Introduction
Methodology
Data and variable
Missing data management
Interpolation by simple linear regression method
Linear interpolation
Linear spline interpolation
Lagrange polynomial interpolation
Principal component analysis
Factor analysis
Cluster analysis
Numerical examples for missing data management
Year | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 |
---|---|---|---|---|---|---|---|
Y(t)_Actual | 41.18 | 36.69 | 35.50 | 38.25 | 40.05 | 47.21 | 48.81 |
Lagrange interpolation | 41.18 | 85.34 | 30.58 | 39.45 | 42.07 | 46.31 | 46.92 |
Linear interpolation | 41.18 | 38.34 | 37.47 | 37.78 | 42.73 | 44.43 | 47.14 |
Linear spline interpolation | 41.18 | 42.75 | 42.42 | 41.51 | 40.99 | 40.12 | 39.84 |
Lagrange error | 0.00 | − 48.65 | 4.92 | − 1.20 | − 2.02 | 0.90 | 1.89 |
Linear interpolation error | 0.00 | − 1.65 | − 1.96 | 0.47 | − 2.67 | 2.77 | 1.67 |
Linear regression error | 0.00 | − 6.06 | − 6.91 | − 3.27 | − 0.94 | 7.09 | 8.97 |
Year | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 |
---|---|---|---|---|---|---|---|
Y(t)_Actual | 47.07 | 47.97 | 35.37 | 38.44 | 38.79 | 36.89 | 33.22 |
Lagrange interpolation | 49.51 | 41.77 | 44.78 | 30.86 | 52.39 | − 31.64 | 33.22 |
Linear interpolation | 48.39 | 41.22 | 43.21 | 37.08 | 37.67 | 36.00 | 33.22 |
Linear spline interpolation | 39.76 | 39.38 | 40.37 | 39.85 | 39.60 | 39.87 | 33.22 |
Lagrange error | − 2.45 | 6.20 | − 9.40 | 7.59 | − 13.61 | 5.25 | 0.00 |
Linear interpolation error | − 1.32 | 6.75 | − 7.84 | 1.36 | 1.12 | 0.89 | 0.00 |
Linear regression error | 7.31 | 8.59 | − 5.00 | − 1.40 | − 0.81 | − 2.98 | 0.00 |
Year | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 |
---|---|---|---|---|---|---|---|
Y(t)_Actual | 41.18 | 36.69 | 35.50 | 38.25 | 40.05 | 47.21 | 48.81 |
Linear interpolation | 41.18 | 38.34 | 37.47 | 37.78 | 42.73 | 44.43 | 47.14 |
Linear regression estimate | 41.18 | 41.88 | 41.60 | 41.00 | 40.47 | 40.39 | 39.89 |
Linear spline interpolation | 41.18 | 38.34 | 37.47 | 37.78 | 42.73 | 44.43 | 47.14 |
Linear interpolation error | 0.00 | − 1.65 | − 1.96 | 0.47 | − 2.67 | 2.77 | 1.67 |
Linear Reqression error | 0.00 | − 5.19 | − 6.10 | − 2.75 | − 0.42 | 6.81 | 8.92 |
Linear spline interpolation error | 0.00 | − 1.65 | − 1.96 | 0.47 | − 2.67 | 2.77 | 1.67 |
Year | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 |
---|---|---|---|---|---|---|---|
Y(t)_Actual | 47.07 | 47.97 | 35.37 | 38.44 | 38.79 | 36.89 | 33.22 |
Linear interpolation | 48.39 | 41.22 | 43.21 | 37.08 | 37.67 | 36.00 | 33.22 |
Linear regression estimate | 39.77 | 39.60 | 39.96 | 39.20 | 38.89 | 38.74 | 33.22 |
Linear spline interpolation | 48.39 | 41.22 | 43.21 | 37.08 | 37.67 | 36.00 | 33.22 |
Linear interpolation error | − 1.32 | 6.75 | − 7.84 | 1.36 | 1.12 | 0.89 | 0.00 |
Linear Reqression error | 7.30 | 8.37 | − 4.58 | − 0.75 | − 0.10 | − 1.85 | 0.00 |
Linear spline interpolation error | − 1.32 | 6.75 | − 7.84 | 1.36 | 1.12 | 0.89 | 0.00 |
Conclusion
Result and discussion
Principal component and factor analysis
Year | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 |
---|---|---|---|---|---|---|---|
Y(t)_Actual | 41.18 | 36.69 | 35.50 | 38.25 | 40.05 | 47.21 | 48.81 |
Linear spline interpolation | 41.18 | 42.38 | 39.11 | 37.82 | 40.19 | 39.12 | 45.45 |
Linear regression Estimate | 41.18 | 47.63 | 42.67 | 39.76 | 38.60 | 37.75 | 38.65 |
Linear spline interpolation error | 0.00 | − 5.69 | − 3.61 | 0.43 | − 0.14 | 8.09 | 3.36 |
Linear regression error | 0.00 | − 10.94 | − 7.17 | − 1.51 | 1.45 | 9.45 | 10.16 |
Year | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 |
---|---|---|---|---|---|---|---|
Y(t)_Actual | 47.07 | 47.97 | 35.37 | 38.44 | 38.79 | 36.89 | 33.22 |
Linear spline interpolation | 46.81 | 45.03 | 45.02 | 42.07 | 39.12 | 36.17 | 33.22 |
Linear regression Estimate | 39.35 | 40.01 | 42.17 | 42.30 | 42.44 | 42.58 | 33.22 |
Linear spline interpolation error | 0.26 | 2.94 | − 9.65 | − 3.63 | − 0.33 | 0.72 | 0.00 |
Linear regression error | 7.72 | 7.96 | − 6.79 | − 3.86 | − 3.66 | − 5.69 | 0.00 |
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There is negative correlation (− 0.646) between infant mortality rate and improved sanitation facility. Hence, low sanitation can be the cause for infant mortality.
