Almost all of these are issues with floating point in general, I'd say. Some are with naive implementations of Excel functions, fair enough (for example variance as (sum x_i^2/n - (sum x_i/n)^2) that are indeed better implemented in R or Julia.

IEEE 754 just has unintuitive properties.

Kahan (the "father of IEEE 754") has a rant (among many others) about Excel as well, and how it tries to hide some of the floating point complexities more or less successfully:

Floating-Point Arithmetic Besieged by “Business Decisions”

https://people.eecs.berkeley.edu/~wkahan/ARITH_17.pdf

But you also have issues like 'Catastrophic cancellation':

https://carolomeetsbarolo.wordpress.com/2012/07/20/catastrop...

Or:

"OOPS XL Did It Again"

https://carolomeetsbarolo.wordpress.com/2014/06/22/oops-xl-d...

From the Wikipedia article:

"Although Excel can display 30 decimal places, its precision for a specified number is confined to 15 significant figures, and calculations may have an accuracy that is even less due to five issues: round off,truncation, and binary storage, accumulation of the deviations of the operands in calculations, and worst: cancellation at subtractions resp. 'Catastrophic cancellation' at subtraction of values with similar magnitude."

That's an issue inherent in floating point computations with fixed precision (such as IEEE 754).

  julia> 1e20 + 1000 - 1e20
  0.0

  julia> 1e20 + 10000 - 1e20
  16384.0

  Python 3.9.6 (default, Jun 28 2021, 19:24:41) 
  >>> 1e20 + 1000 - 1e20
  0.0
  >>> 1e20 + 10000 - 1e20
  16384.0