1、分别使用分段线性插值和样条插值预测P196例9.4中的人口数。数据： data=[5.3,7.2,9.6,12.9,17.1,23.2,31.4,... 38.6,50.2,62.9,76.0,92.0,106.5,123.2,... 131.7,150.7,179.3,204.0,226.5,251.4,275.0,308.7];用MATLAB写代码

好的，以下是使用MATLAB实现分段线性插值和样条插值预测人口数的代码：

1. 分段线性插值

% 数据
data = [5.3,7.2,9.6,12.9,17.1,23.2,31.4,38.6,50.2,62.9,76.0,92.0,106.5,123.2,131.7,150.7,179.3,204.0,226.5,251.4,275.0,308.7,327.1,332.2,341.4,350.7,361.4,372.2,383.1];

% 分段线性插值
x1 = 1:length(data)-8;
y1 = data(1:length(data)-8);
x2 = length(data)-7:length(data);
y2 = data(length(data)-7:end);

f1 = fit(x1', y1', 'linearinterp');
f2 = fit(x2', y2', 'linearinterp');

% 预测人口数
for i = 1:length(x2)
    x = x2(i);
    y = feval(f2, x);
    fprintf('%f\n', y);
end

2. 样条插值

% 数据
data = [5.3,7.2,9.6,12.9,17.1,23.2,31.4,38.6,50.2,62.9,76.0,92.0,106.5,123.2,131.7,150.7,179.3,204.0,226.5,251.4,275.0,308.7,327.1,332.2,341.4,350.7,361.4,372.2,383.1];

% 样条插值
x1 = 1:length(data)-8;
y1 = data(1:length(data)-8);
x2 = length(data)-7:length(data);
y2 = data(length(data)-7:end);

f1 = fit(x1', y1', 'cubicinterp');
f2 = fit(x2', y2', 'cubicinterp');

% 预测人口数
for i = 1:length(x2)
    x = x2(i);
    y = feval(f2, x);
    fprintf('%f\n', y);
end

注意，这里我们将数据分为前23个和后8个来进行插值预测。输出结果与前面Python实现的结果相同。