Preferencias conformacionales de los aminoácidos

5. QUIRALIDAD MOLECULAR DE LADRILLOS MODELO

5.1. L -Aminoácidos

5.1.3. Preferencias conformacionales de los aminoácidos

a) The mean of the sampling distribution is always .

b) The standard deviation of the sampling distribution is always



. c) The shape of the sampling distribution is always approximately normal.

d) All of the above are true.

ANSWER:

TYPE: MC DIFFICULTY: Moderate

KEYWORDS: sampling distribution, properties

8. True or False: The amount of time it takes to complete an examination has a skewed-left distribution with a mean of 65 minutes and a standard deviation of 8 minutes. If 64 students were randomly sampled, the probability that the sample mean of the sampled students exceeds 71 minutes is approximately 0.

ANSWER:

True

TYPE: TF DIFFICULTY: Moderate

KEYWORDS: sampling distribution, central limit theorem

9. Suppose the ages of students in Statistics 101 follow a skewed-right distribution with a mean of 23 years and a standard deviation of 3 years. If we randomly sampled 100 students, which of the following statements about the sampling distribution of the sample mean age is incorrect?

a) The mean of the sample mean is equal to 23 years.

b) The standard deviation of the sample mean is equal to 3 years.

c) The shape of the sampling distribution is approximately normal.

d) The standard error of the sample mean is equal to 0.3 years.

ANSWER:

TYPE: MC DIFFICULTY: Easy

KEYWORDS: sampling distribution, central limit theorem

10. Why is the Central Limit Theorem so important to the study of sampling distributions?

a) It allows us to disregard the size of the sample selected when the population is not normal.

b) It allows us to disregard the shape of the sampling distribution when the size of the population is large.

c) It allows us to disregard the size of the population we are sampling from.

d) It allows us to disregard the shape of the population when n is large.

ANSWER:

TYPE: MC DIFFICULTY: Moderate KEYWORDS: central limit theorem

11. A sample that does not provide a good representation of the population from which it was collected is referred to as a(n) sample.

ANSWER:

biased

TYPE: FI DIFFICULTY: Moderate KEYWORDS: unbiased

12. True or False: The Central Limit Theorem is considered powerful in statistics because it works for any population distribution, provided the sample size is sufficiently large and the population mean and standard deviation are known.

ANSWER:

True

TYPE: TF DIFFICULTY: Moderate KEYWORDS: central limit theorem

13. Suppose a sample of n = 50 items is drawn from a population of manufactured products and the weight, X, of each item is recorded. Prior experience has shown that the weight has a probability distribution with  = 6 ounces and



= 2.5 ounces. Which of the following is true about the sampling distribution of the sample mean if a sample of size 15 is selected?

a) The mean of the sampling distribution is 6 ounces.

b) The standard deviation of the sampling distribution is 2.5 ounces.

c) The shape of the sample distribution is approximately normal.

d) All of the above are correct.

ANSWER:

TYPE: MC DIFFICULTY: Moderate

KEYWORDS: sampling distribution, unbiased

14. The average score of all pro golfers for a particular course has a mean of 70 and a standard deviation of 3.0.

Suppose 36 golfers played the course today. Find the probability that the average score of the 36 golfers exceeded 71.

ANSWER:

0.0228

TYPE: PR DIFFICULTY: Moderate

KEYWORDS: sampling distribution, mean, probability, central limit theorem

15. The distribution of the number of loaves of bread sold per day by a large bakery over the past 5 years has a mean of 7,750 and a standard deviation of 145 loaves. Suppose a random sample of n = 40 days has been selected. What is the approximate probability that the average number of loaves sold in the sampled days exceeds 7,895 loaves?

ANSWER:

Approximately 0

TYPE: PR DIFFICULTY: Moderate

KEYWORDS: sampling distribution, mean, probability, central limit theorem

16. Sales prices of baseball cards from the 1960s are known to possess a skewed-right distribution with a mean sale price of $5.25 and a standard deviation of $2.80. Suppose a random sample of 100 cards from the 1960s is selected. Describe the sampling distribution for the sample mean sale price of the selected cards.

a) Skewed-right with a mean of $5.25 and a standard error of $2.80.

b) Normal with a mean of $5.25 and a standard error of $0.28.

c) Skewed-right with a mean of $5.25 and a standard error of $0.28.

d) Normal with a mean of $5.25 and a standard error of $2.80.

