Data Management - Case Study Example

Statistics Data Management One Stats and Two Stats Variable Introduction 2. Background 3. Aims and Objective 4. Research Questions 5. Hypothesis 6. Data Analysis and Results 6.1. Regression 6.2. Correlation 6.3. Measures of Central Tendency 6.4. Effects of Outliers 6.5. Cause and Effect 6.6. Hidden and Extraneous Variables 6.7. Graphs 7. Discussion 8. Conclusion Gasoline versus Hybrid Vehicles 1. Introduction This study involves a comparison between gasoline and hybrid vehicles in order to determine the one that is better than the other. Each of the vehicle types has a set of unique advantages and disadvantages. From the attributes identified in the comparison, hybrid vehicles are better than the ordinary gasoline vehicles in various aspects of performance. The performance of hybrid vehicles is characterized by more power and covers more miles per gallon of fuel than the gasoline vehicles. Additionally, the analysis of performance shows that hybrid vehicles are more favorable to the environment than the gasoline vehicles, even though hybrid also generates pollution. However, in the aspect of cost efficiency and effectiveness, it is cheaper to produce and operate a gasoline vehicle than a hybrid vehicle. 2. Background The target audience for this project is the young drivers who are in the decision making process on which vehicle to buy between the gasoline cars and hybrid vehicles. Of course, the decision on the purchase of a car is very stressful in the target market full of varieties (Ehrenberg 49). From the inherent view, it is easier to conclude that hybrid vehicles are of more ben3fits than gasoline cars. This research is meant to use practical data to provide an informed ground to support the decision by referring to the pros and cons of using hybrid and gasoline vehicles. The advantages of hybrid vehicles are seen to be higher gas mileage, minimal taxation and environmental safety. Looking at the advantages of gasoline cars, they generate more power and are less expensive to produce and operate. Comparing the disadvantages, it is very expensive to produce and operate hybrid vehicles. Secondly, they generate less power compared to gasoline vehicles. On the other hand, gasoline vehicles have less gas mileage and do not reduce the taxes. The images of gasoline car and hybrid car are shown below: Figure 1: Gasoline Car - Honda Accord Sedan Figure 2: Hybrid Car - Honda Civic Sedan 3. Aims and Objectives The objective of this study is to establish the relationship between the vehicle model and the performance in order to identify the vehicle of choice between gasoline and hybrid vehicle. It intends to use practical data collected in the market, to make informed decision on the vehicle to buy. This implies that the data will have to be analyzed quantitatively after comparing the advantages and disadvantages of the two models. The advantages and disadvantages are summarized in the tables 1 and 2 below: Car Model Honda Civic Hybrid Estimated Mileage 17,000 Gas Mileage Average 48 Cost factor cost / gallon $3.20 Annual gas cost $950.00 Initial Price $19,900 Annual Gas Cost $8,000 Tax Saving ($700) Total Cost $27,300 Table 1: Hybrid Car Table 2: Gasoline Car 4. Research Questions The study questions for this study include: Does the vehicle model determine the age determine the cost, environmental safety, the mileage covered and the power generated? Which vehicle model is better than the other in the target market? 