PROBABILITY AND STATISTICAL
INFERENCE 10TH EDITION ROBERT
HOGG ELLIOT TANIS DALE ZIMMERMAN
SOLUTIONS MANUAL ALL CHAPTERS 100
PERCENT ORIGINAL SCRIPT UPDATED
⫸ Sampling distribution. Answer: All the statistics from all possible
samples of the same size.
⫸ Sampling error. Answer: The difference between any sample statistic
and the population parameter.
⫸ Randomization condition. Answer: Your sample must be
representative of the population.
⫸ Independence or 10% condition. Answer: Either each subject is
independent or the population size must be at least 10 times the sample
size.
⫸ Large sample/normal condition. Answer: n must be large - how you
check it depends on the type of inference
, ⫸ Central limit theorem. Answer: The mean of a random sample is a
random variable whose sampling distribution can be approximately
normal as long as the sample is large enough.
⫸ Standard error. Answer: When we estimate the standard deviation of
a sampling distribution.
⫸ Confidence level. Answer: How likely we are to capture the true
parameter when taking many samples of the same size.
⫸ One-proportion z-interval. Answer: Confidence interval created to
estimate the true proportion.
⫸ Two-proportion z-interval. Answer: Confidence interval created to
estimate the difference in true proportions.
⫸ One-proportion z-test. Answer: Hypothesis test where the null
hypothesis is a single claimed proportion.
⫸ Two-proportion z-test. Answer: Hypothesis test where the null
hypotheses is the difference between two proportions.
⫸ Margin of error. Answer: The extent of the interval on either side of
the estimate.
INFERENCE 10TH EDITION ROBERT
HOGG ELLIOT TANIS DALE ZIMMERMAN
SOLUTIONS MANUAL ALL CHAPTERS 100
PERCENT ORIGINAL SCRIPT UPDATED
⫸ Sampling distribution. Answer: All the statistics from all possible
samples of the same size.
⫸ Sampling error. Answer: The difference between any sample statistic
and the population parameter.
⫸ Randomization condition. Answer: Your sample must be
representative of the population.
⫸ Independence or 10% condition. Answer: Either each subject is
independent or the population size must be at least 10 times the sample
size.
⫸ Large sample/normal condition. Answer: n must be large - how you
check it depends on the type of inference
, ⫸ Central limit theorem. Answer: The mean of a random sample is a
random variable whose sampling distribution can be approximately
normal as long as the sample is large enough.
⫸ Standard error. Answer: When we estimate the standard deviation of
a sampling distribution.
⫸ Confidence level. Answer: How likely we are to capture the true
parameter when taking many samples of the same size.
⫸ One-proportion z-interval. Answer: Confidence interval created to
estimate the true proportion.
⫸ Two-proportion z-interval. Answer: Confidence interval created to
estimate the difference in true proportions.
⫸ One-proportion z-test. Answer: Hypothesis test where the null
hypothesis is a single claimed proportion.
⫸ Two-proportion z-test. Answer: Hypothesis test where the null
hypotheses is the difference between two proportions.
⫸ Margin of error. Answer: The extent of the interval on either side of
the estimate.
