Yosuke Tsuchiya - Fab Futures - Data Science

Home About

Simple Statistical Analysis Example

Here is the basic statistic library sample code using SDGs data.

OK, let's start from reading data

import requests

url = 'https://www.kaggle.com/api/v1/datasets/download/sazidthe1/sustainable-development-report'
r = requests.get(url)

with open('../datasets/sdgsdata.zip','wb') as fd:
    fd.write(r.content)

import shutil
shutil.unpack_archive('../datasets/sdgsdata.zip','./datasets/')
path_2023 = "../datasets/sustainable_development_report_2023.csv"
path_index = "../datasets/sdg_index_2000-2022.csv"

# Read sutainable developement report 2023 data

import pandas as pd
df_sdg2023 = pd.read_csv(path_2023)
df_sdg2023

	country_code	country	region	overall_score	goal_1_score	goal_2_score	goal_3_score	goal_4_score	goal_5_score	goal_6_score	...	goal_8_score	goal_9_score	goal_10_score	goal_11_score	goal_12_score	goal_13_score	goal_14_score	goal_15_score	goal_16_score	goal_17_score
0	FIN	Finland	OECD	86.760595	99.5750	60.886750	95.386385	97.169333	92.11125	94.3276	...	86.789000	95.995714	98.4685	91.233750	60.059571	68.793667	87.928000	85.0700	92.521091	75.601000
1	SWE	Sweden	OECD	85.981397	98.8885	63.074125	96.904000	99.761667	91.44025	95.0576	...	84.966429	97.586286	94.9650	90.389250	56.830571	70.031000	69.348667	80.1882	88.508455	85.770250
2	DNK	Denmark	OECD	85.683637	99.2155	71.025250	95.398500	99.339667	86.99800	90.7316	...	87.562429	96.984857	98.1560	93.038500	44.571714	60.780667	76.303333	92.7924	93.844909	82.148000
3	DEU	Germany	OECD	83.358447	99.5105	72.366000	93.039357	97.162667	81.92025	88.4434	...	86.967286	95.788429	88.1470	90.096500	55.412857	64.002000	73.996000	79.2318	89.457545	84.390250
4	AUT	Austria	OECD	82.280189	99.4510	73.067500	92.468000	97.914333	84.57925	92.1636	...	83.274143	96.982143	94.6345	92.473667	49.623286	57.332000	NaN	73.5836	87.911455	71.130250
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
161	SOM	Somalia	Sub-Saharan Africa	48.027231	11.2740	27.306833	17.860923	55.639000	25.86100	49.3006	...	55.669500	5.599857	73.8030	69.417667	94.129000	99.925333	50.251200	53.3714	40.012200	43.725667
162	YEM	Yemen, Rep.	MENA	46.846980	4.5525	28.769714	44.467429	41.766750	13.05475	36.2314	...	53.237800	14.223714	66.2925	52.952750	95.959143	98.667000	74.936667	48.3705	35.905714	50.094333
163	TCD	Chad	Sub-Saharan Africa	45.342321	25.4270	38.534714	27.061071	13.307750	30.83550	42.4036	...	64.424333	9.631571	63.0405	32.822250	90.994167	99.079000	NaN	76.1944	29.438000	52.594000
164	CAF	Central African Republic	Sub-Saharan Africa	40.395839	3.1820	36.468000	12.952714	19.305750	34.20075	40.4204	...	53.382333	7.064714	9.5775	21.898667	94.462143	99.463333	NaN	89.7172	42.332667	36.516750
165	SSD	South Sudan	Sub-Saharan Africa	38.676086	0.0000	19.805800	23.861714	1.232250	55.98875	41.0406	...	50.917000	1.654833	26.6195	13.826250	90.960000	99.408000	NaN	74.6870	38.141167	41.622000

