Note in 31 and 33 there are some tedious calculations that I did for you. You can see the output once you knit this file.

9.2

21.

  1. type answer here. A is not reasonable because confidence intervals are not based on probability.

  2. type answer here. B is the most reasonable interpretation

  3. type answer here. C is not reasonable because this problem talks about having 95 % confidence, not 95 % of the population.

  4. type answer here. D is not reasonable because this problem talks about the population, not a sample

23.

type answer here. We are 90 % confident that the mean for time spent completing an order at Taco Bell’s drive through is between 161.5 and 164.7 seconds.

25.

type answer here. –The sample size can be increased because the more information you have in the sample, the more precise your estimate is. –A more precise confidence interval can be calculated–99 % maybe

27.

  1. type answer here. A large sample size is needed because the confidence interval will increase, and the estimate will be more precise

  2. type answer here. The caveat is satisifed, the sample size is less than 5 % of the population

  3. type answer here. 1646, .16934–with 90 % confidence, we can say the mean amount of grams per deciliter in a driver involved in a fatal car crash was between .1646 and .16934.

  4. type answer here. It’s possible, because the true mean may not be captured in this proportion

29.

type answer here. 317.63, 394.57, we are 95 % confident that the mean numner of licks to the center of a tootsie pop is between 317.63 and 394.57.

31.

  1. type answer here. 4.8925

  2. type answer here. 4.670, 5.096, we are 95 % confident that the mean pH of rain water in West Virginia is between 4.690 and 5.096.

data <- c(4.58,5.72,5.19,4.75,5.05,5.02,4.8,4.74,4.77,4.76,4.77,4.56)
mean(data)
## [1] 4.8925
sd(data)
## [1] 0.3194064

  1. type answer here. 4.607, 5.179, We are 99 % confident that the mean pH of rain water in West Virginia is between 4.607 and 5.179.

  2. type answer here. As the level of confidence increases, so does the margin of error

33.

  1. skip

  2. skip

data2 <- c(3148,2057,1758,663,1071,2637,3345,773,743,1370)
mean(data2)
## [1] 1756.5
sd(data2)
## [1] 1007.454

  1. type answer here. $1035.8, $2477.2, we are 95 % confident that the mean repair cost for low impact collision for vehicles is between $1035.8 and $2477.2

  2. type answer here. The 95 % confidence interval would be narrow

9.3

5.

type answer here. 30.144 = alpha / 2 of 0.05, 10.117 =1-(alpha/2) of 0.95

7.

type answer here. 40.289 = alpha/2 of 0.01, 9.542= alpha/2 of 0.99

9.

  1. type answer here. 7.94, 23.66

  2. type answer here. 8.59, 20.63

  3. type answer here. 6.61, 31.36

11.

type answer here. 0.226, 0.542–we can be 95 % confident that the population standard deviation of the pH of rainwater in West Virginia is between 0.226 and 0.542.

13.

type answer here. $734.8, $1657.5, we can be 90 % confident that the population standard deviation of repair costs of a low impact bumper crash on a mini or micro car is between $734.8 and $1657.5.