实验运行程序、基本步骤及运行结果：
1.根据数据文件估计北京市人均住房面积的影响模型，并进行相应分析。
（1）.首先，要确定因变量和自变量，根据题目，
因变量为：
人均住房面积y
自变量为：
人均全年收入x1
人均可支配收入x2
城镇储蓄存款余额x3
人均储蓄余额x4
国内生产总值x5
人均生产总值x6
基本投资额x7
人均基本投资额x8
（2）.然后利用SPSS进行多元线性回归分析,得到结果为：
模型汇总b
模型
R
R方
调整R方
标准估计的误差
Durbin-Watson
1
.994a
.988
.981
.24634
1.681
a.预测变量:
（常量）,x8,x7,x3,x6,x1,x2,x4。
b.因变量:
y
分析：
根据拟合出来的模型可以知道，可决系数为0.988，调整后的可决系数为0.981.说明解释变量解释了被解释变量变异程度的98.1%，进而可以说明模型的拟合效果好。
Anovab
模型
平方和
df
均方
F
Sig.
1
回归
59.608
7
8.515
140.325
.000a
残差
.728
12
.061
总计
60.336
19
a.预测变量:
（常量）,x8,x7,x3,x6,x1,x2,x4。
b.因变量:
y
分析：
这是对于模型的整体显著性检验（F检验），根据结果可以看出F检验统计量为140.325，概率P值为0.000<0.05，说明模型通过了显著性检验，模型的拟合是有效的。
已排除的变量b
模型
BetaIn
t
Sig.
偏相关
共线性统计量
容差
VIF
最小容差
1
x5
10.462a
1.469
.170
.405
1.809E-5
55278.779
1.780E-5
a.模型中的预测变量:
（常量）,x8,x7,x3,x6,x1,x2,x4。
b.因变量:
y
分析：
根据多元线性回归模型的建立，将变量x5排除，它与模型中的其他解释变量存在很严重的多重共线性。
系数a
模型
非标准化系数
标准系数
t
Sig.
共线性统计量
B
标准误差
试用版
容差
VIF
1
（常量）
3.964
.241
16.477
.000
x1
.000
.001
-.956
-.817
.430
.001
1361.278
x2
-.001
.001
-2.180
-2.195
.049
.001
980.463
x3
.001
.002
.749
.627
.542
.001
1418.704
x4
.000
.000
-2.480
-2.067
.061
.001
1431.296
x6
.001
.000
5.155
6.301
.000
.002
665.397
x7
3.285E-7
.000
.349
2.505
.028
.052
19.316
x8
.000
.000
.330
.972
.350
.009
114.391
a.因变量:
y
分析：
这是对于模型的系数显著性检验（t检验），根据结果可以看出,常数项的P值为0.000<0.05，即是通过了显著性检验；x1的P值为0.43>0.05，没有通过显著性检验；x2的P照顾为0.049<0.05，通过了显著性检验；x3的P值为0.542>0.05,即是没有通过显著性检验；x4的P值为0.061>0.05，没有通过显著性检验；x6的P值为0.000<0.05，通过了显著性检验；x7的P值为0.052>0.05,没有通过显著性检验；x8的P值为0.009<0.05，通过了显著性检验。
再根据方差扩大因子可以看出x1,x2,x3,x4,x6,x8存在多重共线性，只有x7不存在多重共线性。
共线性诊断a
模型
维数
特征值
条件索引
方差比例
（常量）
x1
x2
x3
x4
x6
x7
x8
1
1
7.444
1.000
.00
.00
.00
.00
.00
.00
.00
.00
2
.484
3.923
.09
.00
.00
.00
.00
.00
.00
.00
3
.045
12.870
.00
.00
.00
.00
.00
.00
.45
.00
4
.023
18.096
.21
.00
.00
.00
.00
.00
.01
.08
5
.003
48.783
.30
.01
.01
.02
.02
.06
.37
.19
6
.001
99.386
.00
.14
.00
.07
.17
.17
.10
.03
7
.000
144.498
.09
.04
.95
.02
.00
.29
.05
.12
8
.000
239.240
.31
.80
.04
.89
.81
.48
.02
.58
a.因变量:
y
残差统计量a
极小值
极大值
均值
标准偏差
N
预测值
5.3141
11.1214
7.8620
1.77123
20
残差
-.41181
.38168
.00000
.19577
20
标准预测值
-1.438
1.840
.000
1.000
20
标准残差
-1.672
1.549
.000
.795
20
a.因变量:
y
（3）.利用岭回归法对模型进行修正
岭回归法就是用过增加一个偏倚量c，使得模型估计更加稳定和显著。
在SPSS中岭回归的实现：
新建一个syntax窗口，调入岭回归语句（引号内为该文件实际所在路径）：
Include"d:
\Ridgeregression.sps".