5. Hypothesis From the study questions, the hypotheses of this study include: The type or model of a vehicle determines the performance of the vehicles in terms of cost, environment safety, gas mileage and the power generated. Hybrid vehicles are better in performance than gasoline vehicles. 6. Data Analysis and Results 6.1. Data The data originated from the http://www.statcan.gc.ca/start-debut-eng.html library. The data is a sample is a historical data for the research carried out to determine the transport issues affecting the choice of vehicles to purchase among drivers. The sample size is 223. From the various analysis methods used, the research will emphasize on the regression and correlation coefficients among other functions. 6.2. Relevance The data is good because it contains realistic, measurable data from a real target. The sample size is sufficient and enough to generate the required analysis. The limitation with the data is that it does not have time variable, so time series is not measurable. 6.3. The Data The data to be used is shown in table 3 in the appendix section. The research uses various variables to represent the economic status of the population as shown below: Variable Description Explanation VM Vehicle Model 1 = Hybrid, 0 = Gasoline M Mileage Measured in Miles FC Fuel cost per gallon In US $ AGM Average Gas Mileage In Miles V Tax saving In % (30 % bracket) SP Starting Price In US $ EE Environmental Effect 0 to 5 rating 0 = no effect, 1 = little effect, 2 = Moderate effect, 3 = strong effect, 4 = very strong effect, 5 = critical effect on the environment Table 3: Variable Definition 6.4. Regression regress VM M FC AGM V SP EE note: AGM omitted because of collinearity note: V omitted because of collinearity Source SS df MS Number of obs = 223 F( 4, 218) = 50.82 Model 26.0784484 4 6.51961211 Prob > F = 0.0000 Residual 27.9663946 218 .128286214 R-squared = 0.4825 Adj R-squared = 0.4730 Total 54.044843 222 .243445239 Root MSE = .35817 VM Coef. Std. Err. t P>t [95% Conf. Interval] M .0000673 5.40e-06 12.46 0.000 .0000567 .000078 FC .000043 .0000109 3.95 0.000 .0000216 .0000644 AGM (omitted) V (omitted) SP -6.25e-06 5.26e-06 -1.19 0.237 -.0000166 4.13e-06 EE -.0410077 .0155126 -2.64 0.009 -.0715815 -.0104339 _cons .0000148 .0859339 0.00 1.000 -.1693528 .1693825 The results for linear regression The coefficient of association between the vehicle model and Mileage is 0000673 while that of FC is 000043. The rest of the variables are either in negative or no correlation with the vehicle models. Fig 3: Plot of Linear Regression The plotteg graph of the regression shows the coefficient of linear regression as 0.8. It shows that the milleage is directly proportional to the vehicle model variable. In this regard, the higher the value, the greazter is the milleage. Hybrid vehicles are thus better than ordinary gasoline vehicles. 6.5. Correlation correlate VM M FC AGM V SP EE (obs=223) | VM M FC AGM V SP EE -------------+--------------------------------------------------------------- VM | 1.0000 M | 0.6547 1.0000 FC | 0.1970 0.0164 1.0000 AGM | 0.6547 1.0000 0.0164 1.0000 V | 0.1970 0.0164 1.0000 0.0164 1.0000 SP | -0.0790 -0.0426 -0.0041 -0.0426 -0.0041 1.0000 EE | -0.2495 -0.2079 0.0471 -0.2079 0.0471 -0.0475 1.0000 Results of Correlation between Vehicle Models and all other variables The Coefficient of relationship is 0.6547 between vehicle model and mileage; this shows that there is a positive correlation and hybrid vehicles are higher in the rank. This shows that hybrid vehicles are better than gasoline vehicles. 