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Download the attached file. The file contains 3 spreadsheets: Data - with variable definitions - Correlation Matrix Regression Results Dependent variable = Health_pc From the data, data definitions, correlation matrix and or Regression results, Identify 3 distinct and separate issues that may indicate that the independent variables are highly collinear/exhibit multi-collinearity. Provide an explanation as to why you believe a given issue is an indicator of multi- collinearity. DA 292 HW #8 Fall 2021.xlsx Data Correlation Matrix Regression Results 4225 Black, Tja LEGEND althy-healthcare expectes per capita Ameril in bili dollar Family that presenumber of that would nebom a woman if she were to live the le in accordance with a specificity of the specified Ralppipation that conto pooplein is defined by national face between total population and the population openly tool is the rate of older dependentspple older that the working propias depend per 100 wakipepe poti hade death rate that indicates the header during the year, per 1.000 plantmatud werpentage of the ages 13-2) met whoeve te les vaccination de 12 utapadol pokal pedihares for health care med by falta pocket expende se bude of the laws and is paid by the Country health pc fertility immune NP 2017 AFG 65.70602417 4,633 64 75.482574 2017 AGO 114.3346034 5.6 42 34.121014 2017 BDI 2338145905 3.502 90 25.462732 2017 BEN 30.05702501 4.906 70 44.17421 2017 BFA 44 40372056 5.271 88 31.677679 2017 BGD 37.43034717 2.062 97 73.833127 2017 BTN 1048551025 1.994 97 13.306466 2017 CAF 29.9101693 4.796 49 56.415894 2017 COD 19.41761519 6,017 $7 40.124722 2017 COM 61.72982892 90 74.28907 2017 DII 72 2347641 2.785 X1 26 157722 2017 ETH 24.92431232 435 $9 34.401615 2017 GIN 36 50 24356 4.777 47 56.223152 2017 GMB 22.71026421 5.281 90 26 840956 2017 GNS SO $725234 4.553 86 76.147812 2017 HTI 62.72842407 2.986 69 39.933181 2017 KHM $2.03518141 2.53 4 60435875 2017 KIR 175.0655212 3.61 $10.1120372 0100 61.20571136 2.700 72 46.237105 2017 LBR 56.9612236 4.37 87 45.502407 2017 LSO 111.6414261 3.171 90 16.642639 2017 MDXC 2463006706 4.13 6034.67635 2017 ML 34 00102997 5.96 70 32.547749 2017 MMR 57 859272 2.168 X375.687332 2017 MOZ 35.9498558 4.922 87 9.9331854 2017 MRT $1.61418533 4.619 78 50 676067 2017 MWI 33.99734379 4102 13 10.590348 2017 NER 28.80011749 7001 82 48.313721 2017 NPL 508261261 1.967 90 $7.786100 2017 RWA 526735153 4.OSS 97 11.686872 2017 SDN 192.6066589 4.469 90 72.479668 2017 SEN 55.10048676 4.697 90 53.780712 2017 SLB 97.43270978 4.44 84 1.9281663 2017 SLE $2.5494918 4.359 NO 40.923931 2017 SSD 2638130379 4.775 SO 19.192814 2017 STP 117.010612 4.374 90 14.757313 2017 TCD 29.4996964 5.846 37 58.023159 2017 TGO 37.47055817 4384 77 60.086071 2012 TLS 26.74797055 4.093 27 X 1449154 2017 TZA 35.4696846 90 23.952702 2017 UGA 41.95500781 $.005 83 1845 2017 MB 6730799561 4.718 96 12.045432 rural A death 74.75 R6,000753 6.575 35.161 96.820228 87.294 91.359181 8.106 53.232 4.885708 9.025 71.257 90.803873 8341 64.142 50,091697 5.533 59.833 47371469 6,243 59.02 90,395761 12.671 $6.12 97.251231 9.691 71.216 74,627954 7.29% 22 152 $2.5285 7.293 79.69 $0.95823 6.691 64.207 N9.23333 2.715 39.401 88.65317 8.044 $7.05S 82.673759 9.805 45.654 62/495626 8.63 77.62 55 40535 6.017 46.718 63.SSSR 6.396 65.632 58.414555 6.SON 49.303 NO 36492 7.729 72.27 60.911514 14.656 63.478 78.261047 6.18 $8.428 10095547 04 69.678 47.945958 8.2005 64.545 91.78322 8.964 47.176 76.657447 7.339 83.286 88.486814 6811 365 111.3434 8.529 0.664 $8.733179 6.404 82.875 75,395179 5.303 65,63 79.82323 7.261 53.26 6.257386 5.845 76.714 78.336745 4.284 58.364 79657069 12.003 0.654 3,394012 10.574 28.032 8437411 4.908 77.142 99 470559 12.309 $XXIR NO.140575 X. 56 69.788 744,404162 6.05 66.947 RN324107 6.622 76,04 97006461 6,760 $7.024 90.462311 6,633 4953 Data Correlation Matrix Regression Results fernly out exp rural fertility immune out exp rural age 0349429 -0.091861 0.145351 0.0555489 0.0319093 0.075524 0.665716-0.350642 -0.0527260.10922

Answer :

To effectively address multicollinearity, consider techniques like dropping correlated variables, using regularization techniques like Ridge or Lasso regression, or using dimensionality reduction methods like Principal Component Analysis.

High Correlation between Independent Variables: If there is a high correlation (close to +1 or -1) between two or more independent variables, it might indicate multicollinearity. This means that these variables provide similar information to the model, making it difficult to distinguish their individual effects.

Inconsistent Sign and Magnitude of Coefficients: In a multiple regression model, if the signs and magnitudes of the coefficients of the independent variables change dramatically when adding or removing other variables from the model, it could be a sign of multicollinearity.

Unstable Estimates: Small changes in the data or minor adjustments to the model can lead to significant changes in the estimated coefficients. This instability suggests that the model is sensitive to variations in the data and could be due to multicollinearity.

Keep in mind that to conclusively diagnose multicollinearity, you might want to calculate the Variance Inflation Factor (VIF) for each independent variable. High VIF values (typically greater than 10) often indicate multicollinearity.

Therefore, To effectively address multicollinearity, consider techniques like dropping correlated variables, using regularization techniques like Ridge or Lasso regression, or using dimensionality reduction methods like Principal Component Analysis (PCA).

To study more about Principal Component Analysis:

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