A randomized block design is used to compare postweaning average daily gains of 4 breeds of beef cattle, Hereford, Angus, Charolais, and Simmental (we can think of the breeds at the “treatments”).  The breeds are divided into 3 weight classes (i.e., 3 blocks).  Block 1 contains cattle weighing 450 to 500 lb at the beginning of the experiment, block 2 contains cattle weighing 500 to 550 lb at the beginning of the experiment, and block 3 contains cattle weighing 550 to 600 lb at the beginning of the experiment.  The postweaning average daily gains (in pounds per day) are as follows:  Block Hereford Angus Charolais Simmental 1 3.50 3.60 3.70 3.75 2 3.55 3.63 3.71 3.80 3 3.56 3.62 3.80 3.90 The partially completed ANOVA table for this experiment is as follows: Source df SS MS F Total   .160     Breed   .139 .046 46 Block   .014 .007   Error   .007 .001   What are the correct degrees of freedom for total, breed, block, and error, respectively?

Find the probability of an observation lying more than z = 0.5 standard deviations below the mean.

Frame score in beef cattle is based on height at the hips and is used as a measure of skeletal size.  Frame scores range from 1 to 10 with a higher number indicating a taller animal.  Independent random samples of frame scores were selected from the Angus and Simmental breeds of beef cattle with the following results: Angus Simmental 5 7 6 7 7 8 5 6 7 7 6   In the Analysis of Variance table, the degrees of freedom for total, breeds, and error, respectively, are:

A random sample of n = 200 observations is selected from a binomial population.  The sample estimate of the proportion of successes is 0.13.  Using a significance level of α = 0.10, we want to test: Ho:  p = 0.10 Ha:  p > 0.10 Assuming that we can use large-sample procedures, calculate the value of the test statistic needed to test these hypotheses.

The weights (in pounds) of a sample of 6 dogs are as follows: Dog Weight 1 30 2 32 3 26 4 42 5 40 6 46 Find the mean of the weights of this sample of 6 dogs.

A survey was conducted to determine how people rate the quality of programming available on TV.  Twenty-one respondents were asked to rate the overall quality from 0 (no quality at all) to 100 (extremely good quality). The stem-and-leaf display based on these data (using the first digit as the stem and the second digit as the leaf) is shown below: Stem                       Leaves                                     3               2     5 4               0     3     4     7     8     9 5               1     1     2     3     4     5 6               1     2     5     6     7 7               7     8 Based on the stem-and-leaf display, the median for these TV ratings is __________.

Breeders of the Longhorn breed of cattle select to increase the length of the horns (i.e., the distance from the tip of one horn to the tip of the other horn).  A Longhorn breeder would like to know the average length of horns found on Longhorn cattle in Texas.  A random sample of 144 Longhorn cattle yields a mean horn length of 72 inches and a standard deviation of 15 inches. Estimate the population mean for length of horns of Longhorn cattle in Texas using a 95% confidence interval.

The partially completed ANOVA table for a 3 x 4 factorial experiment (i.e., there are 3 levels of factor A and 4 levels of factor B) with two replications is shown below: Source          df          SS          MS          F    Total                          18.1 Factor A                     0.8 Factor B                     5.3 A x B                           9.6 Error                                                                 What are the degrees of freedom for total, factor A, factor B, the A x B interaction, and error?

A farm supply store manager wants to predict monthly sales, Y, for her company using advertising expenditures, X.  She has collected 10 months of data on past performance as shown in the following table. Advertising expenditures, in thousands of dollars, X Monthly sales, Y XY X2 Y2 1.2 101 121.2 1.44 10,201 0.8 92 73.6 0.64 8,464 1.0 110 110.0 1.00 12,100 1.3 120 156.0 1.69 14,400 0.7 90 63.0 0.49 8,100 0.8 82 65.6 0.64 6,724 1.0 93 93.0 1.00 8,649 0.6 75 45.0 0.36 5,625 0.9 91 81.9 0.81 8,281 1.1 105 115.5 1.21 11,025 TOTAL 9.4 959 924.8 9.28 93,569 Find the Y-intercept of the regression line for the regression of Y on X.

Assume X is a binomial random variable.  If n = 5 and p = 0.10, find P (r > 1 success).