请问MMA如何做回归分析？

Y X1 X2 X3 X4
5.88 1 1 1 1
2.56 6 1 1 1
11.11 1 1 1 1
0.79 6 1 1 1
0 6 1 1 1
0 0 1 1 1
15.6 8 1 1 1
3.7 4 1 1 1
8.49 3 1 1 1
51.2 6 1 1 1
14.2 7 1 1 1
7.14 5 1 1 1
4.2 7 1 1 1
6.15 4 1 1 1
10.46 6 1 1 1
0 8 1 1 1
10.42 2 1 1 1
17.36 5 1 1 1
13.41 8 1 1 1
41.67 0 1 1 1
2.78 0 1 1 1
2.98 8 1 1 1
9.62 7 1 1 1
0 0 1 1 1
4.65 5 1 0 2
3.13 3 1 0 2
24.58 6 1 0 2
0 1 1 0 2
5.56 4 1 0 2
9.26 3 1 0 2
0 0 1 0 2
0 0 1 0 2
3.13 1 1 0 2
0 0 1 0 2
7.56 5 0 1 3
9.93 6 0 1 3
0 8 0 1 3
16.67 6 0 1 3
16.89 7 0 1 3
13.71 6 0 1 3
6.35 5 0 1 3
2.5 3 0 1 3
2.47 7 0 1 3
21.74 3 0 1 3
23.6 8 0 0 4
11.11 8 0 0 4
0 7 0 0 4
3.57 8 0 0 4
2.9 5 0 0 4
2.94 3 0 0 4
2.42 8 0 0 4
18.75 4 0 0 4
0 5 0 0 4
2.27 3 0 0 4

回归统计
Multiple R 0.218341811
R Square 0.047673146
Adjusted R Square -0.009466465
标准误差 10.13673187
观测值 54
-----------------------------------------------------------------------
方差分析
　 df SS MS F Significance F
回归分析 3 257.1898 85.72993 0.834327 0.481381
残差 50 5137.667 102.7533
总计 53 5394.856
------------------------------------------------------------------------
　 Coefficients 标准误差 t Stat P-value
Intercept 4.656067 4.956396 0.939406 0.35203869
X Variable 1 0.386247 0.565168 0.683419 0.49749677
X Variable 2 0 0 65535 #NUM!
X Variable 3 3.591779 3.741181 0.960066 #NUM!
X Variable 4 -0.11539 1.579488 -0.07306 0.94205222

mathematica的结果
{
{"", "DF", "SS", "MS", "F\[Hyphen]Statistic", "P\[Hyphen]Value"},
{x1, 1, 81.1052, 81.1052, 0.773533, 0.383419},
{x2, 1, 18.3613, 18.3613, 0.175119, 0.67743},
{x3, 1, 157.723, 157.723, 1.50427, 0.225877},
{x4, 1, 0., 0., 0., 1.},
{"Error", 49, 5137.67, 104.85, , },
{"Total", 53, 5394.86, , , }
}
并不能剔除错误数据，R和minitab就可以，有什么方法验证数据么？
剔除错误数据的结果
{
{"", "DF", "SS", "MS", "F\[Hyphen]Statistic", "P\[Hyphen]Value"},
{x1, 1, 81.1052, 81.1052, 0.789319, 0.37856},
{x2, 1, 18.3613, 18.3613, 0.178693, 0.674311},
{x3, 1, 157.723, 157.723, 1.53497, 0.221153},
{"Error", 50, 5137.67, 102.753, , },
{"Total", 53, 5394.86, , , }
}
具体项x1,x2,x3还是和minitab和R不太一样
minitab结果
Regression Analysis: y versus x1, x2, x3, x4
The following terms cannot be estimated and were removed:
x4
Analysis of Variance
Source DF Adj SS Adj MS F-Value P-Value
Regression 3 257.19 85.730 0.83 0.481
x1 1 47.99 47.992 0.47 0.497
x2 1 0.55 0.548 0.01 0.942
x3 1 157.72 157.723 1.53 0.221
Error 50 5137.67 102.753
Lack-of-Fit 21 1073.88 51.137 0.36 0.990
Pure Error 29 4063.79 140.131
Total 53 5394.86
R结果
Residuals: Min 1Q Median 3Q Max -11.222 -5.821 -2.546 3.171 40.750 Coefficients: (1 not defined because of singularities) Estimate Std. Error t value Pr(>|t|)(Intercept) 4.1945 3.9749 1.055 0.296x1 0.3862 0.5652 0.683 0.497x2 0.2308 3.1590 0.073 0.942x3 3.7072 2.9922 1.239 0.221x4 NA NA NA NAResidual standard error: 10.14 on 50 degrees of freedomMultiple R-squared: 0.04767,Adjusted R-squared: -0.009466 F-statistic: 0.8343 on 3 and 50 DF, p-value: 0.4814