Example 3 Lectures
Lack of precision

Normalize 747
  1 bit sign 7 bit exponent 8 bit significand

  Covert to binary   

  747           747 - 1 * 512 = 235  msb
  373 1  lsb    235 - 0 * 256 = 235    
  186 1         235 - 1 * 128 = 107
   93 0         107 - 1 *  64 =  43
   46 1          43 - 1 *  32 =  11
   23 0          11 - 0 *  16 =  11
   11 1          11 - 1 *   8 =   3
    5 1           3 - 0 *   4 =   3
    2 1           3 - 1 *   2 =   1
    1 0           1 - 1 *   1 =   0  lsb
    0 1  msb
 
  Scientific notation 1011101011 -> 1. 0111 0101 1 x 2 ^ 9
  
  Bias 2^(7-1)-1 = 63    9 + 63 = 72

   72
   36 0
   18 0
    9 0
    4 1
    2 0
    1 0
    0 1    biased exponent   1001000

   Note the number of significant digits is larger than significand
     storage, you must truncate or round.

    1.0111 0101 1  -> truncate -> 1.0111 0101
    1.0111 0101 1  -> round -> 1.0111 0110

  Float - truncated
   0   1001000  01110101
  sign  exp.     sig.

  Float - rounded
   0   1001000  01110110
  sign  exp.     sig.

Conversion back.
    
   Exponent  
    1 0 0 1  0  0  0
    1 2 4 9 18 36 72   72 - 63(bias) = 9 exponent


   Trucated
     Significand  01110101   -> 1.01110101 x 2 ^9 - hidden bit restored
       1011101010
 
     1 0 1  1  1  0  1   0   1   0 
     1 2 5 11 23 46 93 186 373 746


     512  256  128  64  32  16  8  4  2  1
       1    0    1   1   1   0  1  0  1  0 
     512   +0 +128 +64 +32  +0 +8 +0 +2 +0
          512      192      32     8     2
                   704            40     2
                                  744    2
                                       746 
     
   Rounded
     Significand  01110110   -> 1.01110110 x 2 ^9 - hidden bit restored
       1011101100

     1 0 1  1  1  0  1   1   0   0 
     1 2 5 11 23 46 93 187 374 748

     512  256  128  64  32  16  8  4  2  1
       1    0    1   1   1   0  1  1  0  0 
     512   +0 +128 +64 +32  +0 +8 +4 +0 +0
          512      192      32    12     0
                   704            44     0
                                       748 

A float can store a much larger value than an integer but you risk loosing
precision even on more modest values.