603916股票分析

这个函数如何用MATLAB做回归分析？

%频率f=200HZ时的功率损耗P频率f=200HZ时的磁通密度B

f200=[...

00

0.02020.0986

0.24220.1965

0.56690.297

0.98560.386

1.52670.5033

1.98570.5986

2.55010.7047

3.26470.8005

3.83490.9005

4.58411.0088

5.38281.1121]

%频率f=400HZ时的功率损耗P频率f=400HZ时的磁通密度B

f400=[...

00

0.30210.1213

1.53890.2906

3.64750.465

5.73690.5813

8.58280.7034

12.07550.8138

15.32020.9301

17.42340.9882

20.09791.046

24.14861.1626]

%频率f=600HZ时的功率损耗P频率f=600HZ时的磁通密度B

f600=[...

00

0.40410.0961

2.14450.2019

4.36970.3022

7.17240.4029

10.08140.509

13.68330.6039

16.62880.7104

19.4060.793

22.32780.8662

30.05741.0157

36.94841.1188]

%频率f=800HZ时的功率损耗P频率f=800HZ时的磁通密度B

f800=[...

00

0.81240.1023

7.5770.3383

9.83960.4006

12.3890.4579

15.04810.5049

18.150.5659

20.78240.6016

22.25160.6333

26.27240.7165

31.03760.8022

37.00170.9184

38.83580.9626]

%频率f=1000HZ时的功率损耗P频率f=1000HZ时的磁通密度B

f1000=[...

00

1.10480.0976

2.68260.1506

4.58320.1994

7.42590.252

9.71120.2931

12.41440.3522

16.03660.3991

19.6610.4662

21.4650.4981

24.59580.548

29.02720.6154

33.93050.6844

43.20310.8]

f=[ones(size(f200),1)*200;ones(size(f400),1)*400;ones(size(f600),1)*600;ones(size(f800),1)*800;ones(size(f1000),1)*1000]

P=[f200(:,1);f400(:,1);f600(:,1);f800(:,1);f1000(:,1)]

B=[f200(:,2);f400(:,2);f600(:,2);f800(:,2);f1000(:,2)]

X=[f,B];

%P=a*f*B*X+b*f2*B2+c*f1.5*B1.5

y=inline('a(1)*x(:,1).*x(:,2)+a(2)*x(:,1).^2.*x(:,2).^2+a(3)*x(:,1).^1.5.*x(:,2).^1.5','a','x')

BETA0=[111]

formatshortG

BETA=nlinfit(X,P,y,BETA0)

[Py(BETA,X)(P-y(BETA,X))./P]%实际值、预测值、误差

结果：

BETA=

-0.030741-6.732e-0050.0048601

ans=

00NaN

0.0202-0.2067811.237

0.2422-0.114691.4736

0.56690.161460.71519

0.98560.522260.47011

1.52671.13180.25863

1.98571.72130.13316

2.55012.46220.034473

3.26473.19830.020338

3.83494.0268-0.050036

4.58414.9857-0.087615

5.38285.9539-0.1061

00NaN

0.3021-0.00743691.0246

1.53891.608-0.044893

3.64754.2819-0.17394

5.73696.4446-0.12336

8.58288.9588-0.043807

12.07611.4040.055621

15.3214.1220.078229

17.42315.5250.10894

20.09816.9480.15674

24.14919.8850.17654

00NaN

0.40410.131610.67431

2.14451.76820.17546

4.36974.07920.066491

7.17246.90180.037722

10.08110.272-0.01889

13.68313.5440.01015

16.62917.435-0.048508

19.40620.574-0.060213

22.32823.424-0.049089

30.05729.3820.022478

36.94833.5580.091771

00NaN

0.81240.631570.22258

7.5778.3884-0.10708

9.839611.118-0.12989

12.38913.781-0.11233

15.04816.054-0.06684

18.1519.101-0.052416

20.78220.927-0.0069522

22.25222.57-0.01429

26.27226.958-0.026102

31.03831.56-0.016844

37.00237.864-0.023311

38.83640.266-0.036826

00NaN

1.10481.04460.054445

2.68262.8259-0.053405

4.58324.8784-0.064399

7.42597.42060.00071124

9.71129.59440.01203

12.41412.947-0.042873

16.03715.7590.017338

19.66119.96-0.015183

21.46522.014-0.025591

24.59625.285-0.02803

29.02729.784-0.026063

33.93134.447-0.015226

43.20342.2950.021016

a*X合并为一个常数