Question 1

1. An experiment has been conducted involving a single factor. The experiment is a completely randomized design. The ANOVA output for this experiment is shown below.

How many treatments (levels of the single factor) were used in this experiment?

4

3

5

None of the above

Question 2

1. An experiment has been conducted involving a single factor. The experiment is a completely randomized design. The ANOVA output for this experiment is shown below.

The computed value of the F-statistic is:

12.55

9.17

5.75

None of the above

Question 3

1. An experiment has been conducted involving a single factor. The experiment is a completely randomized design. The ANOVA output for this experiment is shown below.

The F-statistic is significant at the 0.05 level.

True

False

Question 4

1. An experiment has been conducted involving a single factor. The experiment is a completely randomized design. The ANOVA output for this experiment is shown below.

The actual P-value for this test statistic is

greater than 0.05

between 0.05 and 0.01

less than 0.01

None of the above

Question 5

1. An experiment has been conducted involving a single factor. The experiment is a completely randomized design. The ANOVA output for this experiment is shown below.

Suppose that the actual P-value is 0.002. This means that the probability that the null hypothesis is false is 0.002.

True

False

Question 6

1. The ANOVA tests the null hypothesis that all treatment means are equal against the alternative that all of the treatment means are different.

True

False

Question 7

1. The ANOVA provides specific information about which treatment means are different.

True

False

Question 8

1. An experiment has been conducted involving a single factor. The experiment is a completely randomized design. The ANOVA output for this experiment is shown below.

Suppose that the experiment had been conducted as a randomized complete block design (RCBD). Obviously, the ANOVA display above would look slightly different. In this new RCBD ANOVA the number of degrees of freedom for Blocks would be:

4

3

5

None of the above

Question 9

1. An experiment has been conducted involving a single factor. The experiment is a completely randomized design. The ANOVA output for this experiment is shown below.

If the experiment had been conducted as an RCBD the number of degrees of freedom for residual (or error) in the new ANOVA table would be:

10

6

9

None of the above

Question 10

1. The square root of the residual mean square in the ANOVA is an estimate of the standard error of a treatment mean.

True

False

Question 11

1. The basic ANOVA treats both quantitative and qualitative factors identically so far as the sums of squares calculations are concerned.

True

False

Question 12

1. Blocking is an experimental design technique that can be used with both controllable and uncontrollable nuisance variables.

True

False

Question 13

1. If there is a strong interaction in a factorial experiment, the individual main effects involved in that interaction may not have much practical meaning.

True

False

Question 14

1. By coding the observations in the ANOVA table we never change the numerical values of the sum of squares.

True

False

Question 15

1. The design technique that is used to guard against unknown and uncontrollable factors is randomization.

True

False

Question 16

1. The RCBD places a restriction on complete randomization of the experiment.

True

False

Question 17

1. An interaction effects twists the response surface plane for the underlying model and results in curvature to the response contour lines.

True

False

Question 18

1. The basic assumptions in the ANOVA are that the observations are drawn from independent normal populations with common variance.

True

False

Question 19

1. The ANOVA is very sensitive to the assumption of normality, and cannot be relied on to produce reliable results if applied to nonnormal data.

True

False

Question 20

1. The residuals from an ANOVA are always:

Normally distributed

Statistically independent

Have a mean of zero

All of the above

None of the above

Question 21

1. The residuals from an ANOVA can be used to check whether the observations are

Approximately normally distributed

Have constant variance

Are statistically independent

None of the above

All of the above

Question 22

1. An unreplicated 24 factorial experiment has been run in a chemical process. The response variable is molecular weight. The engineer has used the following four factors:

After conducting the experiment, the engineer finds that three of the main effects and one of the interactions are large enough to be considered significant. The effect estimates are A = 40, B = 36, C = 50, and AC = 28. The overall average of the 16 runs in this experiment is 1500.

The model (in terms of the coded variables) for predicting the molecular weight response is:

= 1500 + 20

+ 18

+ 25

- 28

= 1500 + 20

+ 18

+ 25

+ 14

= 1500 + 40

+ 36

+ 50

+ 28

Question 23

1. An unreplicated 24 factorial experiment has been run in a chemical process. The response variable is molecular weight. The engineer has used the following four factors:

After conducting the experiment, the engineer finds that three of the main effects and one of the interactions are large enough to be considered significant. The effect estimates are A = 40, B = 36, C = 50, and AC = 28. The overall average of the 16 runs in this experiment is 1500.

Use the model from the experiment to predict molecular weight at the point time = 40, temperature = 125, feed rate = 12, and concentration = 45. The predicted response is:

1520

1585

1559

1537

None of the above

Question 24

1. In the previous question, it matters what value is given for concentration.

True

False

Question 25

1. Adding center points to a two-level factorial design provides information about pure quadratic second-order effects plus an estimate of pure experimental error.

True

False