fish <- read.csv("FishGills3.csv")
nutrition <- read.csv("NutritionStudy.csv")Problem 1
ACTN3 is a gene that encodes alpha-actinin-3, a protein in fast-twitch muscle fibers, important for activities like sprinting and weightlifting. The gene has two main alleles: R (functional) and X (non-functional). The R allele is linked to better performance in strength, speed, and power sports, while the X allele is associated with endurance due to a greater reliance on slow-twitch fibers. However, athletic performance is influenced by various factors, including training, environment, and other genes, making the ACTN3 genotype just one contributing factor.
A study examines the ACTN3 genetic alleles R and X, also associated with fast-twitch muscles. Of the 436 people in this sample, 244 were classified as R, and 192 were classified as X. Does the sample provide evidence that the two options are not equally likely? Conduct the test using a chi-square goodness-of-fit test.
Hypothesis
\(H_0\):\(p_1\) = \(p_2\) = 1/2
\(H_a\): at least on \(p_i\) \(\neq\) 1/2
observed <- c(244, 192)
theoritical_prop <- rep(1/2, 2)
chisq.test(observed)##
## Chi-squared test for given probabilities
##
## data: observed
## X-squared = 6.2018, df = 1, p-value = 0.01276Conclusion 1
Because the p-value is low, we have sufficient evidence to reject the null hypothesis. This means that there is enough evidence to say that the ACTN3 alleles, R vs X, are not equally likely in the sample given.
Problem 2
Who Is More Likely to Take Vitamins: Males or Females? The dataset NutritionStudy contains, among other things, information about vitamin use and the gender of the participants. Is there a significant association between these two variables? Use the variables VitaminUse and Gender to conduct a chi-square analysis and give the results. (Test for Association)
Hypothesis
\(H_0\) : Vitamin use is not associated with gender
\(H_a\) : Vitamin use is associate with gender
observed_nutrition <- table(nutrition$VitaminUse, nutrition$Sex)
observed_nutrition##
## Female Male
## No 87 24
## Occasional 77 5
## Regular 109 13chisq.test(observed_nutrition)##
## Pearson's Chi-squared test
##
## data: observed_nutrition
## X-squared = 11.071, df = 2, p-value = 0.003944Conclusion 2
Because the p-value is low, we have sufficient evidence to reject the null hypothesis. This means that there is a significant association between vitamin use and gender.
Problem 3
Most fish use gills for respiration in water, and researchers can observe how fast a fish’s gill cover beats to study ventilation, much like we might observe a person’s breathing rate. Professor Brad Baldwin is interested in how water chemistry might affect gill beat rates. In one experiment, he randomly assigned fish to tanks with different calcium levels. One tank was low in calcium (0.71 mg/L), the second tank had a medium amount (5.24 mg/L), and the third tank had water with a high calcium level (18.24 mg/L). His research team counted gill rates (beats per minute) for samples of 30 fish in each tank. The results are stored in FishGills3. Perform ANOVA test to see if the mean gill rate differs depending on the calcium level of the water.
Hypothesis
\(H_0\): \(\mu_A\) = \(\mu_B\) = \(\mu_C\)
\(H_a\): not all \(\mu_i\) are equal
anova_result <- aov(GillRate ~ Calcium, data = fish)
anova_result## Call:
## aov(formula = GillRate ~ Calcium, data = fish)
##
## Terms:
## Calcium Residuals
## Sum of Squares 2037.222 19064.333
## Deg. of Freedom 2 87
##
## Residual standard error: 14.80305
## Estimated effects may be unbalancedsummary(anova_result)## Df Sum Sq Mean Sq F value Pr(>F)
## Calcium 2 2037 1018.6 4.648 0.0121 *
## Residuals 87 19064 219.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1conclusion 3 Because the p-value is low, it means that there is enough evidence to say that there is a difference in the mean gill rate depending on the calcium level of the water.