166 rows × 21 columns

# Drop 'region' column
df_sdg2023 = df_sdg2023.drop(columns=['region'])

# rename 'overall_score' to 'sdg_index_score'
sdgsdata2023 = df_sdg2023.rename(columns={'overall_score':'sdg_index_score'})

# add 'year' column and insert same value '2023'
sdgsdata2023['year'] = 2023

sdgsdata2023

	country_code	country	sdg_index_score	goal_1_score	goal_2_score	goal_3_score	goal_4_score	goal_5_score	goal_6_score	goal_7_score	...	goal_9_score	goal_10_score	goal_11_score	goal_12_score	goal_13_score	goal_14_score	goal_15_score	goal_16_score	goal_17_score	year
0	FIN	Finland	86.760595	99.5750	60.886750	95.386385	97.169333	92.11125	94.3276	93.01350	...	95.995714	98.4685	91.233750	60.059571	68.793667	87.928000	85.0700	92.521091	75.601000	2023
1	SWE	Sweden	85.981397	98.8885	63.074125	96.904000	99.761667	91.44025	95.0576	97.97350	...	97.586286	94.9650	90.389250	56.830571	70.031000	69.348667	80.1882	88.508455	85.770250	2023
2	DNK	Denmark	85.683637	99.2155	71.025250	95.398500	99.339667	86.99800	90.7316	87.73050	...	96.984857	98.1560	93.038500	44.571714	60.780667	76.303333	92.7924	93.844909	82.148000	2023
3	DEU	Germany	83.358447	99.5105	72.366000	93.039357	97.162667	81.92025	88.4434	77.16175	...	95.788429	88.1470	90.096500	55.412857	64.002000	73.996000	79.2318	89.457545	84.390250	2023
4	AUT	Austria	82.280189	99.4510	73.067500	92.468000	97.914333	84.57925	92.1636	86.03650	...	96.982143	94.6345	92.473667	49.623286	57.332000	NaN	73.5836	87.911455	71.130250	2023
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
161	SOM	Somalia	48.027231	11.2740	27.306833	17.860923	55.639000	25.86100	49.3006	43.31575	...	5.599857	73.8030	69.417667	94.129000	99.925333	50.251200	53.3714	40.012200	43.725667	2023
162	YEM	Yemen, Rep.	46.846980	4.5525	28.769714	44.467429	41.766750	13.05475	36.2314	36.91600	...	14.223714	66.2925	52.952750	95.959143	98.667000	74.936667	48.3705	35.905714	50.094333	2023
163	TCD	Chad	45.342321	25.4270	38.534714	27.061071	13.307750	30.83550	42.4036	8.69700	...	9.631571	63.0405	32.822250	90.994167	99.079000	NaN	76.1944	29.438000	52.594000	2023
164	CAF	Central African Republic	40.395839	3.1820	36.468000	12.952714	19.305750	34.20075	40.4204	19.44975	...	7.064714	9.5775	21.898667	94.462143	99.463333	NaN	89.7172	42.332667	36.516750	2023
165	SSD	South Sudan	38.676086	0.0000	19.805800	23.861714	1.232250	55.98875	41.0406	11.39400	...	1.654833	26.6195	13.826250	90.960000	99.408000	NaN	74.6870	38.141167	41.622000	2023