6.6. Measures of Central Tendency mean VM M FC AGM V SP EE Mean estimation Number of obs = 223 -------------------------------------------------------------- | Mean Std. Err. [95% Conf. Interval] -------------+------------------------------------------------ VM | .4125561 .0330406 .3474427 .4776695 M | 6433.767 305.1455 5832.414 7035.119 FC | 3406.413 148.1778 3114.398 3698.428 AGM | 32.16883 1.525727 29.16207 35.1756 V | 340.6413 14.81778 311.4398 369.8428 SP | 6511.933 306.605 5907.704 7116.161 EE | 3.080717 .1064087 2.871017 3.290418 -------------------------------------------------------------- Results for Descriptive Statistical Summary All the means of each variable was shown above, with the confidence interval being 95%. 6.7. Effects of Outliers summarize VM M FC AGM V SP EE, detail VM Percentiles Smallest 1% 0 0 5% 0 0 10% 0 0 Obs 223 25% 0 0 Sum of Wgt. 223 50% 0 Mean .4125561 Largest Std. Dev. .4934017 75% 1 1 90% 1 1 Variance .2434452 95% 1 1 Skewness .3552508 99% 1 1 Kurtosis 1.126203 M Percentiles Smallest 1% 425 305 5% 990 310 10% 1114 425 Obs 223 25% 3056 425 Sum of Wgt. 223 50% 4964 Mean 6433.767 Largest Std. Dev. 4556.794 75% 9836 16656 90% 13524 17876 Variance 2.08e+07 95% 14888 18666 Skewness .8530747 99% 17876 24345 Kurtosis 3.246732 FC Percentiles Smallest 1% 800 780 5% 900 790 10% 990 800 Obs 223 25% 1450 830 Sum of Wgt. 223 50% 3170 Mean 3406.413 Largest Std. Dev. 2212.766 75% 4710 9870 90% 6180 9990 Variance 4896332 95% 7410 10000 Skewness .9913678 99% 9990 12970 Kurtosis 4.226528 AGM Percentiles Smallest 1% 2.125 1.525 5% 4.95 1.55 10% 5.57 2.125 Obs 223 25% 15.28 2.125 Sum of Wgt. 223 50% 24.82 Mean 32.16883 Largest Std. Dev. 22.78397 75% 49.18 83.28 90% 67.62 89.38 Variance 519.1092 95% 74.44 93.33 Skewness .8530747 99% 89.38 121.725 Kurtosis 3.246731 V Percentiles Smallest 1% 80 78 5% 90 79 10% 99 80 Obs 223 25% 145 83 Sum of Wgt. 223 50% 317 Mean 340.6413 Largest Std. Dev. 221.2766 75% 471 987 90% 618 999 Variance 48963.32 95% 741 1000 Skewness .9913678 99% 999 1297 Kurtosis 4.226528 SP Percentiles Smallest 1% 310 5 5% 1027 305 10% 1114 310 Obs 223 25% 3106 425 Sum of Wgt. 223 50% 5085 Mean 6511.933 Largest Std. Dev. 4578.589 75% 9656 16656 90% 14458 17876 Variance 2.10e+07 95% 14888 18666 Skewness .7258122 99% 17876 18666 Kurtosis 2.553027 EE Percentiles Smallest 1% 0 0 5% 0 0 10% 1 0 Obs 223 25% 2 0 Sum of Wgt. 223 50% 3 Mean 3.080717 Largest Std. Dev. 1.589021 75% 4 5 90% 5 5 Variance 2.524987 95% 5 5 Skewness -.3554039 99% 5 5 Kurtosis 1.946065 The gasoline vehicles represented by 0 were less than 50 %. This shows that hybrid vehicles are better than hybrid vehicles. summarize VM M FC AGM V SP EE Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- VM | 223 .4125561 .4934017 0 1 M | 223 6433.767 4556.794 305 24345 FC | 223 3406.413 2212.766 780 12970 AGM | 223 32.16883 22.78397 1.525 121.725 V | 223 340.6413 221.2766 78 1297 -------------+-------------------------------------------------------- SP | 223 6511.933 4578.589 5 18666 EE | 223 3.080717 1.589021 0 5 6.8. Cause and Effect Fig 5: Histogram Plotting The histogram shows evidence of outliers. The density for the third bar is 1.47 * 10-4 while the last is 2.0 * 10-5. The third bar in the graph is the outlier. Fig 6: Comparison of mean The bar graph