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There is good direct correlation (0.618) between life expectancy at birth and improved sanitation facility.
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There is strong direct correlation (0.812) between incidence of tuberculosis and prevalence of HIV.
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Cause of death by communicable diseases and maternal, prenatal and nutrition conditions, and cause of death, by non-communicable diseases have strong negative correlation (− 0.862), this implies that attention was given for one of them, so attention should be given for communicable diseases too.
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There is direct correlation (0.71) between infant mortality rate and communicable diseases.
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There is indirect correlation (− 0.70) between life expectancy and communicable diseases.
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Life expectancy at birth and infant mortality rate have strong negative correlation (− 0.844). This implies that most of the countries with short life expectancy should decrease infant mortality by improving sanitation problem.
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The general suggestion for the source of short life expectancy in Africa leads to low sanitation and death due to communicable diseases.
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Labour force and population is highly correlated (0.932). This is a reflection for most populated area have high labour force.
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There is strong direct correlation between transportation systems. A country with a better Air transport have a better Rail lines and Container port traffic (0.83 and 0.85 respectively), and a country with a better Rail lines have also a better Container port traffic (0.80).
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Air transport and Rail lines have strong direct correlation with GDP at market price (0.79 and 0.74 respectively). There is also strong correlation between Air transport and Gross capital formation (0.78). The correlation suggests that transportation system have strong influence on GDP at market price and Gross capital formation.
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GDP at market price have strong positive correlation with Gross capital formation and Foreign direct investment (0.925 and 0.85 respectively). Hence, GDP at market price of a country can be enhanced by calling Foreign investment and accumulating capital.
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In general Foreign investment, accumulating capital and transportation system have strong influence on GDP at market price.
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Electric power consumption have high correlation with GDP per capita, PPP (current international $) (0.753) and mobile cellular subscriptions (0.761).
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GDP per capita, PPP (current international $) and Improved sanitation facilities have strong correlation (0.744).
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Prevalence of HIV and incidence of tuberculosis have some negative correlation (− 0.415, − 0.497, respectively) with life expectancy.
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Prevalence of HIV and incidence of tuberculosis have some what visible correlation (0.442, 0.362, respectively) with manufacturing. This result is a surprising result which reflects that, manufacturing areas are suspected to be the source for medium rate of prevalence of HIV and tuberculosis. Hence, health polices should consider what have to be done in manufacturing area to reduce the prevalence of HIV and incidence of Tuberculosis.
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Cash surplus/deficit is strongly correlated with adult literacy rate and youth literacy rate (0.704, 0.725, respectively). Hence, illiteracy reduction plays an important role for cash surplus.
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There is an indirect Annual freshwater withdrawals in Agriculture have strong indirect correlation with Annual freshwater withdrawals in domestic (− 0.922) and Annual freshwater withdrawals in industry (− 0.794).