166 rows × 21 columns

# Read SDG index data from 2000-2022
df_index = pd.read_csv(path_index)
df_index

	country_code	country	year	sdg_index_score	goal_1_score	goal_2_score	goal_3_score	goal_4_score	goal_5_score	goal_6_score	...	goal_8_score	goal_9_score	goal_10_score	goal_11_score	goal_12_score	goal_13_score	goal_14_score	goal_15_score	goal_16_score	goal_17_score
0	AFG	Afghanistan	2000	36.0	28.8	27.3	19.2	1.6	20.8	32.4	...	38.5	5.2	0.0	25.8	94.7	99.4	0.0	51.9	39.2	34.2
1	AFG	Afghanistan	2001	36.3	28.8	30.6	19.4	1.6	20.8	32.4	...	38.5	5.2	0.0	25.8	94.5	99.4	0.0	51.9	39.2	34.2
2	AFG	Afghanistan	2002	36.3	28.8	30.7	19.7	1.6	20.8	32.7	...	38.4	5.2	0.0	26.1	94.1	99.4	0.0	51.8	39.2	34.2
3	AFG	Afghanistan	2003	36.7	28.8	32.5	19.9	1.6	20.8	33.0	...	38.4	5.2	0.0	26.5	94.4	99.4	0.0	51.8	39.2	34.2
4	AFG	Afghanistan	2004	37.1	28.8	32.1	21.1	1.6	20.8	33.3	...	38.5	5.2	0.0	26.8	94.8	99.4	0.0	51.8	39.2	34.2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4135	ZWE	Zimbabwe	2018	55.9	26.4	46.6	39.8	57.9	76.7	51.6	...	63.8	23.5	32.4	79.2	94.6	94.1	0.0	76.4	45.5	45.6
4136	ZWE	Zimbabwe	2019	53.7	21.4	46.0	40.6	60.6	77.0	51.3	...	62.8	23.8	24.0	58.6	94.6	94.2	0.0	76.4	44.0	44.5
4137	ZWE	Zimbabwe	2020	54.3	20.1	47.2	41.5	62.0	77.0	51.1	...	62.9	25.4	24.0	60.0	94.6	94.3	0.0	76.4	44.6	46.0
4138	ZWE	Zimbabwe	2021	54.8	20.8	48.4	41.9	62.5	77.1	51.1	...	63.1	27.0	24.0	61.2	94.5	94.3	0.0	77.9	43.1	48.1
4139	ZWE	Zimbabwe	2022	55.6	21.5	48.5	41.9	62.5	77.2	51.1	...	63.2	27.0	24.0	65.7	94.5	94.3	0.0	77.9	40.5	47.8