in figure 6 above shows evidence of outliers caused by the difference between the scales of the variables. Three variables FC, M and SP are in the units of 1000 while that of the AGM, VM and EE are in the units of 10. xtmepoisson VM M FC AGM V EE SP, exposure(FC) M:, covariance(identity) note: AGM omitted because of collinearity note: V omitted because of collinearity Refining starting values: Iteration 0: log likelihood = -169.92994 Iteration 1: log likelihood = -150.05031 Iteration 2: log likelihood = -148.45344 Performing gradient-based optimization: Iteration 0: log likelihood = -148.45344 Iteration 1: log likelihood = -147.93637 Iteration 2: log likelihood = -147.93001 Iteration 3: log likelihood = -147.93001 Mixed-effects Poisson regression Number of obs = 223 Group variable: M Number of groups = 165 Obs per group: min = 1 avg = 1.4 max = 4 Integration points = 7 Wald chi2 (4) = 75.85 Log likelihood = -147.93001 Prob > chi2 = 0.0000 VM Coef. Std. Err. z P>z [95% Conf. Interval] M .0001339 .0000197 6.81 0.000 .0000954 .0001724 FC -.000158 .0000506 -3.12 0.002 -.0002571 -.0000589 AGM (omitted) V (omitted) EE -.138468 .0692581 -2.00 0.046 -.2742114 -.0027245 SP -7.09e-06 .0000232 -0.30 0.760 -.0000526 .0000385 _cons -8.965154 .405504 -22.11 0.000 -9.759927 -8.170381 regress VM M FC AGM V SP EE note: AGM omitted because of collinearity note: V omitted because of collinearity Source | SS df MS Number of obs = 223 -------------+------------------------------ F (4, 218) = 50.82 Model | 26.0784484 4 6.51961211 Prob > F = 0.0000 Residual | 27.9663946 218 .128286214 R-squared = 0.4825 -------------+------------------------------ Adj R-squared = 0.4730 Total | 54.044843 222 .243445239 Root MSE = .35817 ------------------------------------------------------------------------------ VM | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- M | .0000673 5.40e-06 12.46 0.000 .0000567 .000078 FC | .000043 .0000109 3.95 0.000 .0000216 .0000644 AGM | (omitted) V | (omitted) SP | -6.25e-06 5.26e-06 -1.19 0.237 -.0000166 4.13e-06 EE | -.0410077 .0155126 -2.64 0.009 -.0715815 -.0104339 _cons | .0000148 .0859339 0.00 1.000 -.1693528 .1693825 ------------------------------------------------------------------------------ 6.9. Hidden and Extraneous Variables AGM and V are the hidden and extraneous variables since they have been omitted by the analysis. The hidden and extraneous variables are omitted because they are not statistically significant as there is null correlation between them and the dependent variable, which is the vehicle model (VM). 6.10. Graphs Box Plot Fig 7: Box Plot Fig 8: Scattered Dots The box plot and the scattered dot matrix indicate that influence of each variable on the performance. Mileage is the most influential, showing that since hybrid vehicle drivers covers larger milleages, the drivers are the determinant of the vehicle mileages in this research. Fig 9: Scattered Dots between FC and M There Fuel Consumption is equally spread in the distribution such that there is no vehicle model that clearly consumes more than the other model. Pie Chart Pie chart The pie chart shows 1 (hybrid vehicles) to be taking the greatest percentage of influence in the target market. There are more drivers for hybrid vehicles than for gasoline vehicles. 