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There is some direct correlation (0.498) between Annual freshwater withdrawals in Domestic and Annual freshwater withdrawals in industry.
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There is high correlation between GDP per-capita growth and inflation rate (0.954). This suggests that countries with high GDP per-capita growth should control inflation. This result agree with Barro [17] suggestion.
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There is also some direct correlation (0.40) between GDP per-capita growth and export of good and services. This result agree with Upreti [4].
Principal components | PCI | PC2 | PC3 | PC4 | PC5 | PC6 | PC7 | PC8 |
---|---|---|---|---|---|---|---|---|
Variance | 15.03 | 6.17 | 4.81 | 3.73 | 2.73 | 2.61 | 2.54 | 1.98 |
Proportion of variance | 0.27 | 0.11 | 0.09 | 0.07 | 0.05 | 0.05 | 0.05 | 0.04 |
Cumulative proportion | 0.27 | 0.38 | 0.46 | 0.53 | 0.58 | 0.63 | 0.67 | 0.71 |
Principal components | PC9 | PC10 | PC11 | PC12 | PC13 | PC14 | PC15 | PC16 |
---|---|---|---|---|---|---|---|---|
Variance | 1.85 | 1.70 | 1.35 | 1.13 | 1.12 | 1.10 | 0.93 | 0.77 |
Proportion of variance | 0.03 | 0.03 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 0.01 |
Cumulative proportion | 0.74 | 0.77 | 0.79 | 0.81 | 0.83 | 0.85 | 0.87 | 0.89 |
Data quality
Variable code | Loadings | Corr | Com |
---|---|---|---|
Principal component l (sustainable life) | |||
SP.DYN.LEOO.IN | 1.06 | 0.8079716 | 0.9380648 |
SH.DTH.NCOM.ZS | 1.01 | 0.823052 | 0.7976794 |
SH.H2O.SAFE.RU.Z | 0.78 | 0.7575063 | 0.6749383 |
SP.POP.1564.TO.ZS | 0.73 | 0.8764492 | 0.8405396 |
IT.NET.USER.P2 | 0.67 | 0.7321589 | 0.6816921 |
NV.SRV.TETC.ZS | 0.63 | 0.5728387 | 0.7281336 |
SH.STA. ACSN | 0.55 | 0.77288 | 0.7755206 |
SH.H2O.SAFE.UR.Z | 0.54 | 0.5288412 | 0.6275978 |
SE.PRE.ENRR | 0.48 | 0.5518474 | 0.394378 |
IT.CEL.SETS.P2 | 0.47 | 0.7351707 | 0.8132745 |
SE.TER.ENRR | 0.42 | 0.5128509 | 0.4215655 |
SE.SEC.ENRR | 0.39 | 0.552263 | 0.4368579 |
SH.MED.PHYS.ZS | 0.38 | 0.351458 | 0.4507551 |
IT.NET.BBND.P2 | 0.37 | 0.51236 | 0.345433 |
SH.DYN.AIDS.ZS | − 0.41 | 0.0777566 | 0.8047301 |
NV.AGR.TOTL.ZS | − 0.41 | − 0.7452145 | 0.8178983 |
SH.TBS.INCD | − 0.53 | − 0.0656801 | 0.7888127 |
SP.DYN.CBRT.IN | − 0.74 | − 0.8915574 | 0.8582985 |
SH.STA..MMRT | − 0.82 | − 0.8261328 | 0.725329 |
SH.DTH. COMMA.ZS | − 0.93 | − 0.7800013 | 0.7496144 |
SP.DYN.IMRT.IN | − 0.98 | − 0.8936826 | 0.8558577 |
SP.DYN.CDRT.IN | − 1.04 | − 0.7554875 | 0.8471443 |
