | -4.893126e-01 | 1.961586e+00 |
| Country_Guatemala | 8.907405e-01 | 6.624187e-01 | 1.344679 | 1.788714e-01 | -4.083110e-01 | 2.189792e+00 |
| Country_Jordan | 1.117858e+00 | 6.942210e-01 | 1.610234 | 1.074942e-01 | -2.435598e-01 | 2.479277e+00 |
| Country_Brazil | 1.180940e+00 | 6.934523e-01 | 1.702986 | 8.871587e-02 | -1.789710e-01 | 2.540850e+00 |
| Country_Dominican Republic | 1.092110e+00 | 6.223024e-01 | 1.754951 | 7.941070e-02 | -1.282700e-01 | 2.312491e+00 |
| Country_Mauritius | 1.066178e+00 | 5.946133e-01 | 1.793061 | 7.310438e-02 | -9.990239e-02 | 2.232258e+00 |
| Country_Cabo Verde | 1.228555e+00 | 6.510965e-01 | 1.886902 | 5.930878e-02 | -4.829266e-02 | 2.505403e+00 |
| Country_Saudi Arabia | 1.203026e+00 | 6.073268e-01 | 1.980855 | 4.773536e-02 | 1.201412e-02 | 2.394039e+00 |
| Country_Seychelles | 1.139089e+00 | 5.688207e-01 | 2.002546 | 4.535204e-02 | 2.359039e-02 | 2.254588e+00 |
| Country_Grenada | 1.570422e+00 | 7.178004e-01 | 2.187826 | 2.879010e-02 | 1.627633e-01 | 2.978082e+00 |
| Schooling | 1.092271e-01 | 4.929293e-02 | 2.215878 | 2.680471e-02 | 1.256008e-02 | 2.058942e-01 |
| Country_Algeria | 1.466607e+00 | 6.378335e-01 | 2.299358 | 2.158052e-02 | 2.157692e-01 | 2.717445e+00 |
| Country_Sri Lanka | 1.709911e+00 | 6.701607e-01 | 2.551494 | 1.079522e-02 | 3.956767e-01 | 3.024145e+00 |
| Country_Thailand | 1.779859e+00 | 6.928353e-01 | 2.568949 | 1.026797e-02 | 4.211579e-01 | 3.138559e+00 |
| Country_Kuwait | 1.610600e+00 | 6.152717e-01 | 2.617705 | 8.914716e-03 | 4.040069e-01 | 2.817192e+00 |
| Country_Honduras | 1.704227e+00 | 6.237124e-01 | 2.732392 | 6.339303e-03 | 4.810812e-01 | 2.927372e+00 |
| Diphtheria | 8.486582e-03 | 2.948735e-03 | 2.878041 | 4.041215e-03 | 2.703897e-03 | 1.426927e-02 |
| Country_Armenia | 1.918190e+00 | 6.644430e-01 | 2.886913 | 3.929434e-03 | 6.151682e-01 | 3.221211e+00 |
| Country_Malaysia | 1.923192e+00 | 6.061451e-01 | 3.172825 | 1.531052e-03 | 7.344972e-01 | 3.111887e+00 |
| Country_Colombia | 1.869283e+00 | 5.786304e-01 | 3.230530 | 1.254349e-03 | 7.345461e-01 | 3.004019e+00 |
| Country_Paraguay | 1.977906e+00 | 5.850927e-01 | 3.380499 | 7.365351e-04 | 8.304960e-01 | 3.125315e+00 |
| Country_Iran (Islamic Republic of) | 2.532728e+00 | 7.280429e-01 | 3.478818 | 5.137009e-04 | 1.104983e+00 | 3.960474e+00 |
| Country_Peru | 2.081224e+00 | 5.878562e-01 | 3.540363 | 4.081111e-04 | 9.283952e-01 | 3.234053e+00 |
| Country_Tunisia | 2.200142e+00 | 6.191602e-01 | 3.553429 | 3.884787e-04 | 9.859233e-01 | 3.414360e+00 |
| Country_Turkey | 2.225846e+00 | 5.908441e-01 | 3.767230 | 1.695281e-04 | 1.067157e+00 | 3.384534e+00 |
| Country_Lebanon | 2.314988e+00 | 6.099977e-01 | 3.795077 | 1.516985e-04 | 1.118738e+00 | 3.511238e+00 |
| Country_Georgia | 2.325753e+00 | 5.933143e-01 | 3.919934 | 9.135753e-05 | 1.162220e+00 | 3.489286e+00 |
| Country_Samoa | 2.665064e+00 | 6.541280e-01 | 4.074224 | 4.785119e-05 | 1.382271e+00 | 3.947857e+00 |
| logExpend | 1.368168e-01 | 3.310361e-02 | 4.132987 | 3.718740e-05 | 7.189819e-02 | 2.017354e-01 |
| Country_Montenegro | 2.661788e+00 | 6.418017e-01 | 4.147368 | 3.494506e-05 | 1.403168e+00 | 3.920408e+00 |
| Country_Nicaragua | 2.620535e+00 | 6.315138e-01 | 4.149608 | 3.460759e-05 | 1.382090e+00 | 3.858980e+00 |
| Country_The former Yugoslav republic of Macedonia | 3.018132e+00 | 6.802430e-01 | 4.436844 | 9.591920e-06 | 1.684126e+00 | 4.352138e+00 |
| Country_Serbia | 2.889230e+00 | 6.315070e-01 | 4.575135 | 5.031413e-06 | 1.650798e+00 | 4.127661e+00 |
| Country_Oman | 2.847179e+00 | 6.038693e-01 | 4.714892 | 2.574149e-06 | 1.662947e+00 | 4.031410e+00 |