7. Conclusion From the analysis, the gasoline vehicles and hybrid vehicles are all good to own. The only difference is made by the nature of the drivers. There is no better vehicle than the other since both of them demonstrate equal advantages and disadvantages. Looking at the fuel consumption, the rate of fuel usage is not dependent on the vehicle model but on the way it is driven. Works Cited Ehrenberg, Smith. Modern Labor Economics (10th international ed.). London: Addison-Wesley, 2008. Appendix VM M FC AGM V SP EE 0 12500 3070 62.5 307 6618 4 1 11789 3430 58.945 343 9299 2 1 11424 6400 57.12 640 4121 2 1 10220 1680 51.1 168 8645 1 1 15286 1170 76.43 117 1625 1 0 8352 3440 41.76 344 18666 2 1 15418 1970 77.09 197 2696 1 1 15484 1970 77.42 197 9656 2 0 8764 850 43.82 85 3167 4 0 3454 4370 17.27 437 5085 3 1 10220 6480 51.1 648 2336 2 1 15286 900 76.43 90 16656 1 0 6603 5130 33.015 513 4237 4 0 1159 3850 5.795 385 4418 5 0 1077 1010 5.385 101 8987 5 0 4604 1790 23.02 179 1027 4 0 8087 3110 40.435 311 8545 5 0 1297 6360 6.485 636 14888 4 0 4731 3260 23.655 326 4567 3 1 12404 5620 62.02 562 7876 2 1 10478 4020 52.39 402 14458 2 0 5300 3670 26.5 367 5 2 1 10903 4350 54.515 435 2265 2 0 6279 2330 31.395 233 7064 4 1 13306 1570 66.53 157 4235 3 0 5687 1030 28.435 103 5664 4 0 5367 3170 26.835 317 1114 4 0 9976 4480 49.88 448 4224 4 1 24345 1790 121.725 179 947 2 0 6776 1040 33.88 104 12449 4 0 5804 2010 29.02 201 15484 4 1 12144 3730 60.72 373 8764 3 1 10045 5730 50.225 573 3454 2 1 4845 2810 24.225 281 10220 0 1 6908 9610 34.54 961 15286 1 0 5258 1830 26.29 183 6603 0 1 10007 5870 50.035 587 1159 2 0 3059 2740 15.295 274 1077 4 0 3107 6180 15.535 618 4604 5 0 4256 3840 21.28 384 8087 5 0 10005 4040 50.025 404 1297 5 1 14343 1060 71.715 106 4731 3 0 2753 3980 13.765 398 12404 5 0 3530 2850 17.65 285 10478 5 1 5879 1180 29.395 118 5300 2 0 2747 4260 13.735 426 10903 5 1 6188 4500 30.94 450 6279 3 0 3849 3630 19.245 363 7876 5 0 4047 1590 20.235 159 14458 5 1 10665 3980 53.325 398 4517 3 1 3987 6590 19.935 659 2265 2 0 2858 3640 14.29 364 7064 5 0 1185 2840 5.925 284 4235 4 1 4264 6010 21.32 601 5664 2 1 4505 2040 22.525 204 1114 1 0 3636 4150 18.18 415 4224 5 0 1597 1980 7.985 198 947 4 0 3987 6610 19.935 661 12449 5 0 6597 2990 32.985 299 5755 4 1 9364 1210 46.82 121 9991 0 1 12284 860 61.42 86 3137 0 0 6019 1620 30.095 162 4553 1 0 2040 1860 10.2 186 4964 1 1 6415 2690 32.075 269 10015 2 0 4198 960 20.99 96 2536 5 0 6618 3160 33.09 316 4410 3 1 9299 5080 46.495 508 9620 1 0 4121 2330 20.605 233 7049 5 0 8645 1660 43.225 166 1220 4 0 1625 4230 8.125 423 2688 4 1 18666 4410 93.33 441 1827 2 0 2696 890 13.48 89 13524 2 0 9656 1020 48.28 102 4237 2 0 3167 850 15.835 85 1656 2 1 5085 4880 25.425 488 4567 4 0 2336 4560 11.68 456 7876 3 1 16656 780 83.28 78 14458 4 1 4237 4580 21.185 458 4517 4 0 4418 5170 22.09 517 14888 4 0 8987 2260 44.935 226 7064 2 0 1027 7060 5.135 706 4235 4 0 8545 4230 42.725 423 5664 4 1 14888 5660 74.44 566 1114 3 0 4567 1110 22.835 111 12897 2 0 7876 4220 39.38 422 4173 0 1 14458 940 72.29 94 4104 1 1 4517 4490 22.585 449 4178 0 0 2265 5750 11.325 575 1530 2 0 7064 990 35.32 99 1103 4 0 4235 3130 21.175 313 6127 5 0 5664 4550 28.32 455 3106 5 0 1114 4960 5.57 496 12120 5 0 4224 1010 21.12 101 2688 3 0 947 2530 4.735 253 1827 5 1 12449 4410 62.245 441 2524 5 0 5755 4620 28.775 462 14237 2 1 9991 7040 49.955 704 4418 5 0 3137 1220 15.685 122 2987 3 0 4553 2680 22.765 268 1027 5 0 4964 1820 24.82 182 8545 5 1 10015 5240 50.075 524 11656 3 0 2536 7660 12.68 766 4567 2 0 4410 3960 22.05 396 17876 5 1 9620 9870 48.1 987 14458 4 0 7049 5470 35.245 547 4517 