Sure - it's just a matter of understanding that none of 0.6, 0.6f, 0.7 and 0.7f are those exact values. They're the closest representable approximations in the appropriate type. The exact values which are stored for those 4 values are:
0.6f => 0.60000002384185791015625
0.6 => 0.59999999999999997779553950749686919152736663818359375
0.7f => 0.699999988079071044921875
0.7 => 0.6999999999999999555910790149937383830547332763671875
With that information, it's clear why you're getting the results you are.
To think of it another way, imagine you had two decimal floating point types, one with 4 digits of precision and one with 8 digits of precision. Now let's look at how 1/3 and 2/3 would be represented:
1/3, 4dp => 0.3333
1/3, 8dp => 0.33333333
2/3, 4dp => 0.6667
2/3, 8dp => 0.66666667
So in this case the lower-precision value is smaller than the higher-precision one for 1/3, but that's reversed for 2/3. It's the same sort of thing for float and double, just in binary.