岭回归命令格式：
ridgeregenter=自变量列表
/dep=因变量
/start=c初始值，默认为0
/stop=c终止值，默认为1
/inc=渐进步长，默认0.05）
/k=c指定偏倚系数，输出详细回归结果.
最后一定要有一个点.
输入ridgeregenter=x1x2x3x4x6x7x8/dep=y/inc=0.01.
点运行按钮run。
得到结果为：
R-SQUAREANDBETACOEFFICIENTSFORESTIMATEDVALUESOFK
KRSQx1x2x3x4x6x7x8
____________________________________________________________________
.00000.98793-.955631-2.18005.748792-2.479815.154638.349141.329859
.01000.94831.378142.176599-.612495-.4981011.173739.185817.140657
.02000.93217.308957.200793-.400480-.301644.779982.112638.242594
.03000.92303.270773.197581-.290430-.203683.608333.085146.273692
.04000.91693.246958.192037-.221381-.143939.510876.073335.282129
.05000.91246.230606.186853-.173260-.103246.447625.068238.281821
.06000.90897.218606.182354-.137464-.073540.403059.066384.277872
.07000.90614.209373.178488-.109634-.050802.369855.066208.272429
.08000.90378.202011.175147-.087294-.032788.344093.066928.266472
.09000.90176.195980.172235-.068922-.018140.323481.068126.260469
.10000.90001.190929.169671-.053524-.005982.306587.069571.254643
.11000.89847.186626.167394-.040419.004278.292467.071127.249094
.12000.89710.182904.165354-.029124.013054.280476.072714.243863
.13000.89588.179646.163513-.019285.020647.270154.074287.238957
.14000.89477.176764.161841-.010636.027280.261166.075818.234368
.15000.89376.174190.160313-.002974.033125.253263.077291.230079
.16000.89283.171875.158908.003862.038311.246253.078698.226069
.17000.89197.169776.157611.009996.042943.239989.080036.222318
.18000.89118.167863.156407.015531.047103.234353.081304.218805
.19000.89045.166108.155285.020549.050859.229252.082503.215509
.20000.88976.164491.154236.025117.054264.224610.083636.212414
.21000.88911.162995.153252.029293.057364.220365.084705.209501
.22000.88850.161603.152325.033124.060197.216467.085713.206756
.23000.88792.160304.151449.036648.062795.212871.086664.204165
.24000.88738.159088.150620.039902.065183.209544.087561.201715
.25000.88686.157946.149833.042913.067386.206453.088407.199395
.26000.88636.156870.149084.045706.069423.203573.089205.197194
.27000.88588.155853.148370.048304.071311.200883.089958.195104
.28000.88543.154890.147687.050725.073064.198362.090669.193116
.29000.88499.153975.147033.052985.074695.195994.091340.191221
.30000.88457.153105.146406.055100.076216.193764.091975.189415
.31000.88416.152276.145802.057082.077637.191660.092574.187689
.32000.88376.151483.145222.058942.078966.189671.093141.186039
.33000.88338.150724.144662.060690.080210.187786.093676.184458
.34000.88301.149997.144122.062336.081378.185997.094183.182944
.35000.88264.149298.143599.063888.082475.184296.094662.181490
.36000.88229.148626.143093.065353.083507.182675.095116.180094
.37000.88194.147979.142603.066736.084478.181130.095546.178751
.38000.88160.147355.142127.068045.085394.179654.095952.177458
.39000.88127.146752.141665.069285.086258.178241.096338.176212
.40000.88095.146169.141215.070460.087073.176889.096702.175011
.41000.88063.145604.140778.071574.087844.175591.097048.173851
