data=read.csv("c:/Users/InteL/desktop/data/karpur.csv")
head(data)
NA

data=data[order(data$k.core, decreasing = TRUE),]
data$k.core
  [1] 15600.0000 14225.3135 13544.9785 13033.5283 11841.7432 11117.4023
  [7] 10860.0000 10649.9580 10540.0000  9898.4785  9533.3623  9458.1729
 [13]  9120.0000  8820.1025  8820.0000  8820.0000  8760.0000  8742.7529
 [19]  8689.8252  8390.0000  8306.0459  8190.0000  8030.0000  7956.4258
 [25]  7930.0000  7918.8984  7862.0825  7740.0000  7725.9600  7525.2207
 [31]  7517.4834  7430.0000  7260.0000  7200.0000  7180.0000  7141.1079
 [37]  7119.8560  7020.0000  6950.0000  6917.3823  6911.2437  6879.2051
 [43]  6860.0000  6844.0718  6697.7793  6690.1787  6680.0000  6447.5181
 [49]  6389.9697  6388.7993  6280.0000  6262.2485  6232.8345  6186.6069
 [55]  6180.0000  6171.7534  6138.8628  6070.0000  6046.8418  6027.3105
 [61]  6008.5645  5963.4404  5954.8203  5950.0000  5880.0000  5855.1367
 [67]  5810.0000  5750.0000  5640.0000  5630.0000  5595.5122  5480.0000
 [73]  5479.2744  5467.9937  5430.0000  5412.3223  5363.0371  5338.7207
 [79]  5266.6743  5202.5576  5200.0000  5190.0000  5067.7798  5048.1919
 [85]  5020.0947  4969.6675  4954.9937  4900.0000  4876.0210  4832.6045
 [91]  4830.0000  4810.0000  4804.8022  4786.5928  4760.0000  4749.9390
 [97]  4730.0000  4703.0840  4678.7793  4630.0000  4561.4106  4560.0000
[103]  4490.0000  4451.9521  4420.0000  4390.0000  4390.0000  4336.2285
[109]  4328.6606  4320.0000  4318.5918  4267.1182  4243.5298  4210.0000
[115]  4203.3662  4154.2788  4135.5439  4119.3125  4111.0464  4075.8799
[121]  4070.0000  4047.0544  4020.0000  4014.4380  4011.0640  3998.8315
[127]  3997.4805  3988.5752  3983.7849  3982.3992  3970.0000  3956.0886
[133]  3950.7856  3940.0000  3936.2375  3890.0000  3885.8645  3882.9058
[139]  3872.8757  3840.0000  3840.0000  3820.7578  3820.0000  3795.9231
[145]  3750.0000  3750.0000  3743.4775  3720.0000  3700.0000  3680.0000
[151]  3677.3889  3660.6824  3660.0000  3650.0000  3650.0000  3630.0000
[157]  3630.0000  3610.0000  3580.1563  3561.8772  3526.3140  3520.5999
[163]  3473.3303  3460.0000  3459.5876  3435.5005  3430.0000  3428.3037
[169]  3427.3506  3420.0000  3405.0000  3385.5239  3380.0000  3370.0000
[175]  3361.7825  3350.0000  3340.3401  3340.0000  3340.0000  3338.0410
[181]  3328.5642  3320.0000  3310.0000  3302.7673  3293.4705  3290.0000
[187]  3250.0000  3250.0000  3230.0000  3220.0000  3210.3184  3181.6086
[193]  3180.0000  3170.0000  3169.1899  3150.3203  3147.1648  3128.8020
[199]  3110.4392  3110.0000  3100.0000  3090.0657  3090.0000  3066.8655
[205]  3053.2048  3040.4338  3040.0000  3040.0000  3035.1306  3033.0540
[211]  3006.9888  3000.0000  2997.9072  2990.0000  2985.1362  2981.4578
[217]  2970.0000  2968.5474  2963.2549  2960.0000  2947.7891  2928.3137
[223]  2928.1562  2918.3340  2913.8325  2895.5957  2890.0000  2865.1624
[229]  2853.9758  2846.9758  2844.1299  2832.4385  2830.0000  2818.8896
[235]  2805.6685  2802.1687  2797.1609  2790.4949  2760.0000  2759.9543
[241]  2759.4919  2750.0000  2749.5947  2736.5945  2730.0000  2707.6108
[247]  2700.0000  2698.5298  2697.3630  2686.7124  2660.0000  2652.5637
[253]  2648.6399  2640.0000  2638.9934  2620.0000  2613.3105  2610.9341
[259]  2602.4993  2593.2590  2590.4043  2573.2031  2570.3201  2570.0000
[265]  2563.3101  2558.1506  2550.4099  2534.7722  2532.9214  2531.0762
[271]  2531.0000  2530.7581  2523.2039  2520.0000  2519.8000  2495.1360
[277]  2495.1101  2492.1201  2485.9099  2483.5156  2478.3799  2473.3740