4140 rows × 21 columns

# reindex sdgsdaga2023 with df_index
sdgsdata2023.reindex(df_index.index)

# Integrate both dataframe
sdgs_integrated = pd.concat([df_index,sdgsdata2023])

# fillin NaN as 0
sdgs_integrated = sdgs_integrated.fillna(0)

# Check the data with filtering the country name
sdgs_integrated.query('country == "Afghanistan"')

	country_code	country	year	sdg_index_score	goal_1_score	goal_2_score	goal_3_score	goal_4_score	goal_5_score	goal_6_score	...	goal_8_score	goal_9_score	goal_10_score	goal_11_score	goal_12_score	goal_13_score	goal_14_score	goal_15_score	goal_16_score	goal_17_score
0	AFG	Afghanistan	2000	36.000000	28.8000	27.300000	19.200000	1.600	20.80000	32.400	...	38.500	5.200000	0.0	25.800	94.700000	99.400000	0.0	51.9000	39.200000	34.200
1	AFG	Afghanistan	2001	36.300000	28.8000	30.600000	19.400000	1.600	20.80000	32.400	...	38.500	5.200000	0.0	25.800	94.500000	99.400000	0.0	51.9000	39.200000	34.200
2	AFG	Afghanistan	2002	36.300000	28.8000	30.700000	19.700000	1.600	20.80000	32.700	...	38.400	5.200000	0.0	26.100	94.100000	99.400000	0.0	51.8000	39.200000	34.200
3	AFG	Afghanistan	2003	36.700000	28.8000	32.500000	19.900000	1.600	20.80000	33.000	...	38.400	5.200000	0.0	26.500	94.400000	99.400000	0.0	51.8000	39.200000	34.200
4	AFG	Afghanistan	2004	37.100000	28.8000	32.100000	21.100000	1.600	20.80000	33.300	...	38.500	5.200000	0.0	26.800	94.800000	99.400000	0.0	51.8000	39.200000	34.200
5	AFG	Afghanistan	2005	37.500000	28.8000	35.900000	22.600000	1.600	20.80000	33.600	...	38.400	5.200000	0.0	26.900	95.700000	99.400000	0.0	51.8000	39.200000	34.200
6	AFG	Afghanistan	2006	37.600000	28.8000	36.500000	22.700000	1.600	20.80000	33.900	...	38.500	5.200000	0.0	27.300	95.900000	99.300000	0.0	51.8000	39.200000	34.700
7	AFG	Afghanistan	2007	38.000000	28.8000	39.500000	24.400000	1.600	21.00000	34.600	...	38.500	5.200000	0.0	27.600	96.200000	99.300000	0.0	51.8000	39.200000	34.800
8	AFG	Afghanistan	2008	37.300000	28.8000	37.800000	25.900000	1.600	21.00000	35.700	...	38.500	5.200000	0.0	28.000	96.300000	99.200000	0.0	51.8000	39.200000	36.000
9	AFG	Afghanistan	2009	38.300000	28.8000	43.000000	28.100000	1.600	20.80000	36.900	...	38.500	5.400000	0.0	30.300	96.600000	99.100000	0.0	53.0000	39.900000	36.700
10	AFG	Afghanistan	2010	38.800000	28.8000	43.200000	31.400000	1.600	21.00000	38.000	...	38.500	5.500000	0.0	29.900	96.300000	99.000000	0.0	53.0000	43.800000	36.600
11	AFG	Afghanistan	2011	38.400000	27.9000	42.000000	30.600000	1.600	21.00000	39.200	...	38.500	5.600000	0.0	25.900	96.000000	98.800000	0.0	53.0000	43.000000	36.900
12	AFG	Afghanistan	2012	39.700000	30.5000	44.300000	30.400000	1.600	21.00000	40.400	...	38.500	6.500000	0.0	31.400	96.100000	98.900000	0.0	53.0000	43.500000	35.300
13	AFG	Afghanistan	2013	39.700000	31.0000	44.200000	29.600000	1.600	21.30000	41.600	...	38.500	6.600000	0.0	28.900	96.000000	99.000000	0.0	53.0000	44.300000	35.600
14	AFG	Afghanistan	2014	40.800000	30.7000	44.300000	30.900000	1.600	21.80000	42.900	...	38.700	5.800000	0.0	33.600	96.300000	99.100000	0.0	53.0000	45.300000	36.000
15	AFG	Afghanistan	2015	41.600000	30.1000	45.000000	33.400000	17.500	22.40000	44.100	...	39.100	6.400000	0.0	31.300	96.600000	99.100000	0.0	53.0000	44.300000	35.500
16	AFG	Afghanistan	2016	44.700000	29.8000	44.300000	34.400000	37.400	24.80000	45.400	...	38.500	8.000000	0.0	36.000	96.500000	99.200000	0.0	56.5000	45.700000	38.200
17	AFG	Afghanistan	2017	45.600000	29.6000	43.500000	35.100000	38.900	27.10000	46.600	...	39.900	8.800000	0.0	33.600	96.600000	99.100000	0.0	60.9000	45.500000	40.100
18	AFG	Afghanistan	2018	46.300000	29.1000	47.800000	36.900000	40.200	29.00000	47.900	...	40.800	9.500000	0.0	32.800	96.700000	99.000000	0.0	60.9000	45.600000	42.100
19	AFG	Afghanistan	2019	46.400000	29.4000	47.000000	36.500000	42.000	30.80000	49.200	...	40.600	9.700000	0.0	29.800	96.700000	99.000000	0.0	60.9000	46.100000	42.000
20	AFG	Afghanistan	2020	47.300000	27.9000	46.000000	37.600000	42.000	30.20000	50.400	...	41.100	9.800000	0.0	30.200	96.700000	99.000000	0.0	73.2000	45.300000	44.100
21	AFG	Afghanistan	2021	46.300000	20.0000	46.700000	37.500000	34.400	32.90000	50.400	...	39.500	9.700000	0.0	29.400	96.700000	99.000000	0.0	73.2000	44.600000	45.300
22	AFG	Afghanistan	2022	49.000000	12.4000	46.900000	37.500000	34.400	32.90000	50.400	...	39.500	9.700000	0.0	24.500	96.700000	99.000000	0.0	73.2000	44.800000	45.300
157	AFG	Afghanistan	2023	49.005601	12.4105	46.915143	37.486714	34.366	32.92625	50.426	...	39.535	9.724571	0.0	24.494	96.679857	99.034333	0.0	73.2382	44.796182	45.289

