data. I looked at just about every 3.5 inch hard drive on Amazon’s electronics page (well, the first the first 80 listed). I didn’t care if it was SATA or SATA 3 or whatever. Just so long as it was a 3.5 inch hard drive. Oh, I skipped some. For instance, check this sucker out.

This one is clearly more expensive than most, especially since it is only 300 GB – but I guess that is because it is a 15000 rpm speed. So, what if I plot price vs. size? Here is what I get:

The first thing you might see in this data is that the variation in price for the lower sized drives is huge. For example, there is a 20 GB hard drive for $7 and an 80 GB for $100. Why is this? Probably because when that 80 GB was first for sale, it was $100 and the price never changed. How about I average the prices for different sizes and just include the standard deviation of the price for that size as an error bar?

Note: if there was only one data point, I did not give it an error bar (for obvious reasons). Double note – this was a useful site explaining error bars in matplotlib. So, what if I want to fit a linear function to this data? Oh, yes I know. Maybe this isn’t the best choice – but really, I just fitting what I have.

This gives a fitting function of:

Here s is the size of the hard drive. A couple of cool things using this function. First, if you want a 0 GB drive, it would still cost you $52 – I guess that would be for the case and stuff. What if I use this function to determine the price of a 5 TB drive? Just put in s = 5000 and you get $233 – not too bad.

I guess this gets back to the real question. What size to buy? I will probably just go with the 1 TB. No one will ever need more than 1 TB, right?

Authors: Rhett Allain

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