Download INFERENCE FOR ONE SAMPLE

In all cases need SRS and N>10n
Sampling Distribution of the Sample Mean x-bar -  known
Mean is

Std. Dev.
 /(sqrt n)
Requirements for Normality of the Sampling Distribution
Population normal or n> 30
Sampling Distribution of the Sample Mean x-bar - NOT known
Mean is

Std. Dev.
s/(sqrt n) where s is the standard deviation of the sample
The sampling distribution is the t-distribution with n-1 degrees of freedom
Requires: Population normal or n> 30
Sampling Distribution of k, the Number of Successes (in a binomial situation)
Mean
np, where n = sample size and p is the population proportion
Std. Dev.
sqrt (npq)
Requirements for Normality of the Sampling Distribution
np, nq > 10
Sampling Distribution of the Proportion of Successes p-hat = k/n
Mean
p, the population proportion
Std. Dev.
sqrt (pq/n)
Requirements for Normality of the Sampling Distribution
np, nq > 10
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A 90% confidence level interval has endpoints at the 5th and 95th percentiles.
A 95% confidence level interval has endpoints at the 2.5th and 97.5th percentiles.
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INVNORM (percentile) gives critical z values
INVT(percentile, df) gives critical t values.
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