Independent random samples of 40 Yorkshire litters and 50 Landrace litters are obtained.  For Yorkshires, the mean litter size is 9.0 pigs and the standard deviation of litter size is 2.0 pigs.  For Landrace, the mean litter size is 8.5 pigs and the standard deviation of litter size is 1.8 pigs.  Construct an 80% confidence interval for the difference in population mean litter sizes of Yorkshire and Landrace.

Independent random samples of litter sizes were selected from the Yorkshire and Landrace breeds of swine, with the following results: Yorkshire Landrace 8 7 9 8 10 9 8 8 9 7 10   We want to analyze these data using Analysis of Variance with a Completely Randomized Design. Which one of the following is not an assumption of this Analysis of Variance with a Completely Randomized Design?

A __________ – tailed hypothesis test is one in which the alternative hypothesis is directional and includes the less than or greater than symbol.

The normal approximation to a binomial probability distrubtion is reasonably good even for small sample sizes (say, n as small as 10) when p = 1.0 and the distribution of X is therefore symmetric about its mean.

If we roll a single die, the sample points are a 1, 2, 3, 4, 5, or 6.  Consider the following two events:        Event A:  toss an even number        Event B:  toss a number less than or equal to 2 List the sample points that would be included in the union of events A and B.

It would be appropriate to construct a large-sample confidence interval for (p1 – p2) as long as both n1 and n2 are ≥ 30.

The ________ hypothesis is the theory that generally represents the status quo, which we accept until it is proven false.

Consider the following discrete probability distribution:                                                                x         -2         -1         0          1           2 P(x)   0.10     0.08     0.25     0.31     0.26 Calculate the mean (μ) of this discrete probability distribution.

The value of a population parameter (e.g., the mean, μ) is a constant; its value does not change; in other words, it does not vary from sample to sample.

Suppose x has a binomial probability distribution with n = 150 and p = 0.60.  Use the normal approximation to the binomial to find P(X>100).