When.com Web Search

Search results

  1. Results From The WOW.Com Content Network
  2. Numeric precision in Microsoft Excel - Wikipedia

    en.wikipedia.org/wiki/Numeric_precision_in...

    Here the 'IEEE 754 double value' resulting of the 15 bit figure is 3.330560653658221E-15, which is rounded by Excel for the 'user interface' to 15 digits 3.33056065365822E-15, and then displayed with 30 decimals digits gets one 'fake zero' added, thus the 'binary' and 'decimal' values in the sample are identical only in display, the values ...

  3. Single-precision floating-point format - Wikipedia

    en.wikipedia.org/wiki/Single-precision_floating...

    If a decimal string with at most 6 significant digits is converted to the IEEE 754 single-precision format, giving a normal number, and then converted back to a decimal string with the same number of digits, the final result should match the original string. If an IEEE 754 single-precision number is converted to a decimal string with at least 9 ...

  4. Double-precision floating-point format - Wikipedia

    en.wikipedia.org/wiki/Double-precision_floating...

    Double-precision floating-point format (sometimes called FP64 or float64) is a floating-point number format, usually occupying 64 bits in computer memory; it represents a wide range of numeric values by using a floating radix point. Double precision may be chosen when the range or precision of single precision would be insufficient.

  5. Half-precision floating-point format - Wikipedia

    en.wikipedia.org/wiki/Half-precision_floating...

    Thus, only 10 bits of the significand appear in the memory format but the total precision is 11 bits. In IEEE 754 parlance, there are 10 bits of significand, but there are 11 bits of significand precision (log 10 (2 11) ≈ 3.311 decimal digits, or 4 digits ± slightly less than 5 units in the last place).

  6. Floating-point arithmetic - Wikipedia

    en.wikipedia.org/wiki/Floating-point_arithmetic

    For example, the decimal numbers 0.1 and 0.01 cannot be represented exactly as binary floating-point numbers. In the IEEE 754 binary32 format with its 24-bit significand, the result of attempting to square the approximation to 0.1 is neither 0.01 nor the representable number closest to it. The decimal number 0.1 is represented in binary as e ...

  7. Decimal floating point - Wikipedia

    en.wikipedia.org/wiki/Decimal_floating_point

    The advantage of decimal floating-point representation over decimal fixed-point and integer representation is that it supports a much wider range of values. For example, while a fixed-point representation that allocates 8 decimal digits and 2 decimal places can represent the numbers 123456.78, 8765.43, 123.00, and so on, a floating-point ...

  8. decimal64 floating-point format - Wikipedia

    en.wikipedia.org/.../Decimal64_floating-point_format

    Decimal64 supports 'normal' values that can have 16 digit precision from ±1.000 000 000 000 000 × 10 ^ −383 to ±9.999 999 999 999 999 × 10 ^ 384, plus 'denormal' values with ramp-down relative precision down to ±1.×10 −398, signed zeros, signed infinities and NaN (Not a Number). This format supports two different encodings.

  9. Positional notation - Wikipedia

    en.wikipedia.org/wiki/Positional_notation

    Another common way of expressing the base is writing it as a decimal subscript after the number that is being represented (this notation is used in this article). 1111011 2 implies that the number 1111011 is a base-2 number, equal to 123 10 (a decimal notation representation), 173 8 and 7B 16 (hexadecimal).