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True

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

T/F

False

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

T/F

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

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

About this deck

Author: Shan A.

Created: 2013-04-22

Updated: 2013-04-22

Size: 34 flashcards

Keywords: flash card flashcards digital flashcards note sharing notes textbook wiki college dorm class classroom exam homework test quiz university college education learn student teachers tutors share, study blue studyblue studyblu

Views: 57

Created: 2013-04-22

Updated: 2013-04-22

Size: 34 flashcards

Keywords: flash card flashcards digital flashcards note sharing notes textbook wiki college dorm class classroom exam homework test quiz university college education learn student teachers tutors share, study blue studyblue studyblu

Views: 57

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