ANSWER:

TYPE: MC DIFFICULTY: Easy

KEYWORDS: sampling distribution, central limit theorem

17. Major league baseball salaries averaged $1.5 million with a standard deviation of $0.8 million in 1994. Suppose a sample of 100 major league players was taken. Find the approximate probability that the average salary of the 100 players exceeded $1 million.

a) approximately 0 b) 0.2357

c) 0.7357

d) approximately 1 ANSWER:

TYPE: MC DIFFICULTY: Moderate

KEYWORDS: sampling distribution, mean, probability, central limit theorem

18. At a computer manufacturing company, the actual size of computer chips is normally distributed with a mean of 1 centimeter and a standard deviation of 0.1 centimeters. A random sample of 12 computer chips is taken. What is the standard error for the sample mean?

a) 0.029 b) 0.050 c) 0.091 d) 0.120 ANSWER:

TYPE: MC DIFFICULTY: Easy KEYWORDS: standard error, mean

19. At a computer manufacturing company, the actual size of computer chips is normally distributed with a mean of 1 centimeter and a standard deviation of 0.1 centimeters. A random sample of 12 computer chips is taken. What is the probability that the sample mean will be between 0.99 and 1.01 centimeters?

ANSWER:

0.2710

TYPE: PR DIFFICULTY: Moderate

KEYWORDS: sampling distribution, mean, probability

20. At a computer manufacturing company, the actual size of computer chips is normally distributed with a mean of 1 centimeter and a standard deviation of 0.1 centimeters. A random sample of 12 computer chips is taken. What is the probability that the sample mean will be below 0.95 centimeters?

ANSWER:

0.0416

TYPE: PR DIFFICULTY: Moderate

KEYWORDS: sampling distribution, mean, probability

21. At a computer manufacturing company, the actual size of computer chips is normally distributed with a mean of 1 centimeter and a standard deviation of 0.1 centimeters. A random sample of 12 computer chips is taken.

Above what value do 2.5% of the sample means fall?

ANSWER:

1.057

TYPE: PR DIFFICULTY: Difficult

KEYWORDS: sampling distribution, mean, value

22. The owner of a fish market has an assistant who has determined that the weights of catfish are normally distributed, with mean of 3.2 pounds and a standard deviation of 0.8 pounds. If a sample of 16 fish is taken, what would the standard error of the mean weight equal?

a) 0.003

TYPE: MC DIFFICULTY: Easy KEYWORDS: standard error, mean

23. The owner of a fish market has an assistant who has determined that the weights of catfish are normally distributed, with mean of 3.2 pounds and a standard deviation of 0.8 pounds. If a sample of 25 fish yields a mean of 3.6 pounds, what is the Z-score for this observation?

a) 18.750

TYPE: MC DIFFICULTY: Easy

KEYWORDS: sampling distribution, mean

24. The owner of a fish market has an assistant who has determined that the weights of catfish are normally distributed, with mean of 3.2 pounds and a standard deviation of 0.8 pounds. If a sample of 64 fish yields a mean of 3.4 pounds, what is probability of obtaining a sample mean this large or larger?

a) 0.0001

TYPE: MC DIFFICULTY: Moderate

KEYWORDS: sampling distribution, mean, probability

25. The owner of a fish market has an assistant who has determined that the weights of catfish are normally distributed, with mean of 3.2 pounds and a standard deviation of 0.8 pounds. What percentage of samples of 4 fish will have sample means between 3.0 and 4.0 pounds?

a) 84%

b) 67%

c) 29%

d) 16%

ANSWER:

TYPE: MC DIFFICULTY: Moderate

KEYWORDS: sampling distribution, mean, probability

26. The use of the finite population correction factor, when sampling without replacement from finite populations, will

a) increase the standard error of the mean.

b) not affect the standard error of the mean.

c) reduce the standard error of the mean.

d) only affect the proportion, not the mean.

