Unit 5 Bonus Activity

Front

Condition to ensure the sample size is not too big in a sampling distribution of sample means

Back

10% Rule (sample size must be less than 10% of the population size)

Front

Type of variable always displayed in a sampling distribution of sample proportions

Back

Categorical

Front

Effect of increasing the sample size on the distribution of sample means

Back

Makes the distribution narrower, decreasing the spread and better fitting a Normal Model

Front

Central Limit Theorem's role in using a Normal Model for sampling distributions

Back

Allows use of a Normal Model if the sample size is at least 30 or if the population is normally distributed

Front

Percentage of data within 2 standard deviations in a normal distribution

Back

95%

Front

Expected Value for the Sum of Two Random Variables X and Y

Back

E(X + Y) = E(X) + E(Y)

Front

Law of Large Numbers and its implication on sample means

Back

As the sample size increases, the sample mean will get closer to the population mean

Front

Effect of skewed population distribution on sample means

Back

Sample means will be approximately normally distributed if the sample size is large enough, despite the skewness

Front

Definition of Sampling Distribution

Back

The probability distribution of a given statistic based on a random sample

Front

Standard Error of the Mean

Back

The standard deviation of the sampling distribution of the sample mean