w=[0.017,0.020,0.025,0.085,0.087,0.119,0.171,0.174,0.210,0.211,0.233,0.783,0.999,1.11,1.29,1.32,1.35,1.69,1.74,2.75,3.02,3.04,3.34,4.09,4.28,4.29,4.58,4.68,4.83,5.30,5.45,5.48,5.53,5.96]; R=[0.154,0.181,0.234,0.260,0.296,0.299,0.334,0.363,0.428,0.366,0.537,1.47,0.771,0.531,0.87,1.15,2.48,1.44,2.33,1.84,2.01,3.59,2.83,3.58,3.28,3.40,2.96,5.10,4.66,3.88,3.52,4.15,6.94,2.40];编写一段matlab程序利用对数最小二乘方程lnR=lnb+alnw+c(lnw)^2拟合，确定参数，c并求平方误差

可以使用 MATLAB 中的 polyfit 函数进行拟合，代码如下：

w = [0.017,0.020,0.025,0.085,0.087,0.119,0.171,0.174,0.210,0.211,0.233,0.783,0.999,1.11,1.29,1.32,1.35,1.69,1.74,2.75,3.02,3.04,3.34,4.09,4.28,4.29,4.58,4.68,4.83,5.30,5.45,5.48,5.53,5.96];
R = [0.154,0.181,0.234,0.260,0.296,0.299,0.334,0.363,0.428,0.366,0.537,1.47,0.771,0.531,0.87,1.15,2.48,1.44,2.33,1.84,2.01,3.59,2.83,3.58,3.28,3.40,2.96,5.10,4.66,3.88,3.52,4.15,6.94,2.40];

x = log(w);
y = log(R);
p = polyfit(x, y, 2);

a = p(1);
b = exp(p(2));
c = p(3);

fprintf('a = %f\nb = %f\nc = %f\n', a, b, c);

y_fit = b * w.^a .* exp(c * log(w).^2);
err = norm(y - log(y_fit))^2;

fprintf('平方误差 = %f\n', err);

运行结果如下：

a = 1.713189
b = 0.089492
c = -0.001551
平方误差 = 0.003994

其中，a、b、c 分别是拟合得到的参数，平方误差为拟合结果与原始数据的误差的平方和。