Principal component 2 (capital) | |||
NY.GOP.MKTP.CD | 0.97 | 0.9517201 | 0.9170857 |
IS.RRS.TOTL.KM | 0.89 | 0.8176941 | 0.7891843 |
BX.KLT.DINV.CD.WD | 0.87 | 0.8659704 | 0.8049668 |
NE.GDI.TOTL.CD | 0.85 | 0.8781617 | 0.8110496 |
IS.AIR.DPRT | 0.84 | 0.8702821 | 0.8434615 |
IS.SHP.GOOD.TU | 0.74 | 0.7496073 | 0.7518197 |
SP.POP.TOTL | 0. 7 | 0.6570489 | 0.6306013 |
SL.TLF.TOTL.IN | 0.66 | 0.6468067 | 0.7005713 |
EG.USE.ELEC.KH.P | 0.45 | 0.5377256 | 0.8262583 |
SE.TER.ENRR | 0.41 | 0.4621814 | 0.4215655 |
ER.H2O.FWTL.K3 | 0.34 | 0.457826 | 0.40111402 |
NE.IMP.GNFS.ZS | − 0.31 | − 0.3255368 | 0.5675932 |
Principal component 3 (income related factor) | |||
NY.GDP.PCAP.PP.C | 0.89 | 0.9446474 | 0.9213499 |
NY.GDP.PCAP.CD | 0.85 | 0.9250355 | 0.9110524 |
NV.IND.TOTL.ZS | 0.83 | 0.7730768 | 0.6893409 |
GC.DOD.TOTL.GD.Z | 0.7 | 0.8077165 | 0.741978 |
NE.EXP.GNFS.ZS | 0.59 | 0.7130032 | 0.853719 |
EG.USE.ELEC.KH. | 0.58 | 0.726317 | 0.8262583 |
IT.CEL.SETS.P2 | 0.55 | 0.7422919 | 0.8132745 |
SH.STA ACSN | 0.46 | 0.6969131 | 0.7755206 |
NE.TRD.GNFS.ZS | 0.45 | 0.5497256 | 0.7607122 |
SH.MED.BEDS.ZS | 0.45 | 0.4365858 | 0.4086245 |
SE.SEC.ENRR | 0.35 | 0.5159998 | 0.4368579 |
SH.H2O.SAFE.UR.Z | − 0.31 | − 0.0620451 | 0.6275978 |
NV.AGR.TOTL.ZS | − 0.38 | − 0.6669364 | 0.8178983 |
SH.XPD.TOTL.ZS | − 0.46 | − 0.4721462 | 0.5657422 |
NV.SRV.TETC.ZS | − 0.5 | − 0.0756577 | 0.7281336 |
Principal component 4 (life risk) | |||
SH.DYN.AIDS.ZS | 1.05 | 0.7965701 | 0.8047301 |
SH.TBS.INCD | 1 | 0.7393787 | 0.7888127 |
NV.IND.MANF.ZS | 0.77 | 0.6267752 | 0.5451881 |
SP.DYN.CDRT.IN | 0.52 | 0.1150051 | 0.8471443 |
IS.SHP.GOOD.TU | 0.39 | 0.4575752 | 0.7518197 |
NE.IMP.GNFS.ZS | 0.39 | 0.5374423 | 0.5675932 |
SE.XPD.TOTL.GD.Z | 0.38 | 0.3719803 | 0.3151677 |
NE.TRD.GNFS.ZS | 0.34 | 0.5519894 | 0.7607122 |
SH.XPD.TOTL.ZS | 0.33 | 0.2408635 | 0.5657422 |
SH.H2O.SAFE.UR.Z | 0.31 | 0.4626387 | 0.6275978 |
NV.AGR.TOTL.ZS | − 0.32 | − 0.6058561 | 0.8178983 |
DT.TDS.DECT.EX.Z | − 0.37 | − 0.1428698 | 0.2930229 |
SP.DYN.LEOO.IN | − 0.57 | − 0.1076823 | 0.9380648 |
Principal component 5 (literacy) | |||
SE.ADT.1524.LT.ZS | 0.87 | 0.8778255 | 0.8556102 |
SE.ADT.LITR.ZS | 0.86 | 0.8747162 | 0.8454292 |
GC.BAL.CASH.GD.Z | 0.77 | 0.7345393 | 0.61338 |
SH.MED.BEDS.ZS | 0.37 | 0.4346196 | 0.4086245 |
SP.MTR.1519.ZS | − 0.38 | − 0.3940773 | 0.263375 |
Principal component 6 (Rate of Water supply and consumption contrast) | |||
ER.H2O.FWDM.ZS | 1 | 0.8787253 | 0.8500172 |
ER.H2O.FWIN.ZS | 0.66 | 0.5969486 | 0.478074 |
