STAT Module 2 Part II.

Random variables are

• Variables that are always constant
• Values that cannot change
• Variables that can take on different values with certain probabilities.
• Numbers that are predetermined

A probability model for a random variable consists of

• Probabilities without associated values
• Possible values and their associated probabilities
• Possible values without probabilities
• Only the highest probability value

A probability model has two characteristics

• It must assign probabilities to outcomes and the probabilities must sum up to 100%.
• It must assign probabilities to outcomes and the probabilities must sum up to 0.
• It must assign probabilities to outcomes and the probabilities must sum up to 1.
• It must assign probabilities to outcomes and the probabilities must sum up to 0.5.

Another term for our expected value (μ) is

• mode
• population mean

• population median

• range

The binomial model is

• A historical timeline of ancient civilizations
• A cooking recipe for baking cakes
• A mathematical formula for calculating prime numbers
• A probability distribution that calculates the likelihood of a specific outcome in a series of trials.

Mark all choices that apply.

Assumptions for the binomial model is

• There are two possible outcomes.

• The probability of a success, p, is constant.

• There are a fixed number of trials.

• There are a fixed number of events.

• The trials are independent.

• the number of successes in a fixed number of trials
• the number of failures in a fixed number of trials
• the total number of trials
• the probability of success in each trial

• number of trials

• number of parameters
• number of successes
• number of variables

• the number of trials in the experiment
• the expected value of the random variable
• the probability of success for a single trial
• the standard deviation of the random variable

• probability of a failure

• probability of a success

• population size

• population standard deviation