[283]  2468.4099  2464.1960  2463.9275  2457.0735  2455.0183  2450.0000
[289]  2448.0698  2445.8406  2442.5901  2436.6628  2434.4473  2433.3545
[295]  2427.4851  2423.3625  2423.1160  2421.8501  2417.5913  2393.9656
[301]  2383.2690  2371.0400  2368.7905  2367.5498  2348.8201  2346.7214
[307]  2335.4399  2331.9900  2322.1902  2309.5901  2303.7000  2302.9199
[313]  2299.0537  2295.7800  2294.7261  2285.0200  2284.4299  2283.9041
[319]  2276.3159  2274.8657  2273.6499  2270.0000  2270.0000  2264.6938
[325]  2262.0811  2250.9197  2234.4399  2225.8699  2216.6179  2214.3201
[331]  2202.3936  2200.3501  2200.1130  2196.3706  2192.6060  2183.5400
[337]  2182.8186  2173.0312  2163.2437  2155.4399  2155.4353  2150.3301
[343]  2149.6299  2147.4971  2137.3313  2126.4495  2114.7256  2100.5769
[349]  2091.1111  2089.7009  2084.4082  2067.3738  2056.2000  2049.1858
[355]  2047.1400  2032.8934  2030.7120  2029.4900  2023.9753  2023.0345
[361]  2019.7881  2014.7900  2011.7695  2010.0000  1994.3353  1990.0415
[367]  1978.5601  1975.0400  1969.0728  1956.1077  1952.0089  1931.9360
[373]  1928.4800  1922.0161  1917.3143  1913.9399  1907.3199  1899.3845
[379]  1884.6757  1876.8239  1873.8779  1868.6801  1866.9200  1866.0350
[385]  1862.8700  1859.2261  1852.2700  1828.3900  1821.4403  1820.0200
[391]  1804.5400  1790.6000  1789.1849  1777.6891  1743.1687  1736.2400
[397]  1722.9451  1712.4135  1708.2007  1679.6505  1652.2738  1648.4301
[403]  1641.8264  1634.0441  1628.7540  1619.7656  1605.7228  1603.0649
[409]  1599.2813  1591.2185  1585.5100  1584.9600  1574.8315  1562.2338
[415]  1556.9391  1552.5758  1550.0200  1538.3466  1530.4865  1528.5027
[421]  1510.6265  1508.5300  1498.7391  1481.5601  1479.8101  1479.3800
[427]  1474.6252  1467.6908  1466.9917  1454.2400  1441.6909  1439.0200
[433]  1437.5200  1435.2444  1433.7565  1411.6324  1408.8621  1405.6776
[439]  1403.4971  1383.6300  1380.1622  1377.0441  1371.7496  1371.3700
[445]  1366.1600  1362.4678  1361.9829  1348.7238  1340.0023  1330.3890
[451]  1312.6384  1309.5500  1308.2550  1306.1700  1302.1428  1300.7900
[457]  1291.5699  1291.2828  1290.3817  1280.9800  1276.5076  1274.9500
[463]  1271.3800  1269.6000  1261.7902  1259.5554  1257.2810  1254.4161
[469]  1249.7059  1248.4808  1244.7603  1237.4061  1227.8700  1217.8118
[475]  1213.6443  1213.0129  1209.9685  1205.7067  1201.6453  1196.2823
[481]  1183.6799  1181.8900  1181.2655  1181.2034  1178.3425  1171.3900
[487]  1149.5182  1142.8588  1138.5061  1137.1615  1117.7709  1114.6503
[493]  1109.5569  1103.1545  1102.0452  1099.4039  1089.8239  1089.4352
[499]  1086.0234  1074.0900  1066.3792  1064.6899  1064.5748  1061.7000
[505]  1059.9347  1054.2761  1053.1267  1053.0554  1042.6234  1040.1243
[511]  1036.0116  1022.5287  1020.4655  1015.6563  1006.8500  1006.0170
[517]   999.1555   999.0900   991.5684   990.7814   980.9962   980.7972
[523]   965.5400   963.2323   959.0341   958.4979   957.2500   956.3419
[529]   945.3500   941.5269   939.4300   927.2867   921.8100   917.3568
[535]   916.6622   913.8117   910.1433   906.6724   902.0577   896.8858
[541]   895.5803   895.5394   893.3821   884.5511   883.1400   881.0916
[547]   881.0173   868.0800   866.4542   865.2035   863.7920   863.4496
[553]   863.3700   862.5884   860.4050   859.5496   857.3100   851.8912
[559]   841.6011   832.0446   831.6595   831.5900   830.4200   827.7700
[565]   827.6600   823.1191   822.9766   817.8306   817.6682   815.7291
[571]   813.0876   809.4100   800.2973   797.7479   794.9600   793.8796
[577]   783.6499   780.4501   770.6907   770.6147   769.9286   768.5499
