Get started today!

Good to have you back!
If you've signed in to StudyBlue with Facebook in the past, please do that again.

- StudyBlue
- Chapter 12 - T/F

Shan A.

True

The *F* distribution's curve is positively skewed.

T/F

False

The test statistic used in ANOVA is *Student's t*.

T/F

Advertisement

True

There is one, unique *F* distribution for a F-statistic with 29 degrees of freedom in the numerator and 28 degrees of freedom in the denominator.

T/F

True

One characteristic of the *F* distribution is that *F* cannot be negative.

T/F

False

One characteristic of the *F* distribution is that the computed *F* can only range between -1 and +1.

T/F

True

The shape of the *F* distribution is determined by the degrees of freedom for the *F-*statistic, one for the numerator and one for the denominator.

T/F

True

Like Student's *t* distribution, a change in the degrees of freedom causes a change in the shape of the F distribution.

T/F

True

If the computed value of *F* is 0.99 and the critical value is 3.89, we would not reject the null hypothesis.

T/F

False

For the hypothesis test, Ho:o1=o2, with n_{1} = 10 and n_{2} = 10, the F-test statistic is 2.56. At the 0.01 level of significance, we would reject the null hypothesis.

T/F

True

For the hypothesis test, Ho:o1=o2, with n_{1} = 4 and n_{2} = 4, the F-test statistic is 50.01. At the 0.01 level of significance, we would reject the null hypothesis.

T/F

False

For the hypothesis test, Ho:o1=o2, with n_{1} = 7 and n_{2} = 7, the F-test statistic is 2.56. At the 0.05 level of significance, we would reject the null hypothesis.

T/F

Advertisement

True

For the hypothesis test, Ho:o1=o2, with n_{1} = 9 and n_{2} = 9, the F-test statistic is 4.53. At the 0.05 level of significance, we would reject the null hypothesis.

T/F

True

To employ ANOVA, the populations being studied must be approximately normally distributed.

T/F

True

To employ ANOVA, the populations should have approximately equal standard deviations.

T/F

False

In an ANOVA table, *k* represents the total number of sample observations and *n* represents the total number of treatments.

T/F

False

The alternate hypothesis used in ANOVA is u1=u2=u3*. *

T/F

True

The alternate hypothesis for ANOVA states that not all the means are equal.

T/F

True

For an ANOVA test, rejection of the null hypothesis does not identify which treatment means differ significantly.

T/F

False

If the computed value of *F* is 4.01 and the critical value is 2.67, we would conclude that all the population means are equal.

T/F

True

If we want to determine which treatment means differ, we compute a confidence interval for the difference between each pair of means.

T/F

True

If a confidence interval for the difference between a pair of treatment means includes 0, then we fail to reject the null hypothesis that there is no difference in the pair of treatment means.

T/F

True

If the endpoints of a confidence interval for the difference between a pair of treatment means are both positive numbers, then we reject the null hypothesis that there is no difference in the pair of treatment means.

T/F

True

A treatment is a specific source of variation in a set of data.

T/F

True

A blocking effect is a specific source of variation in a set of data.

T/F

True

When a blocking effect is included in an ANOVA, the result is a smaller error sum of squares.

T/F

False

When a blocking effect is included in an ANOVA, two sources of variation are reported: treatment variation and block variation.

T/F

True

When a blocking effect is included in an ANOVA, the analysis is more likely to detect differences in the treatment means.

T/F

False

The F-statistic to test for a blocking effect is computed as the ratio of the Treatment Mean Square and the Block Mean Square.

T/F

True

In a two-way ANOVA, the sum of the treatment, block, and error degrees of freedom equal the total degrees of freedom.

T/F

False

In a two-way ANOVA, the sum of the treatment and block mean squares equals the error mean square.

T/F

True

In a two-way ANOVA, the sum of the treatment, block, and error sum of squares equals the total sum of squares.

T/F

True

In a two-way ANOVA with interaction, there are two factor effects and an interaction effect.

T/F

True

In an interaction plot, parallel lines are an indication that there is no interaction effect.

T/F

False

Interaction between two factors occurs when the effect of one factor on the response variable is the same for any value of another factor.

T/F

"StudyBlue is great for studying. I love the study guides, flashcards and quizzes. So extremely helpful for all of my classes!"

Alice , Arizona State University"I'm a student using StudyBlue, and I can 100% say that it helps me so much. Study materials for almost every subject in school are available in StudyBlue. It is so helpful for my education!"

Tim , University of Florida"StudyBlue provides way more features than other studying apps, and thus allows me to learn very quickly!Â I actually feel much more comfortable taking my exams after I study with this app. It's amazing!"

Jennifer , Rutgers University"I love flashcards but carrying around physical flashcards is cumbersome and simply outdated. StudyBlue is exactly what I was looking for!"

Justin , LSU