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# Factors influencing the Human Development Index (HDI) using Multiple Linear Regression

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Identified the most crucial factors that influence Human Development Index through regression analysis using Minitab software

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### Factors influencing the Human Development Index (HDI) using Multiple Linear Regression

1. 1. Factors influencing the Human Development Index (HDI) using Multiple linear regression<br />ADITYA PANUGANTI<br />1202062944<br />Industrial Engineering<br />Year of data: 2008<br />Source: UN Development Programme Database<br />
2. 2. Objective and Dataset description<br />To find which of the following variables have an effect on the Human Development Index (HDI)<br />
3. 3. Fitting the full model without interaction terms<br />The regression equation for full model is<br />y = 0.0596 + 0.00440 LIF + 0.000007 GDP - 0.000748 GRO + 0.0158 SCH + 0.0080 GEN+ 0.0159 EXP - 0.000004 GNI + 0.000003 MAT - 0.000051 HOM - 0.000540 MOR+ 0.000176 LIT - 0.0185 DEP + 0.0023 CON1 - 0.0117 CON2 - 0.0100 CON3+ 0.00431 CON4 - 0.0268 CON5<br />Difficult to interpret the coefficients of the above regression equation.<br />Hence standardized the regression coefficients using Unit Normal scaling<br />
4. 4. Fitting the full model after Standardization<br />The regression equation is<br /> y = 0.684 + 0.0404 LIF + 0.100 GDP - 0.0117 GRO + 0.0408 SCH + 0.00136 GEN+ 0.0443 EXP - 0.0627 GNI + 0.00089 MAT - 0.00068 HOM - 0.0196 MOR+ 0.00259 LIT - 0.0185 DEP + 0.0023 CON1 - 0.0117 CON2 - 0.0100 CON3+ 0.00431 CON4 - 0.0268 CON5<br />Model Statistics:<br />R-Sq = 98.5% R-Sq(adj) = 98.2%<br />Analysis of Variance (ANOVA)<br /> Source DF SS MS F P<br /> Regression 17 2.21784 0.13046 325.49 0.000<br /> Residual Error 84 0.03367 0.00040<br /> Total 101 2.25150<br />
5. 5. Signs of Multicollinearity<br />Inference from Variance Inflation Factor (VIFs):<br /> VIF of GDP = 560.116 and VIF of GNI = 533.109 (Indicating Severe Multicollinearity)<br /> VIF of EXP = 18.368 and VIF of GRO = 16.456 (just over 10; Indicating Multicollinearity)<br />Inference from Correlation matrix: <br /> LIF GDP GRO SCH GEN EXP GNI MAT<br /> GDP 0.595<br /> GRO 0.719 0.630<br /> SCH 0.603 0.553 0.776<br /> GEN -0.677 -0.705 -0.758 -0.743<br /> EXP 0.692 0.636 0.956 0.774 -0.798<br /> GNI 0.584 0.999 0.618 0.539 -0.688 0.620<br /><ul><li>Dropped GNI from the model.
6. 6. No change in R-sq and R-sq(adj) statistics before and after dropping the model </li></ul>R-Sq = 98.5% R-Sq(adj) = 98.2%<br />To confirm Multicollinearity between EXP and GRO, did a further analysis using Principal Component Analysis.<br />Found the condition number to be (Condition number = λmax/ λmin=7.8001/0.0327 = 238.53 <br />>100, indicating moderate multicollinearity<br /><ul><li>Dropped EXP also from the model and check the Model summary statistics- a slight reduction in R-sq and R-sq(adj) .</li></li></ul><li>Residual plots and Model Adequacy<br />Both normality and Residual vs fitted plots look good, satisfying the normality and constant variance conditions<br />
7. 7. Indicator Interactions<br />Considered interaction terms of DEP and other numerical variables.<br />24 variables in all including all the interaction terms<br />S = 0.0220704 R-Sq = 98.3% R-Sq(adj) = 97.8%; R-Sq(pred) = 96.80%<br />Residual plots:<br />
8. 8. Outliers and Influential points<br />
9. 9. Other outliers in graph<br />Fitting each of the datapoints 45, 50, 80 and checking if there is any changes in summary stats<br />These points are not contributing to any leverage, nor being influential; except for the fact that they are outliers; also R-sq not changing much, therefore we are leaving them in the model.<br />
10. 10. Residual plots after taking off the outliers and influential points<br /><ul><li>Normal probability plot looks good but the Residuals vs fit looks like a double bow shaped.
11. 11. To confirm this, we have used box cox transformation which showed us that there is a need in the transformation on ‘y’</li></li></ul><li>Box-Cox Transformation<br />Suggests lambda = 2, implies transform y  y2<br />
12. 12. Residual plots after transformation<br />Can find some outliers in the Normal probability plot<br />
13. 13. Outliers and Influential points<br />
14. 14. Residual plots after taking off the outliers and influential points<br />No need for any transformation, Box-Cox suggests λ = 1<br />
15. 15. Variable selection and Model building<br />
16. 16. Fit the selected model<br />Regression equation:<br /> y2= 0.476 - 0.0164 GEN + 0.0403 GRO + 0.0422 LIF + 0.0557 GDP + 0.0449 SCH - 0.0181 CON2 - 0.0388 MOR + 0.0523 GDP_D + 0.0289 CON5 + 0.0412 MOR_D - 0.0476 HOM_D<br />Detected Multicollinearity using Principal component analysis<br />condition number = 134.837 (>100, Moderate Multicollinearity)<br />Linear dependency equation: 0.107GRO+0.337LIF+0.798MOR-0.467MOR_D (dependency between the variables in the equation)<br />Using correlation matrix found that the variable MOR has large correlation with LIF and MOR_D.<br />Dropping MOR removed multicollinearity from model (condition number = 39.04617 (<100, No multicollinearity)<br />
17. 17. Residual plots after dropping MOR<br /><ul><li>Presence of an outlier  datapoint 72
18. 18. No need for any transformation, Box-Cox suggests λ = 1</li></li></ul><li>Fit the model after dropping off the outlier<br />The regression equation is<br /> y2= 0.482 - 0.0221 GEN + 0.0436 GRO + 0.0576 LIF + 0.0528 GDP + 0.0483 SCH - 0.0115 CON2 + 0.0556 GDP_D + 0.0182 CON5 + 0.0169 MOR_D - 0.0538 HOM_D<br />R-sq = 99.1% R-sq(adj) = 99% R-sq(pred) = 98.73%<br />
19. 19. Model validation<br />Considered 118 countries for modelling <br />102  Estimation data and 16  prediction data<br />
20. 20. Conclusion<br />The reduced model has a better R-sq than the actual model and most of the variables are significant (low p-value) in the model.<br />The following variables were found to be significant <br />Gender inequality index<br />Combined gross enrolment<br />Life expectancy at birth<br />GDP<br />Mean schooling years<br />Countries in continent 2<br />GDP& intensity of deprivation<br />Under 5 mortality rate& intensity of deprivation<br />Homicide rate& intensity of deprivation<br />
21. 21. Possible improvements<br />More datapoints<br />Ridge regression to eliminate multicollinearity<br />Robust regression – to add more weight to the datapoints and retain them in the model.<br />