Stats
For randomized block ANOVA, we partition the total sum of squares (SST) into the sum of squares between (SSB) and the sum of squares block (SSBL).
false
In a two-way ANOVA procedure, there are two hypotheses to be tested—the test for Factor A and the test for Factor B.
false
One-way ANOVA requires that samples across the levels are equal to one another.
false
The degrees of freedom for the sum of squares block for randomized block ANOVA equal the number of blocks.
false
The degrees of freedom for the total sum of squares for one-way ANOVA equal the total number of observations minus two.
false
The null hypothesis for ANOVA assumes that not all of the population means being compared are equal.
false
The sum of squares within (SSW) measures the variation between each data value and the grand mean for all of the data.
false
Two-way ANOVA incorporates a blocking factor to account for variation outside of the main factor in the hopes of increasing the likelihood of detecting a variation due to the main factor.
false
When the F-test statistic is greater than the critical F-score for ANOVA, the correct conclusion is to fail to reject the null hypothesis.
false
A factor in an ANOVA test describes the cause of the variation in the data.
true
ANOVA provides a lower probability of a Type I error when compared to multiple t-tests when comparing three or more population means.
true
All analysis of variance procedures require that each of the populations being compared follows the normal probability distribution.
true
Analysis of variance compares the variance between samples to the variance within those samples to determine if means of populations are different.
true
Analysis of variance only tests to see if any pair of population means are different. To find out which population mean pairs are different, we need to perform a multiple comparisons test.
true
For randomized block ANOVA, the sum of squares error (SSE) represents the random variation in the data not attributed to either the main factor or the blocking factor.
true
For two-way ANOVA, we partition the total sum of squares (SST) into the sum of squares for Factor A (SSFA), the sum of squares for Factor B (SSFB), the sum of squares interaction (SSAB), and the sum of squares error (SSE).
true
One-way ANOVA is used when we are looking at the influence that one factor has on the data values.
true
The sum of squares between (SSB) measures the variation between each sample mean and the grand mean of the data.
true
The total sum of squares (SST) measures the amount of variation between each data value and the grand mean.
true
Unlike the critical z-score and critical t-score, the critical F-score can only take on positive values.
true