[583]   767.2200   762.7256   759.7528   745.9776   744.1807   739.4100
[589]   736.8026   735.0908   733.3159   729.7252   729.5110   728.3082
[595]   722.0266   721.3600   718.6742   712.3636   710.9836   705.0552
[601]   704.7114   699.5100   691.0581   690.1351   683.6700   679.7400
[607]   677.5000   673.3079   672.4337   669.3860   665.2421   664.4689
[613]   662.5491   660.1379   654.5244   652.6051   651.9620   643.0700
[619]   641.5605   635.5146   625.7729   619.6802   614.1521   613.7889
[625]   609.8132   609.7765   603.8804   601.2100   599.3371   597.4316
[631]   595.5261   593.6207   591.7152   589.8097   588.2900   586.3036
[637]   583.4219   582.1041   578.0658   575.6158   572.5554   564.8300
[643]   562.5400   558.2472   546.8344   546.3184   537.4426   534.7041
[649]   526.1400   522.3776   520.6862   514.5711   512.2100   512.1923
[655]   508.1339   507.4758   507.3651   499.3700   485.5200   482.8237
[661]   480.6394   470.2819   467.8958   467.5622   465.0500   459.3311
[667]   458.8995   458.2000   454.5060   452.0254   451.0764   449.4991
[673]   447.7827   440.9626   437.7937   433.0881   431.1000   428.4266
[679]   421.6921   419.3290   418.6400   417.1300   412.7467   406.2400
[685]   399.1000   398.5300   397.6351   395.8943   392.7800   388.9573
[691]   387.5817   386.1200   383.8092   371.8761   369.7330   369.4849
[697]   368.3500   358.7004   358.4117   357.9313   355.8343   350.6500
[703]   349.4881   339.4923   332.4404   324.0869   322.6981   321.5066
[709]   316.3239   310.0188   308.4382   303.5096   303.1158   300.3600
[715]   300.2600   298.5033   292.3396   287.3767   282.9963   282.9518
[721]   282.0900   279.4883   273.4974   271.5998   271.4900   270.5496
[727]   263.7113   260.5923   258.1300   257.7916   255.8228   248.3004
[733]   247.9343   246.6400   244.4739   242.9852   240.2611   240.0458
[739]   231.2015   231.0803   228.8449   228.5700   226.0300   223.7040
[745]   216.6000   210.2700   204.7999   197.0975   193.4600   191.6111
[751]   190.5800   185.0957   181.7682   173.4400   168.2400   165.3502
[757]   162.9400   158.1300   152.9717   152.1418   149.2137   146.7933
[763]   146.0790   143.3300   140.6149   140.0700   134.4366   133.6028
[769]   131.3576   128.2582   126.0047   124.7100   122.0799   119.7091
[775]   116.5507   115.9015   114.3747   112.6725   109.7232   103.5448
[781]   102.8927   101.8555    97.3665    92.4887    91.1881    86.9600
[787]    86.0763    85.0097    76.8100    74.1400    73.2072    73.2033
[793]    70.1081    69.2599    63.8231    55.5300    54.2100    52.4436
[799]    50.1500    48.6200    44.5030    43.9438    39.6700    39.4679
[805]    35.8600    35.6272    34.9919    33.7340    32.5300    30.6700
[811]    22.0900     7.3800     5.8300     4.8748     3.5809     3.2300
[817]     2.2869     0.8100     0.4200
sample =c(1: length(K))
k_percent <- (sample * 100) / length(K)


plot(k_percent, K, log='y', xlab="Portion of total sample having higher permeability", ylab="Sample permeability, md", pch=10, cex=1, col="red")


log_k <- log(K)
slr <- lm(log_k ~ k_percent)
plot(k_percent, log_k, xlab="Portion of total sample having higher permeability", ylab="Sample permeability, md", pch=10, cex=1, col="#001c49")
abline(slr, col='red', lwd=2)



Hdata <- data.frame(k_percent = c(50, 84.1))
pred.values <- predict(slr, Hdata)
Heterogeneity.index <- (pred.values[1] - pred.values[2]) / pred.values[1]
Heterogeneity.index
        1 
0.2035464
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