.42000.88031.145057.140351.072633.088573.174345.097375.172731
.43000.88000.144526.139936.073639.089263.173148.097685.171648
.44000.87970.144011.139530.074595.089916.171995.097979.170599
.45000.87939.143510.139133.075506.090535.170884.098257.169584
.46000.87910.143023.138746.076373.091123.169813.098520.168600
.47000.87880.142548.138367.077200.091680.168779.098770.167646
.48000.87851.142085.137996.077988.092209.167780.099006.166720
.49000.87822.141634.137632.078740.092711.166813.099229.165820
.50000.87794.141193.137276.079458.093188.165878.099441.164946
.51000.87765.140763.136926.080144.093642.164972.099641.164096
.52000.87737.140342.136583.080799.094073.164094.099830.163269
.53000.87709.139931.136247.081426.094484.163241.100009.162464
.54000.87681.139528.135916.082026.094874.162414.100178.161679
.55000.87653.139133.135591.082599.095245.161610.100337.160915
.56000.87626.138747.135271.083148.095598.160828.100488.160169
.57000.87598.138368.134956.083674.095935.160067.100630.159442
.58000.87571.137996.134646.084178.096255.159327.100763.158732
.59000.87544.137631.134341.084661.096560.158606.100889.158039
.60000.87517.137273.134041.085124.096850.157903.101007.157361
.61000.87489.136921.133745.085568.097126.157217.101118.156699
.62000.87462.136575.133453.085993.097390.156548.101222.156051
.63000.87435.136234.133165.086402.097640.155895.101319.155417
.64000.87408.135900.132881.086793.097879.155257.101410.154796
.65000.87381.135570.132600.087169.098106.154634.101495.154189
.66000.87355.135246.132324.087530.098322.154024.101574.153594
.67000.87328.134926.132050.087876.098527.153428.101647.153011
.68000.87301.134611.131780.088209.098723.152844.101715.152439
.69000.87274.134301.131513.088528.098909.152273.101778.151878
.70000.87247.133995.131250.088835.099086.151713.101836.151328
.71000.87220.133694.130989.089129.099254.151165.101889.150788
.72000.87193.133396.130731.089412.099413.150627.101938.150258
.73000.87166.133102.130476.089684.099565.150100.101982.149738
.74000.87139.132812.130224.089945.099709.149583.102021.149227
.75000.87112.132526.129974.090195.099845.149075.102057.148724
.76000.87085.132243.129727.090436.099974.148577.102089.148230
.77000.87058.131964.129482.090667.100097.148088.102116.147745
.78000.87031.131688.129240.090889.100213.147607.102141.147267
.79000.87004.131415.129000.091102.100322.147135.102161.146798
.80000.86976.131145.128762.091307.100426.146670.102179.146335
.81000.86949.130878.128527.091503.100523.146214.102193.145880
.82000.86922.130614.128294.091692.100615.145764.102203.145432
.83000.86894.130353.128062.091873.100702.145322.102211.144991
.84000.86867.130095.127833.092047.100783.144887.102216.144556
.85000.86840.129839.127606.092213.100860.144459.102218.144128
.86000.86812.129586.127380.092373.100931.144038.102217.143706
.87000.86784.129335.127157.092526.100998.143622.102213.143290
.88000.86757.129087.126935.092673.101060.143213.102207.1
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