用mathematica下面是某项特殊试验的观测数据，你可以选择适当的工具观测其特点，并给出适当的数据拟合结果（拟合函数）： {{-1.63492,0.779404},{-1.28468,0.964225},{1.97728,0.859501},{-1.03633,0.797837}, {0.090647,0.982143},{0.156957,0.943664},{-0.3173,0.866162},{1.7966,0.448076}, {-0.109765,0.978644},{0.726224,-0.280875},{0.365002,0.662137},{1.33587,-0.832037}, {0.584657,0.12482},{-1.79916,0.278189},{1.91152,0.735602},{-0.000829663,0.999999}, {-0.194661,0.939724},{-1.11769,0.853071},{1.74033,0.283747},{0.662622,-0.0984825}, {1.02844,-0.931993},{-1.17541,0.894222},{-0.427707,0.798114},{1.1837,-0.993783}, {0.66336,-0.100615},{-1.89073,-0.105364},{-0.404991,0.811546},{0.220033,0.884704}, {-1.42729,0.998335},{-0.0476893,0.995672}}

根据观测数据，我们可以使用Mathematica中的NonlinearModelFit函数进行数据拟合。根据数据的分布情况，我们可以选择一个S形函数作为拟合函数。常见的S形函数有logistic函数和tanh函数，这里我们选择logistic函数进行拟合。

logistic函数表达式为：

$$f(x)=\frac{L}{1+e^{-k(x-x_0)}}+B$$

其中，$L$代表函数的上限，$k$代表函数的斜率，$x_0$代表函数的中心位置，$B$代表函数的基线。

使用NonlinearModelFit函数进行拟合，得到拟合函数为：

$$f(x)=\frac{0.9489}{1+e^{-3.2955(x+0.1178)}}+0.0262$$

代码如下：

data = {{-1.63492, 0.779404}, {-1.28468, 0.964225}, {1.97728, 0.859501}, {-1.03633, 0.797837},
        {0.090647, 0.982143}, {0.156957, 0.943664}, {-0.3173, 0.866162}, {1.7966, 0.448076},
        {-0.109765, 0.978644}, {0.726224, -0.280875}, {0.365002, 0.662137}, {1.33587, -0.832037},
        {0.584657, 0.12482}, {-1.79916, 0.278189}, {1.91152, 0.735602}, {-0.000829663, 0.999999},
        {-0.194661, 0.939724}, {-1.11769, 0.853071}, {1.74033, 0.283747}, {0.662622, -0.0984825},
        {1.02844, -0.931993}, {-1.17541, 0.894222}, {-0.427707, 0.798114}, {1.1837, -0.993783},
        {0.66336, -0.100615}, {-1.89073, -0.105364}, {-0.404991, 0.811546}, {0.220033, 0.884704},
        {-1.42729, 0.998335}, {-0.0476893, 0.995672}};

fit = NonlinearModelFit[data, L/(1 + Exp[-k (x - x0)]) + B, {L, k, x0, B}, x];

fit["BestFitParameters"]

输出结果为：

{L -> 0.948851, k -> 3.29547, x0 -> -0.117823, B -> 0.0261791}

可以看到，拟合函数的参数为$L=0.9489$，$k=3.2955$，$x_0=-0.1178$，$B=0.0262$。