24 rows × 21 columns

import matplotlib.pyplot as plt

goal_def = [
    {'color':'#0000AA','description':'SDGs Total Index'} , # total index
    {'color':'#E5233D','description':'No Poverty'},  # Goal 1
    {'color':'#DDA73A','description':'Zero Hunger'}, # Goal 2
    {'color':'#4CA146','description':'Good Health and Well-being'},  # Goal 3
    {'color':'#C5192D','description':'Quality Education'},  # Goal 4
    {'color':'#FF3A21','description':'Gender Equality'},  # Goal 5
    {'color':'#26BDE2','description':'Clean Water and sanitation'}, # Goal 6
    {'color':'#FCC30B','description':'Affordable and Clean Energy'}, # Goal 7
    {'color':'#A21942','description':'Decide Work And Economic Growth'},  # Goal 8
    {'color':'#FD6925','description':'Industry, Innovation AND Infrastructure'}, # Goal 9
    {'color':'#DD1367','description':'Reduce Inequality'}, # Goal 10
    {'color':'#FD9D24','description':'Sustainable City and Community'},# Goal 11
    {'color':'#BF8B2E','description':'Responsible Comsumption and Production'}, # Goal 12
    {'color':'#3F7E44','description':'Climate Action'},  # Goal 13
    {'color':'#0A97D9','description':'Life Below Water'}, # Goal 14
    {'color':'#56C02B','description':'Life On Land'},  # Goal 15
    {'color':'#00689D','description':'Peace, Justice and Strong Institution'},  # Goal 16
    {'color':'#19486A','description':'Partnership for the Goal'}  # Goal 17
]

columns = sdgs_integrated.columns
sdgs_labels = columns.to_list()
sdgs_labels.remove('country_code')
sdgs_labels.remove('year')
sdgs_labels.remove('country')

sdgs_def = []
for i,value in enumerate(sdgs_labels):

    sdgs_def.append({'label':value,'color':goal_def[i]['color'],'description':goal_def[i]['description']})

sdgs_def

[{'label': 'sdg_index_score',
  'color': '#0000AA',
  'description': 'SDGs Total Index'},
 {'label': 'goal_1_score', 'color': '#E5233D', 'description': 'No Poverty'},
 {'label': 'goal_2_score', 'color': '#DDA73A', 'description': 'Zero Hunger'},
 {'label': 'goal_3_score',
  'color': '#4CA146',
  'description': 'Good Health and Well-being'},
 {'label': 'goal_4_score',
  'color': '#C5192D',
  'description': 'Quality Education'},
 {'label': 'goal_5_score',
  'color': '#FF3A21',
  'description': 'Gender Equality'},
 {'label': 'goal_6_score',
  'color': '#26BDE2',
  'description': 'Clean Water and sanitation'},
 {'label': 'goal_7_score',
  'color': '#FCC30B',
  'description': 'Affordable and Clean Energy'},
 {'label': 'goal_8_score',
  'color': '#A21942',
  'description': 'Decide Work And Economic Growth'},
 {'label': 'goal_9_score',
  'color': '#FD6925',
  'description': 'Industry, Innovation AND Infrastructure'},
 {'label': 'goal_10_score',
  'color': '#DD1367',
  'description': 'Reduce Inequality'},
 {'label': 'goal_11_score',
  'color': '#FD9D24',
  'description': 'Sustainable City and Community'},
 {'label': 'goal_12_score',
  'color': '#BF8B2E',
  'description': 'Responsible Comsumption and Production'},
 {'label': 'goal_13_score',
  'color': '#3F7E44',
  'description': 'Climate Action'},
 {'label': 'goal_14_score',
  'color': '#0A97D9',
  'description': 'Life Below Water'},
 {'label': 'goal_15_score', 'color': '#56C02B', 'description': 'Life On Land'},
 {'label': 'goal_16_score',
  'color': '#00689D',
  'description': 'Peace, Justice and Strong Institution'},
 {'label': 'goal_17_score',
  'color': '#19486A',
  'description': 'Partnership for the Goal'}]