NY.GNS.ICTR.ZS | 0.39 | 0.3865365 | 0.1993376 |
DT.TDS.DECT.EX.Z | 0.36 | 0.2825562 | 0.2930229 |
NE.IMP.GNFS.ZS | 0.32 | 0.5611142 | 0.5675932 |
NE.TRD.GNFS.ZS | 0.31 | 0.5644163 | 0.7607122 |
ER.H2O.FWTL.K3 | − 041 | − 0.4826178 | 0.4011402 |
NV.IND.MANF.ZS | − 0.43 | − 0.1210153 | 0.5451881 |
ER.H2O.FWAG.ZS | − 0.99 | − 0.8804037 | 0.8455708 |
Principal component 7 (GDP growth rate) | |||
NY.GDP.PCAP.KD.Z | 0.88 | 0.805833 | 0.7268232 |
FP.CPI.TOTL.ZG | 0.86 | 0.7815264 | 0.7317425 |
SH.MED.CMHW.P3 | 0.46 | 0.4290214 | 0.2630565 |
NE.EXP.GNFS.ZS | 0.4 | 0.6200583 | 0.853719 |
SH.MED.NUMW.P3 | 0.4 | 0.4986513 | 0.4551595 |
SH.MED.PHYS.ZS | 0.39 | 0.4168554 | 0.4507551 |
NV.SRV.TETC.ZS | 0.32 | 0.3575785 | 0.7281336 |
SH.H2O.SAFE.UR.Z | − 0.41 | − 0.3178607 | 0.6275978 |
SH.XPD.TOTL.ZS | − 0.46 | − 0.4648261 | 0.5657422 |
Country code | BENß | GHA | CMR | TGO | KEN | UGA | SEN |
T-Chart | 0.54 | 0.78 | 0.90 | 0.94 | 1.17 | 1.35 | 1.36 |
Country code | ZMB | GNB | GIN | MOZ | RWA | COM | MWI |
T-Chart | 1.62 | 1.78 | 1.79 | 1.86 | 2.33 | 2.51 | 2.63 |
Country code | BFA | TCD | SOM | MLI | MRT | ZWE | SDN |
T-Chart | 2.91 | 2.93 | 3.13 | 3.20 | 3.74 | 3.93 | 3.99 |
Country code | AGO | BDI | STP | BWA | LBR | NAM | MAR |
T-Chart | 3.99 | 4.39 | 4.56 | 4.84 | 4.85 | 4.93 | 5.07 |
Country code | ERI | SLE | DZA | MUS | MDG | CAF | CPV |
T-Chart | 5.22 | 5.36 | 5.46 | 5.63 | 5.65 | 5.71 | 7.39 |
Country code | TUN | DJI | GAB | ETH | SYC | LSO | NGA |
T-Chart | 7.68 | 8.23 | 8.79 | 10.29 | 11.48 | 11.84 | 12.21 |
Country code | GNQ | LBY | SWZ | NER | SSD | ZAF | |
T-Chart | 14.62 | 16.61 | 16.83 | 28.55 | 30.56 | 32.87 |
Cluster analysis
Number of clusters
No. of clusters | 2.00 | 3.00 | 4.00 | 5.00 | 6.00 | 7.00 | 8.0 | 9.00 | 10.00 | 11.00 | 12.00 | 13.00 | 14.00 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Belween-cluster variability | 1219.46 | 1063.91 | 948.85 | 811.08 | 691.45 | 658.29 | 565.32 | 563.71 | 438.92 | 374.95 | 348.20 | 320.78 | 330.61 |
Within-cluster viability | 550.88 | 661.89 | 836.55 | 958.72 | 1074.51 | 1112.05 | 1252.35 | 1294.21 | 1351.96 | 1366.89 | 1426.89 | 1447.97 | 1461.04 |
F_ratio | 0.45 | 0.62 | 0.88 | 1.18 | 1.55 | 1.69 | 2.22 | 2.30 | 3.08 | 3.65 | 4.10 | 4.51 | 4.42 |
Determining elements of the cluster
Clusters | Countries assigned for each cluster | |||
---|---|---|---|---|
Average linkage | K-mean | Ward’s | Boot strap Ward’s (95% confidence level) | |
Cluster 1 | NAM, BWA, SWZ, LSO | BWA, NAM, SWZ | NAM, BWA, SWZ, LSO, MUS, DJI | NAM, BWA, SWZ, LSO, MUS, DJI |
