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When doing an ANOVA, you observe large differences in means between groups. Within the ANOVA framework, this would most likely be interpreted as evidence strongly favoring the [[0]] hypothesis.

\n

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Chicken farming is a multi-billion dollar industry, and any methods that increase the growth rate of young chicks can reduce consumer costs while increasing company profits, possibly by millions of dollars. An experiment was conducted to measure and compare the effectiveness of various feed supplements on the growth rate of chickens. Newly hatched chicks were randomly allocated into six groups, and each group was given a different feed supplement. Below are some summary statistics from this data set along with box plots showing the distribution of weights by feed type.

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The ANOVA output below can be used to test for differences between the average weights of chicks on different diets.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
DfSum SqMean SqF valuep-value
feed5231129.1646225.8315.360.0000
Residuals65195556.023008.55
\n

Conduct a hypothesis test to determine if these data provide convincing evidence that the average weight of chicks varies across some (or all) groups.

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The conditions for an ANOVA are:

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Independence: Chicks are randomly assigned to feed types (presumably kept separate from one another), therefore independence of observations [[0]] reasonable.

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Approximately normal: The distributions of weights within each feed type appear [[1]] fairly symmetric, with the possible exception of the sunflower group.

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Constant variance: Based on the side-by-side box plots, the constant variance assumption appears [[2]] reasonable.

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The hypotheses are:

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$H_0: \\mu_1 = \\mu = 2 = ... = \\mu_6$

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$H_A:$ The average weight varies across some (or all) groups.

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The F-value = [[3]].

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The p-value [[4]] approx. 0. 

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With such a [[5]] p-value, we [[6]] $H_0$.

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The data provide convincing evidence that the average weight of chicks [[7]] across some (or all) feed supplement groups.

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vary"], "matrix": ["1", 0], "distractors": ["", ""]}], "sortAnswers": false}], "partsMode": "all", "maxMarks": 0, "objectives": [], "penalties": [], "objectiveVisibility": "always", "penaltyVisibility": "always"}, {"name": "7.39 Coffee, depression, and physical activity", "extensions": [], "custom_part_types": [], "resources": [["question-resources/Screenshot_2020-10-30_at_10.19.17.png", "/srv/numbas/media/question-resources/Screenshot_2020-10-30_at_10.19.17.png"]], "navigation": {"allowregen": true, "showfrontpage": false, "preventleave": false, "typeendtoleave": false}, "contributors": [{"name": "L1Stats UofG", "profile_url": "https://numbas.mathcentre.ac.uk/accounts/profile/5882/"}], "tags": [], "metadata": {"description": "", "licence": "None specified"}, "statement": "

Caffeine is the world’s most widely used stimulant, with approximately 80% consumed in the form of coffee.

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Participants in a study investigating the relationship between coffee consumption and exercise were asked to report the number of hours they spent per week on moderate (e.g., brisk walking) and vigorous (e.g., strenuous sports and jogging) exercise. Based on these data the researchers estimated the total hours of metabolic equivalent tasks (MET) per week, a value always greater than 0.

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The table below gives summary statistics of MET for women in this study based on the amount of coffee consumed.

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What are the hypotheses for this test?

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$H_0:$ The mean MET for each group is [[0]] to each other.

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$H_A:$ At least one pair of means [[1]] different.

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What are the conditions needed for an ANOVA?

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Fill in the gaps to complete the output.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
dfSum SqMean SqF valuep-value
coffee[[0]][[3]][[4]][[6]]0.0003
Residuals[[1]]25564819[[5]]
Total[[2]]25575327
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"adaptiveMarkingPenalty": 0, "exploreObjective": null, "prompt": "

What is the correct conclusions of the test?

\n

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Undergraduate students taking an introductory statistics course at Duke University conducted a survey about GPA and major. The side-by-side box plots show the distribution of GPA among three groups of majors. Also provided is the ANOVA output.

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What are the hypotheses?

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$H_0:$ Average GPA is [[0]] for all majors

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$H_A:$ At least one pair of means [[1]] different.

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Given the p-value from the output, what is the conclusion?

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What is the sample size?

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Determine if the following statements are true or false in ANOVA.

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As the number of groups increases, the modified significance level for pairwise tests increases as well.

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As the total sample size increases, the degrees of freedom for the residuals increases as well.

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The constant variance condition can be somewhat relaxed when the sample sizes are relatively consistent across groups.

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The independence assumption can be relaxed when the total sample size is large

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Note: This question follows on from question 7.31, please look at this question again for more details.

\n

An experiment that was conducted with the goal of identifying a treatment that reduces subjects’ psychopathic deviant T scores, where this score measures a person’s need for control or his rebellion against control. This analysis involves comparing the success of treatments. The relevant ANOVA output is given below.

\n

\n

\n

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c)

\n

$SE = \\sqrt{\\frac{9.739^2}{14} + \\frac{9.739^2}{14}} = 3.7$

\n

\n

$T = \\frac{(6.21 - 2.86) - 0}{3.7} = 0.91$

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The hypotheses are:

\n

$H_0:$ Average score difference is the same for all treatments

\n

$H_1$: At least one pair of means are different.

\n

\n

What is the conclusion of the test at the 5% level?

\n

[[0]]

\n

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Conduct pairwise tests to determine which groups are different from each other.

\n

\n

\n

\n

$K$ = 3 x 2/2 = 3 pairwise t-test

\n

The new significance level is [[0]].

\n

\n

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