2 0 1220 1060 6.1 106 14888 1 0 2688 3820 13.44 382 7064 5 0 1827 1410 9.135 141 4235 4 1 13524 3720 67.62 372 5664 5 0 4237 1440 21.185 144 1114 4 0 4418 980 22.09 98 12897 0 0 8987 3450 44.935 345 4731 0 0 1027 1150 5.135 115 4041 1 0 8545 1070 42.725 107 4782 1 0 1656 4600 8.28 460 14530 2 0 4567 800 22.835 80 1036 5 1 7876 12970 39.38 1297 6277 3 1 14458 4730 72.29 473 3064 1 1 4517 4040 22.585 404 5684 5 1 14888 4780 74.44 478 5363 4 1 7064 5300 35.32 530 990 4 0 4235 1030 21.175 103 2431 2 1 5664 6270 28.32 627 8412 2 0 1114 3060 5.57 306 580 2 1 12897 5680 64.485 568 1211 2 0 4173 5360 20.865 536 10014 4 0 4104 990 20.52 99 4843 3 0 4178 2430 20.89 243 4121 4 0 1530 8410 7.65 841 8645 4 0 1103 5800 5.515 580 1625 4 0 6127 1210 30.635 121 18666 2 0 3106 1000 15.53 100 2696 4 0 5168 4840 25.84 484 9656 4 1 9836 6900 49.18 690 3167 3 0 990 5250 4.95 525 5085 2 0 2431 9990 12.155 999 2336 0 0 8411 3050 42.055 305 16656 1 1 5809 3100 29.045 310 4237 0 0 1210 4250 6.05 425 4418 2 1 10089 10000 50.445 1000 8987 4 0 484 2190 2.42 219 1027 5 0 2907 1830 14.535 183 8545 5 1 12525 1060 62.625 106 14888 5 1 9999 4120 49.995 412 4567 3 0 305 1410 1.525 141 7876 5 0 310 1500 1.55 150 14458 5 0 425 1620 2.125 162 4517 2 1 10000 3980 50 398 2265 5 0 3545 6160 17.725 616 7064 3 1 14888 7690 74.44 769 4235 5 0 4567 8850 22.835 885 5664 5 1 7876 3900 39.38 390 1114 3 1 14458 4310 72.29 431 4224 2 1 4517 4710 22.585 471 947 5 0 2265 7570 11.325 757 12449 4 1 7064 2340 35.32 234 5755 2 0 4235 1980 21.175 198 9991 1 1 5664 8030 28.32 803 1027 5 0 1114 2260 5.57 226 8545 4 0 4224 1960 21.12 196 1656 5 0 947 890 4.735 89 4567 4 1 12449 1050 62.245 105 7876 0 0 5755 960 28.775 96 14458 0 1 9991 1020 49.955 102 4517 1 1 3137 3320 15.685 332 14888 1 0 4553 3480 22.765 348 7064 2 1 4964 4260 24.82 426 4235 5 1 10015 6810 50.075 681 5664 3 0 2536 5120 12.68 512 1114 1 1 4410 980 22.05 98 12897 5 1 9620 5190 48.1 519 4173 4 1 7049 930 35.245 93 4104 4 1 12120 930 60.6 93 4178 2 1 2688 1470 13.44 147 1530 2 0 1827 2380 9.135 238 1103 2 0 2524 1550 12.62 155 6127 2 1 14237 4850 71.185 485 3106 4 0 4418 1000 22.09 100 5168 3 0 2987 1530 14.935 153 9836 4 0 1027 3860 5.135 386 12500 4 1 8545 5690 42.725 569 11789 4 1 11656 5770 58.28 577 11424 2 0 4567 4120 22.835 412 10220 4 1 17876 4730 89.38 473 15286 4 1 14458 900 72.29 90 8352 3 0 4517 1770 22.585 177 15418 2 1 14888 4130 74.44 413 15484 0 1 7064 1270 35.32 127 8764 1 1 4235 5390 21.175 539 3454 0 1 5664 4570 28.32 457 10220 2 0 1114 4510 5.57 451 15286 4 1 12897 1750 64.485 175 6603 5 0 4731 1320 23.655 132 1159 5 0 4041 830 20.205 83 1077 5 0 4782 2080 23.91 208 4604 3 1 14530 1660 72.65 166 8087 5 0 1036 4040 5.18 404 1297 5 1 6277 4920 31.385 492 4731 2 0 3064 1010 15.32 101 12404 5 0 5684 790 28.42 79 10478 3 0 5363 3260 26.815 326 5300 5 0 990 1070 4.95 107 10903 5 0 2431 7410 12.155 741 6279 3 1 8412 1380 42.06 138 13306 2 0 580 1010 2.9 101 5687 5 0 1211 4310 6.055 431 5367 4 1 10014 960 50.07 96 305 2 0 4843 980 24.215 98 310 1 1 690 4830 3.45 483 425 5 0 525 1480 2.625 148 10000 4 1 999 900 4.995 90 3545 5 0 3056 1510 15.28 151 14888 4 1 12310 4660 61.55 466 4567 0 0 425 1050 2.125 105 7876 0 0 1000 4990 5 499 14458 1 1 988 4490 4.94 449 4517 1 0 4838 3190 24.19 319 2265 2 0 1488 1450 7.44 145 7064 5 1 9340 3580 46.7 358 4235 3 1 15145 1030 75.725 103 5664 1 1 14666 1970 73.33 197 1114 5 1 15466 1530 77.33 153 4224 4 0 4991 3090 24.955 309 947 4 Table 5 Read More