chapter 15: Sampling distributions

Question 1

parameter vs. statistic

Answer 1

A parameter is a number that describes a population (in practice hard to know). A statistic is a number that can be computed from the sample data (without making use of any unknown parameters).

s is for statistics and samples

p is for parameters and populations

Question 2

μ, going forward

Answer 2

Mu is for the population mean

Question 3

σ, going forward

Answer 3

sigma is now for the population standard deviation

Question 4

x̄, going forward

Answer 4

x̄ is now the mean of the sample

Question 5

s, going forward

Answer 5

s is now the standard deviation of the sample

Question 6

the law of large numbers

Answer 6

as you draw more and more observations from a population through random sampling, the mean x̄ of each sample gradually approaches the magical mean μ of the total population. In other words, The law of large numbers states that as the sample size increases, the values of the sample mean gets closer and closer to the population mean.

Question 7

simulation

Answer 7

using software to simulate chance behavior

Question 8

population distribution

Answer 8

a population’s mean μ and standard deviation σ

Question 9

sample distribution

Answer 9

the distrubution of values taken by a statistic in all possible samples of the same size from the same population. Population dist. describes individuals, but sample dist. describes the numbers ie stats.

Question 10

as the sample size increases, the variability ________

Answer 10

decreases. The standard deviation of this sampling distribution is less than the standard deviation of its population distribution, because it becomes more accurate, and is divided by the square root.

Question 11

Because the sample mean is a _______ variable, we model its behavior with a probability model.

Answer 11

random