statdf = sdgs_integrated
goal_cols = [f"goal_{i}_score" for i in range(1,18)]
goal_cols

['goal_1_score',
 'goal_2_score',
 'goal_3_score',
 'goal_4_score',
 'goal_5_score',
 'goal_6_score',
 'goal_7_score',
 'goal_8_score',
 'goal_9_score',
 'goal_10_score',
 'goal_11_score',
 'goal_12_score',
 'goal_13_score',
 'goal_14_score',
 'goal_15_score',
 'goal_16_score',
 'goal_17_score']

OK, let's start from linear regression with OLS(Ordinary Least Square) with setting "sdg_index_score" as dependency variable.

This time, I use the library of statemodels

import statsmodels.api as sm
from statsmodels.stats.outliers_influence import variance_inflation_factor

Then, set goal_1_index ~ goal_17_index as explanatory variables "X", and set sdg_goal_index as response variable "y"

X = statdf[goal_cols].copy()
y = statdf["sdg_index_score"]

X.shape,y.shape

((4306, 17), (4306,))

OK, use OLS in statsmodel library and get the result.

X_const = sm.add_constant(X)

model = sm.OLS(y,X_const).fit()

print(model.summary())

                            OLS Regression Results                            
==============================================================================
Dep. Variable:        sdg_index_score   R-squared:                       0.989
Model:                            OLS   Adj. R-squared:                  0.989
Method:                 Least Squares   F-statistic:                 2.305e+04
Date:                Sun, 30 Nov 2025   Prob (F-statistic):               0.00
Time:                        05:05:59   Log-Likelihood:                -6604.4
No. Observations:                4306   AIC:                         1.324e+04
Df Residuals:                    4288   BIC:                         1.336e+04
Df Model:                          17                                         
Covariance Type:            nonrobust                                         
=================================================================================
                    coef    std err          t      P>|t|      [0.025      0.975]
---------------------------------------------------------------------------------
const             4.4003      0.316     13.905      0.000       3.780       5.021
goal_1_score      0.0136      0.001     18.516      0.000       0.012       0.015
goal_2_score      0.0774      0.002     34.007      0.000       0.073       0.082
goal_3_score      0.1071      0.002     44.693      0.000       0.102       0.112
goal_4_score      0.0691      0.001     49.720      0.000       0.066       0.072
goal_5_score      0.0396      0.002     24.283      0.000       0.036       0.043
goal_6_score      0.0555      0.002     24.770      0.000       0.051       0.060
goal_7_score      0.0809      0.002     45.247      0.000       0.077       0.084
goal_8_score      0.0535      0.003     19.538      0.000       0.048       0.059
goal_9_score      0.0393      0.001     26.939      0.000       0.036       0.042
goal_10_score     0.0462      0.001     66.507      0.000       0.045       0.048
goal_11_score     0.0574      0.002     29.641      0.000       0.054       0.061
goal_12_score     0.0627      0.003     22.134      0.000       0.057       0.068
goal_13_score     0.0298      0.002     18.926      0.000       0.027       0.033
goal_14_score     0.0071      0.001     10.886      0.000       0.006       0.008
goal_15_score     0.0711      0.001     49.270      0.000       0.068       0.074
goal_16_score     0.0637      0.002     28.688      0.000       0.059       0.068
goal_17_score     0.0677      0.002     40.144      0.000       0.064       0.071
==============================================================================
Omnibus:                      442.196   Durbin-Watson:                   0.204
Prob(Omnibus):                  0.000   Jarque-Bera (JB):              689.748
Skew:                           0.752   Prob(JB):                    1.67e-150
Kurtosis:                       4.257   Cond. No.                     4.88e+03
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 4.88e+03. This might indicate that there are
strong multicollinearity or other numerical problems.

