Frequency bin of the positive and negative frequency
The tutorial you are looking at is attempting to model only the seasonality of a time-series.  They first look at power of different frequencies, in the second figure in the tutorial.  They find a lot of power for the 1/12 frequency, as well as a few other harmonics, and also the 1/72 frequency.  But they decide that since the 1/12 frequency has the most power, and most of the others are harmonics of this, they will only model the 1/12 frequency.  You can see this from this quote:


  Based on the spectral analysis of the data, fit a simple linear
  regression model using a cosine and sine term with a frequency of the
  most signficant component: 1 cycle/year (1 cycle/12 months).


So you therefore only need one frequency bin, and its negative, since you are only modeling one frequency.  In other words, in this part, they are just using essentially the equivalent of a single sine wave (at the 1/12 frequency) as their model to fit the data.  All the frequency bin variable does is to act as an index and select the component of the fft() that contains the 1/12 frequency.

They then analyze their result (the autocorrelation plots) and find that they have indeed missed some of the cyclical patterns at other frequencies they did not model.  So then they add three more frequencies to the model.  Rather than using frequency bins this time to do the indexing, they simply ask for the 6 highest in power (6 because each of the 3 needs both positive and negative frequencies) components of the fft() with these lines: 

[Y,I] = sort(abs(tsdft),'descend');
indices = I(1:6);
tsfit2dft(indices) = tsdft(indices);


You can see the resulting plot that follows now better models the time series since it includes more of the frequencies that showed increased power.