Linear model Poly2:
f(x) = p1*x^2 + p2*x + p3
Coefficients (with 95% confidence bounds):
p1 = -0.0002381 (-0.0003034, -0.0001729) p2 = 0.1013 (0.07535, 0.1273) p3 = 10.23 (8.295, 12.16)
Goodness of fit:
SSE: 9.541 R-square: 0.9249 Adjusted R-square: 0.9035 RMSE: 1.167 （2）三次多项式函数回归模型
Linear model Poly3:
f(x) = p1*x^3 + p2*x^2 + p3*x + p4 Coefficients (with 95% confidence bounds):
p1 = -4.955e-10 (-6.719e-07, 6.709e-07) p2 = -0.0002379 (-0.0006361, 0.0001604) p3 = 0.1013 (0.03699, 0.1656) p4 = 10.23 (7.641, 12.82)
Goodness of fit:
SSE: 9.541 R-square: 0.9249 Adjusted R-square: 0.8874 RMSE: 1.261 （3）单项正弦函数回归模型
General model Sin1:
f(x) = a1*sin(b1*x+c1)
Coefficients (with 95% confidence bounds): a1 = 21.17 (19.79, 22.54)
b1 = 0.004981 (0.004422, 0.00554) c1 = 0.511 (0.3986, 0.6235)
Goodness of fit:
SSE: 8.367 R-square: 0.9341 Adjusted R-square: 0.9153 RMSE: 1.093 （4）两项正弦函数回归模型
General model Sin2:
f(x) = a1*sin(b1*x+c1) + a2*sin(b2*x+c2) Coefficients (with 95% confidence bounds): a1 = 21.4 (20.28, 22.53)
b1 = 0.005007 (0.004152, 0.005862) c1 = 0.5232 (0.3647, 0.6817) a2 = -1.294 (-2.077, -0.511) b2 = 0.02706 (0.01633, 0.03779) c2 = -0.3913 (-2.352, 1.569)
Goodness of fit:
SSE: 1.127 R-square: 0.9911 Adjusted R-square: 0.98 RMSE: 0.5307 建立表格比较各参数的拟合量，其中系数只比较方差和修改后的拟合优度
分类 对象 N的施用量同土豆产量的函数关系 回归模型 二次多项式 三次多项式 单项正弦 两项正弦 N的施用量同生菜产量的函数关系 二次多项式 三次多项式 单项正弦 两项正弦 经比较应选择两项正弦。 三、P对作物生长的影响 1.P对土豆生长的影响
（1）二次多项式函数回归模型
方差（SSE） 11.33 6.914 10.32 3.436 9.541 9.541 8.367 1.127 改进后的拟合优度（AR-s） 0.9824 0.9875 0.984 0.9906 0.9035 0.8874 0.9153 0.98
Linear model Poly2:
f(x) = p1*x^2 + p2*x + p3
Coefficients (with 95% confidence bounds):
p1 = -0.0001378 (-0.0002491, -2.654e-05) p2 = 0.07186 (0.03322, 0.1105) p3 = 32.92 (30.41, 35.42)
Goodness of fit:
SSE: 16.09 R-square: 0.8645 Adjusted R-square: 0.8258 RMSE: 1.516 （2）三次多项式函数回归模型
Linear model Poly3:
f(x) = p1*x^3 + p2*x^2 + p3*x + p4
Coefficients (with 95% confidence bounds):
p1 = 6.498e-07 (-4.929e-07, 1.793e-06) p2 = -0.0004684 (-0.00106, 0.0001229) p3 = 0.1141 (0.03088, 0.1973) p4 = 32 (29.09, 34.92)
Goodness of fit:
SSE: 12.17 R-square: 0.8975 Adjusted R-square: 0.8463 RMSE: 1.424 （3）单项正弦函数回归模型
General model Sin1:
f(x) = a1*sin(b1*x+c1)
Coefficients (with 95% confidence bounds): a1 = 42.3 (40.59, 44.02)
b1 = 0.002603 (0.001565, 0.003641) c1 = 0.8942 (0.7748, 1.014)
Goodness of fit:
SSE: 16.39 R-square: 0.862 Adjusted R-square: 0.8226 RMSE: 1.53 （4）两项正弦函数回归模型