Discrete Uniform & Bernoulli Distributions

Compare equal-mass finite models with zero-one event indicators. Compute means and variances, inspect Bernoulli CDFs, handle endpoint parameters and analyze transformed rewards or spaced supports. Prerequisites: PMFs, expectation and variance. Challenges remain elementary and focus on model assumptions, parameter ambiguity and consequences of coding. Includes single-select and multiselect questions with worked solutions and distractor feedback.

References: Notes, Notes, 3.2.4 Definition, computational formula and affine transformation, 3.2.2 Definition and linearity theorem.