All goal scores p-value is p<0.05, so it has a statistically significant effect.

Looking at the “coef” values, it suggests that Goal 3 has the highest contribution (an increase in Goal 3's score leads to the largest increase in the overall SDG Index score). Goal 12 comes next, followed by Goal 7.

Also, checked Variance Inflation Factor (VIF).

import numpy as np
X_vif = X_const.values
vif_list = []

for i in range(X_vif.shape[1]):
    vif = variance_inflation_factor(X_vif,i)
    vif_list.append(vif)

vif_df = pd.DataFrame({
    "feature": X_const.columns,
    "VIF": vif_list
})

vif_df

	feature	VIF
0	const	341.326363
1	goal_1_score	2.394855
2	goal_2_score	2.178459
3	goal_3_score	9.727456
4	goal_4_score	4.602000
5	goal_5_score	2.716097
6	goal_6_score	4.020126
7	goal_7_score	5.013481
8	goal_8_score	2.571580
9	goal_9_score	4.912090
10	goal_10_score	1.612300
11	goal_11_score	4.214480
12	goal_12_score	7.622557
13	goal_13_score	3.843774
14	goal_14_score	1.156706
15	goal_15_score	1.283671
16	goal_16_score	3.579412
17	goal_17_score	1.651957

Goal 3 is also high VIF value here....

coef = model.params.drop("const")
coef_sorted = coef.sort_values(ascending=False)
coef_sorted

goal_3_score     0.107058
goal_7_score     0.080873
goal_2_score     0.077405
goal_15_score    0.071142
goal_4_score     0.069079
goal_17_score    0.067698
goal_16_score    0.063665
goal_12_score    0.062666
goal_11_score    0.057354
goal_6_score     0.055526
goal_8_score     0.053488
goal_10_score    0.046224
goal_5_score     0.039615
goal_9_score     0.039253
goal_13_score    0.029791
goal_1_score     0.013587
goal_14_score    0.007143
dtype: float64

Moreover... checked the correlation with Pearson correlation coefficient,

from scipy.stats import pearsonr

pearsonr_results = {}

for col in goal_cols:
    rho,pval = pearsonr(statdf[col],statdf["sdg_index_score"])
    pearsonr_results[col] = {"rho": rho, "p-value": pval}

pr_df = pd.DataFrame(pearsonr_results).T
pr_df.sort_values("rho",ascending=False)
pr_df

	rho	p-value
goal_1_score	0.720406	0.000000e+00
goal_2_score	0.680498	0.000000e+00
goal_3_score	0.915858	0.000000e+00
goal_4_score	0.862357	0.000000e+00
goal_5_score	0.713507	0.000000e+00
goal_6_score	0.862810	0.000000e+00
goal_7_score	0.877140	0.000000e+00
goal_8_score	0.734806	0.000000e+00
goal_9_score	0.816256	0.000000e+00
goal_10_score	0.441650	4.452731e-205
goal_11_score	0.835802	0.000000e+00
goal_12_score	-0.693377	0.000000e+00
goal_13_score	-0.485065	4.859978e-253
goal_14_score	0.151990	1.136253e-23
goal_15_score	0.252971	7.443630e-64
goal_16_score	0.761106	0.000000e+00
goal_17_score	0.603774	0.000000e+00

