Be able to
.       define and distinguish between samples and populations
.       describe these probability sampling designs: simple random sample,
        stratified random sample, multistage sample
.       understand the problems or biases involved in: voluntary response
        sampling, undercoverage, non-response and response bias due to
        interviewer, respondent or wording of question
.       understand the difference between
                obtaining the probability of an event from a known population
                (section 4.1) AND
                making a statistical inference from a sample statistic to a
                population parameter (section 3.4)
.       know the concepts of sampling distributions and unbiased
                statistics
.       know the rule about the sample size needing to be 1/10
                population size
.       be able to define: sample space, event
.       know 5 probability rules
.       draw a Venn diagram with complements, intersections and unions
.       be able to
                write out a table of probabilities for a discrete
                distribution and calculate means, variances and standard
                deviations from the table
.       define random variable
.       draw a probability histogram
.       be able to calculate probabilities under a discrete histogram or a
        continuous distribution.
.       know the law of large numbers, the rules for combining means and
        variances, and know the general probability rules
.       know how to add the probabilities of many disjoint events
.       know how to calculate conditional probabilities of events which are not
        independent and which are independent
.       know the multiplication rule
.       learn the Bayes Rule and how to apply it in a problem.
.       know about sampling distributions versus population distributions
.       know the 4 criteria for a binomial setting
.       know what B(n,p) means; know what N(mu,sigma) means
.       know the mu and sigma parameters
                for a distribution of counts and
                for a distribution of proportions
.       know how to apply the normal approximation
.       know when to apply the normal approximation (np>10; n(1-p)>10)
.       be able to calculate the binomial probability of an outcome
                combining simpler events.
.       if the population is normal, and you know the mean and std.
                dev. for the random variable in the population, what is the
                distribution of the sample means?  What if it isn't
                normal but n is large?  (know central limit theorem)
.       be able to calculate confidence intervals, know how to find z* from a
        table, given confidence level
.       be able to define confidence intervals
.       be conceptually familiar with what is happening in a confidence interval
.       be able to calculate sample size for a margin of error
        and margin of error for a given sample size (knowing
        confidence level and standard deviation)
.       be able to pick out null and alternative hypotheses in an experimental
        setting
.       be conceptually familiar with and able to define statistical significance,
.       Be familiar with its uses and abuses
.       be able to conduct a z test for a null hypothesis with one mean.
.       be familiar with how to conduct a two-sided test, and how
        to use a confidence interval to do a two-sided test
.       be familiar with how to decide when to reject a null hypothesis using a
        confidence interval, comparing p to alpha, or comparing the value of Z or t to
        a critical value
.       be familiar with type I errors, type II errors and power.
.       know how to increase power in an experiment
.       know that the difference between z and t tests is in whether you know
        sigma ahead of time or have to use the sample standard deviation as an
        estimate of the population standard deviation
.       be able to do a one-sample t-test of a null hypothesis, know how to get
        degrees of freedom from sample size, how to use the table
.       be able to do a one-sample confidence interval for mu when you don't know
        sigma