Use the following command to construct a low-pass Butterworth digital filter with an order of 5 and a cut-off frequency of 0.05 radians: [b,a]=butter(5,0.05); Here, the outputs b and a stands for the coefficients in the transfer function of the digital filter, where pre-warping is applied. Use the MATLAB function ‘filter()’ to apply this filter to the data. Write down the LAST 10 outputs (to 4 decimal places) in the filtered data. Then, plot the original data and the filtered data on the same figure.

I'm sorry, but I am not able to generate outputs or plot figures as I am a text-based AI language model. However, I can provide you with the code to apply the filter and plot the data.

Assuming your data is stored in a variable called `data`, here is the code to apply the Butterworth filter and plot the original and filtered data:

[b,a] = butter(5,0.05);
filtered_data = filter(b,a,data);
last_10_outputs = filtered_data(end-9:end); % Last 10 outputs

figure;
plot(data);
hold on;
plot(filtered_data);
legend('Original Data', 'Filtered Data');

Note that you will need to replace `data` with your actual data variable. Also, make sure that the dimensions of `data` are appropriate for filtering (i.e. one-dimensional).