| Country_Venezuela (Bolivarian Republic of) | 3.193301e+00 | 6.566846e-01 | 4.862763 | 1.241834e-06 | 1.905495e+00 | 4.481108e+00 |
| Country_Barbados | 3.298628e+00 | 6.569644e-01 | 5.021016 | 5.564854e-07 | 2.010273e+00 | 4.586984e+00 |
| Country_Maldives | 3.954402e+00 | 7.761869e-01 | 5.094651 | 3.800035e-07 | 2.432243e+00 | 5.476561e+00 |
| Country_Jamaica | 3.090735e+00 | 6.025343e-01 | 5.129558 | 3.165830e-07 | 1.909121e+00 | 4.272349e+00 |
| Country_Argentina | 3.522508e+00 | 6.554501e-01 | 5.374181 | 8.529424e-08 | 2.237122e+00 | 4.807893e+00 |
| Country_Viet Nam | 3.633903e+00 | 6.619204e-01 | 5.489940 | 4.497364e-08 | 2.335829e+00 | 4.931978e+00 |
| Country_Estonia | 3.866735e+00 | 7.027873e-01 | 5.501999 | 4.204258e-08 | 2.488518e+00 | 5.244952e+00 |
| Country_Saint Vincent and the Grenadines | 3.546004e+00 | 6.322555e-01 | 5.608499 | 2.304945e-08 | 2.306105e+00 | 4.785904e+00 |
| Country_Albania | 3.527683e+00 | 6.070984e-01 | 5.810726 | 7.151623e-09 | 2.337118e+00 | 4.718247e+00 |
| Country_Saint Lucia | 3.699044e+00 | 6.330338e-01 | 5.843359 | 5.899884e-09 | 2.457618e+00 | 4.940470e+00 |
| Country_Ecuador | 3.275261e+00 | 5.603980e-01 | 5.844526 | 5.859342e-09 | 2.176279e+00 | 4.374242e+00 |
| Country_Bahrain | 3.896774e+00 | 6.644473e-01 | 5.864684 | 5.200030e-09 | 2.593744e+00 | 5.199804e+00 |
| Country_United Arab Emirates | 3.702151e+00 | 6.243671e-01 | 5.929446 | 3.534649e-09 | 2.477722e+00 | 4.926581e+00 |
| Country_Bahamas | 4.168751e+00 | 6.880588e-01 | 6.058714 | 1.616754e-09 | 2.819418e+00 | 5.518085e+00 |
| Country_Antigua and Barbuda | 4.402229e+00 | 6.664384e-01 | 6.605606 | 4.979918e-11 | 3.095295e+00 | 5.709164e+00 |
| Country_Mexico | 4.116571e+00 | 6.010069e-01 | 6.849457 | 9.654499e-12 | 2.937952e+00 | 5.295189e+00 |
| Country_Bosnia and Herzegovina | 4.633266e+00 | 6.700182e-01 | 6.915134 | 6.148647e-12 | 3.319311e+00 | 5.947221e+00 |
| infant deaths | 9.060206e-02 | 1.306114e-02 | 6.936766 | 5.294981e-12 | 6.498821e-02 | 1.162159e-01 |
| Country_Brunei Darussalam | 4.179517e+00 | 5.983163e-01 | 6.985463 | 3.776118e-12 | 3.006175e+00 | 5.352859e+00 |
| Country_Qatar | 5.105357e+00 | 6.542522e-01 | 7.803348 | 9.351701e-15 | 3.822321e+00 | 6.388394e+00 |
| Country_Uruguay | 4.940473e+00 | 6.139174e-01 | 8.047456 | 1.386888e-15 | 3.736537e+00 | 6.144410e+00 |
| Country_Panama | 5.375253e+00 | 5.747970e-01 | 9.351567 | 2.102387e-20 | 4.248034e+00 | 6.502472e+00 |
| Country_Cuba | 6.024380e+00 | 5.841622e-01 | 10.312854 | 2.269453e-24 | 4.878795e+00 | 7.169964e+00 |
| Country_Chile | 7.631849e+00 | 6.605734e-01 | 11.553371 | 5.398430e-30 | 6.336417e+00 | 8.927282e+00 |
| Country_Costa Rica | 6.982737e+00 | 5.697886e-01 | 12.254960 | 2.027169e-33 | 5.865339e+00 | 8.100134e+00 |
| Country_Republic of Korea | 9.750169e+00 | 7.194085e-01 | 13.553035 | 3.300087e-40 | 8.339356e+00 | 1.116098e+01 |
| Country_Greece | 1.026885e+01 | 7.291159e-01 | 14.083974 | 3.785762e-43 | 8.839000e+00 | 1.169870e+01 |
| Country_Canada | 1.002031e+01 | 7.083147e-01 | 14.146690 | 1.677612e-43 | 8.631251e+00 | 1.140937e+01 |
| Country_Finland | 9.252164e+00 | 6.449588e-01 | 14.345356 | 1.248971e-44 | 7.987352e+00 | 1.051698e+01 |
| Country_France | 1.074428e+01 | 7.392236e-01 | 14.534550 | 1.023979e-45 | 9.294611e+00 | 1.219395e+01 |
| Country_Israel | 9.392544e+00 | 6.020722e-01 | 15.600361 | 4.764306e-52 | 8.211836e+00 | 1.057325e+01 |
| Year | 3.207764e-01 | 1.329531e-02 | 24.127039 | 5.805972e-114 | 2.947033e-01 | 3.468494e-01 |
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