Download 2. Remember our assumptions for Hypothesis tests

Test 3 Review
Vocabulary and symbolic notation:
 Point estimate of the population mean  of a statistic used to estimate the value
of a parameter.
 Confidence interval- is an interval of numbers obtained from a point estimate and
a percentage that specifies how confident we are that a parameter lies in an
interval.
 Confidence level-confidence percentage
 Margin of Error (E)
 T-distribution, degrees of freedom
Assumptions for Confidence Intervals:
 Point estimate of the population mean   value of a statistic used to estimate
the parameter.
 Normal populations or large samples.
 All samples are simple random samples
Chapter 8
 Know that X is a point estimate of 
 Know how to compute (1   )  100% Confidence Interval for 
 Remember our assumptions for CI
 Normal populations or large samples.
 All samples are simple random samples
1. When  is known (Z-interval)
2. When  is unknown (T-interval)
 Interpret the Confidence Interval
 What is the margin of error for a given, CI, know how it changes with
increased sample size. Also know how the confidence level affects the
Confidence Interval.
 Estimate the sample size for given E and confidence level.
Chapter 9
1. Know new vocabulary and symbolic notation:
 Null and alternative hypotheses
 Two - tailed, left-tailed, right-tailed alternative hypothesis
 rejection region, critical value(s)
 test statistics
 significance level of the test (  )
 p-value
 Type I and II errors
2. Remember our assumptions for Hypothesis tests:
 Normal populations or large samples.
 All samples are simple random samples
 Sigma is or is not known.
One Mean z-test
 Know how to test Ho:  =  0 versus appropriate alternative hypothesis
 Known population standard deviation
Know how to compute the test statistic,

Know how to interpret hypothesis test for  0

One Mean t-test
 Know how to test Ho:  =  0 versus appropriate alternative hypothesis
 Calculated sample standard deviation
Know how to compute and interpret CI for  0

Chapter 10
1. Non-pooled t-test:
 Know how to test Ho: 1  2 versus appropriate alternative hypothesis (for
independent samples)
 Unknown population standard deviations (not assumed equal)
 (2 samples approximate t- test, use df=estimated by formula)
 Know how to compute and interpret CI for 1  2 (non-pooled case only)
 Know connection between two-(tailed) sided hypothesis test and confidence
interval for 1  2
 If CI contains 0, we can't reject Ho: 1  2 for two tailed test (at the given  )
2. Paired data t-test:
 Know how to test Ho: 1  2 versus appropriate alternative hypothesis (for
dependent samples)
 Standard deviation for paired data
 (2 samples approximate t- test, use df=number of pairs - 1)
 Know how to compute and interpret CI for 1  2
 Know connection between two-(tailed) sided hypothesis test and confidence
interval for 1  2
 If CI contains 0, we can't reject Ho: 1  2 for two tailed test (at the given  )