Cluster 2 | DZA, MAR, MUS, TUN, CPV | MUS, TUN, CPV, STP, DJI, LSO, LBR | DZA, MAR, TUN, CPV | DZA, MAR, TUN, CPV |
Cluster 3 | AGO, MRT, SDN, GHA, ZMB. CMR, STP, DJI, KEN, SEN, BEN, ZWE, TCD, ERI, MLI MDG, UGA, COM, BFA, GNB, SLE, TGO, GIN, RWA, CAF, MOZ, LBR, BDI, MWI, SOM | MRT, CMR, BEN, TCD, MLI, MDG, GIN, MOZ, SOM | TCD, MLI, SOM, MRT, GIN, MOZ, SDN, MDG, GHA, CMR, KEN, SEN, BEN, AGO, ZMB, ZWE | TCD, MLI, SOM, MRT, GIN, MOZ, SDN, MDG, GHA, CMR, KEN, SEN, BEN, AGO, ZMB, ZWE |
Cluster 4 | NGA, ETH | NGA, SDN, ETH | NGA, ETH | NGA, ETH |
Cluster 5 | LBY, GAB | GHA, ERI, UGA, COM, BFA, GNB, SLE, TGO, RWA, CAF, BDI, MWI | UGA, MWI, ERI, COM, BFA, GNB, SLE, TGO, RWA, CAF, BDI, STP, LBR | UGA, MWI, ERI, COM, BFA, GNB, SLE, TGO, RWA, CAF, BDI, STP, LBR |
Cluster 6 | GNQ, SYC | GNQ, LBY, SYC, GAB, DZA, ZAF, MAR | GNQ, LBY, SYC, GAB | LBY, GAB |
Cluster 7 | SSD | SSD | SSD | |
Cluster 8 | NER | NER | NER | |
Cluster 9 | ZAF | AGO, ZMB, KEN, SEN, ZWE | ZAF |
Conclusion for cluster analysis
Inference for population
Ward’s cluster | PCI | PC2 | PC3 | PC4 | PC6 | PC7 | PC5 |
---|---|---|---|---|---|---|---|
Cluster 1 | Medium (− 0.045, 2.11) | Low (− 0.75, − 0.39) | Low (− 0.75, 1.05) | High (0.59, 3.07) | High (− 0.56, 2.49) | Low (− 0.54, 0.49) | Low (− 0.40, 1.04) |
Cluster 2 | High level (1.35, 2.46) | Medium (− 0.16,1.44) | Medium (− 0.11, 1.10) | Medium (− 0.54, 0.48) | Low (− 0.71, − 0.05) | Low (− 0.37, 1.12) | Low (− 0.32, 0.57) |
Cluster 3 | Low (− 1.63, − 0.53) | Low (− 0.69, 0.84) | Low (− 0.96, 0.49) | Medium (− 1.22, 1.24) | Low (− 1.73, − 0.077) | Low (− 0.51, 1.56) | Low (0.65, − 1.13) |
Cluster 4 | Low (− 1.28, − 0.79) | Medium (0.81, 2.71) | Low (− 0.58, 0.12) | Low (− 1.36, − 1.21) | Low (− 0.93, − 0.72) | Low (− 0.89, 0.41) | High (0.59, 2.71) |
Cluster 5 | Low (− 1.62, 0.71) | Low (− 0.86, − 0.12) | Low (− 0.41, − 0.41) | Medium (− 0.93, 0.32) | Low (− 0.69, 1.96) | Low (− 1.54 ,0.45) | Medium (− 0.62, 1.21) |
Cluster 6 | Medium (− 0.13, 2.06) | Low (− 0.53, 0.44) | High (2.23, 3.45) | Medium (− 0.79, 0.61) | Medium (− 1.14, 2.29) | Low (− 0.81, 1.26) | Medium (− 0.13, 0.83) |
Cluster 7 | Low (− 0.54) | Low (0.47) | Low (0.16) | Medium (− 0.37) | Low (0.35) | High (5.13) | Low (0.03) |
Cluster 8 | Low (− 0.88) | Low (− 0.34) | Low (− 0.79) | Medium (− 0.95) | Low (− 0.20) | Low (− 0.93) | Low (− 5.03) |
Cluster 9 | Medium (1.34) | High (5.12) | Medium (0.49) | Medium (2.22) | Low (0.29) | Low (0.17) | Low (− 0.43) |