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[ÇóÖú] MATLABÖÐfitlmº¯Êý²úÉúµÄ²ÎÊýµÄº¬Òå ÒÑÓÐ1È˲ÎÓë

fitlmº¯Êý²úÉúµÄ²ÎÊýÎÊÌâ
1.pÖµÊÇÿ¸ö×Ô±äÁ¿ÔÚ·½³ÌÖÐËùÕ¼µÄÖØÒªÐÔÂ𣿻»ÑÔÖ®£¬¿ÉÒÔ¸ù¾ÝpÖµ´óС±È½Ï¸÷×Ô±äÁ¿¶Ô·½³ÌµÄÓ°Ïì³Ì¶ÈÂð£¿
2.F-statistic vs constant model: 4.35£¬ÕâÊǸöʲôÒâ˼
¿ÉÄÜÎÊÌâ±È½Ï°×³Õ£¬µ«ÊÇÕæµÄÊǼ±ÇóÓÐÈ˽â´ð£¬¹òл£¬¸½ÉÏÎÒµÄÔËËã³ÌÐò
>> clear
x1=[34 28.001 28 28 28 22.001 22 22 22 22 16.001 16 16 16 10.001 10 10 10 10 4.001 4 4 4 4];
x2=[12 6 6 6.001 6 1 1 1.001 1 1 15 15 15.001 15 9 9 9.001 9 9 3 3 3.001 3 3];
x3=[8 2 2 2 2.001 10 10 10 10.001 10 4 4 4 4.001 12 12 12 12.001 12 6 6 6 6.001 6];
y=[9.78 14.12 14.34 9.08 12.49 9.84 12.86 11.36 14.96 12.33 7.19 10.29 12.64 9.45 16.53 13.64 15.58 15.71 15.27 13.58 17.71 14.00 9.51 14.72]';
x=[x1',x2',x3'];
fitlm(x, y,'quadratic');
>>
>> ans

ans =


Linear regression model:
    y ~ 1 + x1*x2 + x1*x3 + x2*x3 + x1^2 + x2^2 + x3^2

Estimated Coefficients:
                   Estimate      SE       tStat      pValue  
                   ________    ______    _______    _________

    (Intercept)      60641      18548     3.2694    0.0055927
    x1             -1509.4     847.11    -1.7818     0.096477
    x2             -3103.3     2782.3    -1.1153      0.28348
    x3              -11908     3742.8    -3.1816    0.0066603
    x1:x2          -73.932     64.765    -1.1415       0.2728
    x1:x3           193.36     76.495     2.5277     0.024137
    x2:x3           336.27     153.26     2.1941     0.045605
    x1^2            19.076     30.905    0.61723      0.54699
    x2^2             139.7     91.156     1.5325      0.14768
    x3^2            411.14     141.57      2.904      0.01155


Number of observations: 24, Error degrees of freedom: 14
Root Mean Squared Error: 1.79
R-squared: 0.737,  Adjusted R-Squared 0.567
F-statistic vs. constant model: 4.35, p-value = 0.00719

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2Â¥2017-02-18 21:19:56
ÒÑÔÄ   »Ø¸´´ËÂ¥   ¹Ø×¢TA ¸øTA·¢ÏûÏ¢ ËÍTAºì»¨ TAµÄ»ØÌû

FMStation

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https://www.mathworks.com/help/stats/fitlm.html

mdl = fitlm(tbl) returns a linear model fit to variables in the table or dataset array tbl. By default, fitlm takes the last variable as the response variable.

mdl = fitlm(tbl,modelspec) returns a linear model of the type you specify in modelspec fit to variables in the table or dataset array tbl.

mdl = fitlm(X,y) returns a linear model of the responses y, fit to the data matrix X.

mdl = fitlm(X,y,modelspec) returns a linear model of the type you specify in modelspec for the responses y, fit to the data matrix X.

mdl = fitlm(___,Name,Value) returns a linear model with additional options specified by one or more Name,Value pair arguments.
3Â¥2017-02-19 11:12:02
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