5 Control of the disease caused by the Eastern Equine Encephalomyelitis (EEE) vi

Question

5 Control of the disease caused by the Eastern Equine Encephalomyelitis (EEE) virus requires a good understanding of which vectors are most important in transmitting the EEE virus to the different hosts. There are six species of mosquitoes in Alabama carrying this virus. A DNA analysis of a sample of blood-engorged female mosquitoes of the species Ae. vexans shows that 48 of the n 127 mosquitoes have fed on mammals. Use the R function binom, test) to find the following 92% confidence intervals for the proportion of blood meals taken from mammals by the species Ae. vexans. out of 8.00 | (a) The 92% two-sided confidence interval ranges from (b) The 92% lower-bound confidence interval ranges from (c) The 92% upper-bound confidence interval ranges from 0 to to to 1. Now calculate the following confidence intervals using the R function prop.test0 (d) The 92% two-sided confidence interval ranges from to to 1. (e) The 92% lower-bound confidence interval ranges from (f) The 92% upper-bound confidence interval ranges from 0 to Check

Explanation / Answer

Rcode with answers below.

a)

> binom.test(48,127, p = 0.5,alternative = c("two.sided", "less", "greater"),conf.level = 0.92)

Exact binomial test

data: 48 and 127

number of successes = 48, number of trials = 127, p-value = 0.00752

alternative hypothesis: true probability of success is not equal to 0.5

92 percent confidence interval:

0.3017543 0.4589727

sample estimates:

probability of success

0.3779528

b)

> binom.test(48,127, p = 0.5,alternative = "less",conf.level = 0.92)

Exact binomial test

data: 48 and 127

number of successes = 48, number of trials = 127, p-value = 0.00376

alternative hypothesis: true probability of success is less than 0.5

92 percent confidence interval:

0.0000000 0.4436474

sample estimates:

probability of success 0.3779528

c)

> binom.test(48,127, p = 0.5,alternative = "greater",conf.level = 0.92)

Exact binomial test

data: 48 and 127

number of successes = 48, number of trials = 127, p-value = 0.9978

alternative hypothesis: true probability of success is greater than 0.5

92 percent confidence interval:

0.3156476 1.0000000

sample estimates:

probability of success

0.3779528

d)

> prop.test(48,127, p = NULL, alternative = c("two.sided", "less", "greater"),conf.level = 0.92, correct = TRUE)

1-sample proportions test with continuity correction

data: 48 out of 127, null probability 0.5

X-squared = 7.0866, df = 1, p-value = 0.007766

alternative hypothesis: true p is not equal to 0.5

92 percent confidence interval:

0.3026481 0.4593062

sample estimates:

p

0.3779528

e)

> prop.test(48,127, p = NULL, alternative = "less", conf.level = 0.92, correct = TRUE)

1-sample proportions test with continuity correction

data: 48 out of 127, null probability 0.5

X-squared = 7.0866, df = 1, p-value = 0.003883

alternative hypothesis: true p is less than 0.5

92 percent confidence interval:

0.0000000 0.4438352

sample estimates:

p

0.3779528

f)

> prop.test(48,127, p = NULL, alternative = "greater", conf.level = 0.92, correct = TRUE)

1-sample proportions test with continuity correction

data: 48 out of 127, null probability 0.5

X-squared = 7.0866, df = 1, p-value = 0.9961

alternative hypothesis: true p is greater than 0.5

92 percent confidence interval:

0.3160481 1.0000000

sample estimates:

p

0.3779528

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