I obtained a significant F-value from the one-way ANOVA model “anova exercisehrs feedback”. I then conducted pairwise comparisons on all groups to detect the differences between groups. Comparing the testing results using Tukey’s test and Scheffé’s test, we can see that:Question 3Answera.The results of Tukey’s test and Scheffé’s test agreed with each otherb.Tukey’s test results showed that the praise group differed significantly from the other two groups on the “exercisehrs”, while Scheffé’s test results only showed a significant difference between the nothing and praise groupsc.Scheffé’s test results showed that the praise group differs significantly from the other two groups on the “exercisehrs”, while Tukey’s test results only showed a significant difference between the nothing and praise groupsd.Both test results showed a significant difference between the nothing and criticism groups

I’ve obtained a significant F-value from the one-way ANOVA model “anova exercisehrs feedback”. Next, I want to compare if the criticism group differs from the praise group in the number of hours exercising per day; what will be the best way to set up my contrast?Question 7Answera.Assign contrast coefficients as follows: praise (1), criticism (-1), nothing (0)b.Assign contrast coefficients as follows: praise (-1), criticism (-1), nothing (0)c.Assign contrast coefficients as follows: praise (2), criticism (2), nothing (0)d.Assign contrast coefficients as follows: praise (.5), criticism (.5), nothing (1)e.Assign contrast coefficients as follows: praise (-1), criticism (-1), nothing (2)Clear my choice

In a  One-Way Analysis of Variance Table which of the following statements is necessarily true?Group of answer choicesThe "Sum of Squares for Groups" measures the variation among individual observations within the group.A "Large" F-value means that most of the observed variation in the response variable is due to sampling variability.The F-test compares the variation between the groups with the background variation within each group.Comparing the means for two independent populations (with unknown standard deviations) using an ANOVA  (F-test) is, necessarily, equivalent to using an independent samples t-test.An F-test is useless because it doesn't tell us which treatment produces "optimal" results

I’m interested in the effect of types of feedback on the number of hours basketball players spend doing exercises. I hypothesise that players who receive praise as feedback will spend significantly longer hours doing exercises than the no feedback group, and players who receive criticism will spend significantly fewer hours doing exercises than the no feedback group. After I’ve obtained a significant F-value from my one-way ANOVA omnibus test, what will be the most appropriate option to conduct my secondary analysis?Question 6Answera.Use the “pwmean” command to run post hoc comparisonsb.Use the “pwmean” command to run planned comparisons, specifically, set up contrasts to compare “praise vs. nothing” and “criticism vs. nothing”c.Use the “contrast” command to run post hoc comparisonsd.Use the “contrast” command to run planned comparisons, specifically, set up contrasts to compare “praise vs. nothing” and “criticism vs. nothing”e.Nothing, because we can get all the information from the omnibus F summary table

conducted planned contrasts using the “contrast {feedback 1 0 -1} {feedback 1 -1 0}, effects mcompare(bon)” command after I obtained a significant omnibus F-test result using “anova exercisehrs feedback”. Based on the outputs given by the first contrast command, choose the correct interpretation of the results.The praise group spent Answer 1 Question 1 Answer 2 Question 1 hours exercising than the nothing group.The praise group spent Answer 3 Question 1 Answer 4 Question 1 hours exercising than the criticism group.

In a one-way ANOVA, F values increase as: SSBetween becomes greater than SSWithin df approaches zero SSTotal becomes smaller SSWithin becomes greater than SSTotal