Probability and Statistics Mean Random Variable

Eva flips a coin. If she gets heads, she wins $4. If she gets tails, she loses $3. What is her expected value of a coin flip?

• $1

• $0.50

• - $0.50

• $0

In a certain community, 30% of households have 1 child, 43% have 2 children, and 27% have 3 children. If a household is selected at random, what is the expected number of children in that household?

HINT: It may be helpful to make a probability distribution table.

• 2.00

• 2.13

• 1.97

• 1.27

It is the characteristic or attribute of a sample or population that changes or varies for different individuals or things.

• Random Variable

• Variable

• Statistics

• Sample Space

A variable whose value is determined by a random experiment.

• random experiment

• random variable

• statistics

• probability

The following is a property of the bell curve except:

• it is asymmetric

• The mean, mode and median are all equal

• The total area under the curve is 1

• it is asymptotic

It deals with predicting the likelihood of future events,

• random experiment

• random variable

• statistics

• probability

What is a random variable?

• A random variable is a variable that only takes on values from a specific set of outcomes.

• A random variable is a constant that represents a specific event in a process.

• A random variable is a fixed number that does not change.

• A random variable is a variable that takes on numerical values determined by the outcomes of a random process.

Explain what a probability distribution is.

• A probability distribution is a fixed set of outcomes that cannot change.

• A probability distribution is a method to calculate the average of a dataset.

• A probability distribution is a graphical representation of data points.

• A probability distribution is a function that describes the likelihood of different outcomes for a random variable.

What is the shape of a normal curve?

• Triangular and uneven.

• Circular and distorted.

• Bell-shaped and symmetric.

• Square and flat.

Describe the Central Limit Theorem in relation to sample means.

• The Central Limit Theorem states that the distribution of sample means approaches a normal distribution as the sample size increases.

• Sample means will always be normally distributed regardless of sample size.

• The Central Limit Theorem applies only to populations with a normal distribution.

• The Central Limit Theorem states that sample means are always equal to the population mean.