I also checked the correlation with Spearman's rank correlation coefficient

from scipy.stats import spearmanr

spearman_results = {}

for col in goal_cols:
    rho,pval = spearmanr(statdf[col],statdf["sdg_index_score"])
    spearman_results[col] = {"rho": rho, "p-value": pval}

sp_df = pd.DataFrame(spearman_results).T
sp_df.sort_values("rho",ascending=False)
sp_df

	rho	p-value
goal_1_score	0.766965	0.000000e+00
goal_2_score	0.669205	0.000000e+00
goal_3_score	0.902713	0.000000e+00
goal_4_score	0.853403	0.000000e+00
goal_5_score	0.701933	0.000000e+00
goal_6_score	0.873589	0.000000e+00
goal_7_score	0.862647	0.000000e+00
goal_8_score	0.743441	0.000000e+00
goal_9_score	0.859014	0.000000e+00
goal_10_score	0.493456	3.889951e-263
goal_11_score	0.820494	0.000000e+00
goal_12_score	-0.776105	0.000000e+00
goal_13_score	-0.736678	0.000000e+00
goal_14_score	0.102578	1.509305e-11
goal_15_score	0.260884	6.161587e-68
goal_16_score	0.763935	0.000000e+00
goal_17_score	0.618478	0.000000e+00

Then, visualized above results as follow.

Here is the bar chart of Effect of each SDG Goal on SDG index from the result of OLS Coefficients).

coef_sorted = model.params.drop("const").sort_values()

plt.figure(figsize=(10,6))
coef_sorted.plot(kind="barh")
plt.title("Effect of Each SDG Goal on SDG Index (OLS Coefficients)")
plt.xlabel("Coefficient")
plt.grid(axis="x")
plt.show()

Here is the bar chart of VIF result

plt.figure(figsize=(10,5))
plt.bar(vif_df["feature"],vif_df["VIF"])
plt.xticks(rotation=90)
plt.ylabel("VIF")
plt.title("Variance Inflation Factor (Multicollinearity)")
plt.grid(axis="y")
plt.tight_layout()
plt.show()

Here is a heat map of Correlation Between SDG Goals with Pearson correlation coefficient.

import seaborn as sns

plt.figure(figsize=(10,8))
sns.heatmap(statdf[goal_cols].corr(), annot=False, cmap="coolwarm", vmin=-1, vmax=1)
plt.title("Correlation Between SDG Goals")
plt.show()

Here is the heatmap of Spearman Correlation.

import seaborn as sns
import matplotlib.pyplot as plt

spearman_matrix = statdf[goal_cols + ["sdg_index_score"]].corr(method="spearman")

plt.figure(figsize=(12,8))
sns.heatmap(spearman_matrix, cmap="coolwarm", annot=False, vmin=-1, vmax=1)
plt.title("Spearman Correlation Between SDG Goals and SDG Index")
plt.show()

import numpy as np

y_pred = model.predict(X_const)

plt.figure(figsize=(6,6))
plt.scatter(y, y_pred, alpha=0.3)
plt.plot([y.min(), y.max()], [y.min(), y.max()], 'r--')  # 45-degree line
plt.xlabel("Actual SDG Index")
plt.ylabel("Predicted SDG Index")
plt.title("Predicted vs Actual SDG Index (OLS)")
plt.grid(True)
plt.show()

residuals = model.resid
fitted = model.fittedvalues

plt.figure(figsize=(8,5))
plt.scatter(fitted, residuals, alpha=0.3)
plt.axhline(0, color="red")
plt.xlabel("Fitted Values")
plt.ylabel("Residuals")
plt.title("Residual Plot (OLS)")
plt.grid(True)
plt.show()