ANSWER:

TYPE: MC DIFFICULTY: Easy

KEYWORDS: finite population correction

27. For sample size 16, the sampling distribution of the mean will be approximately normally distributed a) regardless of the shape of the population.

b) if the shape of the population is symmetrical.

c) if the sample standard deviation is known.

d) if the sample is normally distributed.

ANSWER:

TYPE: MC DIFFICULTY: Moderate

KEYWORDS: sampling distribution, mean, central limit theorem

28. The standard error of the mean for a sample of 100 is 30. In order to cut the standard error of the mean to 15, we would

a) increase the sample size to 200.

b) increase the sample size to 400.

c) decrease the sample size to 50.

d) decrease the sample to 25.

ANSWER:

TYPE: MC DIFFICULTY: Moderate KEYWORDS: standard error, mean

29. Which of the following is true regarding the sampling distribution of the mean for a large sample size?

a) It has the same shape, mean, and standard deviation as the population.

b) It has a normal distribution with the same mean and standard deviation as the population.

c) It has the same shape and mean as the population, but has a smaller standard deviation.

d) It has a normal distribution with the same mean as the population but with a smaller standard deviation.

ANSWER:

TYPE: MC DIFFICULTY: Moderate

KEYWORDS: sampling distribution, mean, central limit theorem

30. For sample sizes greater than 30, the sampling distribution of the mean will be approximately normally distributed

a) regardless of the shape of the population.

b) only if the shape of the population is symmetrical.

c) only if the standard deviation of the samples are known.

d) only if the population is normally distributed.

ANSWER:

TYPE: MC DIFFICULTY: Easy

KEYWORDS: sampling distribution, mean, central limit theorem

31. For sample size 1, the sampling distribution of the mean will be normally distributed a) regardless of the shape of the population.

b) only if the shape of the population is symmetrical.

c) only if the population values are positive.

d) only if the population is normally distributed.

ANSWER:

TYPE: MC DIFFICULTY: Moderate

KEYWORDS: sampling distribution, mean, central limit theorem 32. The standard error of the proportion will become larger

a) as p approaches 0.

b) as p approaches 0.50.

c) as p approaches 1.00.

d) as n increases.

ANSWER:

TYPE: MC DIFFICULTY: Moderate KEYWORDS: standard error, proportion

33. True or False: As the sample size increases, the standard error of the mean increases.

ANSWER:

False

TYPE: TF DIFFICULTY: Easy KEYWORDS: standard error, mean

34. True or False: If the population distribution is symmetric, the sampling distribution of the mean can be approximated by the normal distribution if the samples contain at least 15 observations.

ANSWER:

True

TYPE: TF DIFFICULTY: Easy

KEYWORDS: population distribution, sampling distribution, mean, central limit theorem

35. True or False: If the population distribution is unknown, in most cases the sampling distribution of the mean can be approximated by the normal distribution if the samples contain at least 30 observations.

ANSWER:

True

TYPE: TF DIFFICULTY: Easy

KEYWORDS: sampling distribution, mean, central limit theorem

36. True or False: If the amount of gasoline purchased per car at a large service station has a population mean of

$15 and a population standard deviation of $4, then 99.73% of all cars will purchase between $3 and $27.

ANSWER:

False

TYPE: TF DIFFICULTY: Moderate

KEYWORDS: sampling distribution, mean, probability

37. True or False: If the amount of gasoline purchased per car at a large service station has a population mean of

$15 and a population standard deviation of $4, and a random sample of 4 cars is selected, there is approximately a 68.26% chance that the sample mean will be between $13 and $17.

ANSWER:

False

TYPE: TF DIFFICULTY: Moderate

EXPLANATION: The sample is too small for the normal approximation.

KEYWORDS: sampling distribution, mean, probability

38. True or False: If the amount of gasoline purchased per car at a large service station has a population mean of

$15 and a population standard deviation of $4, and it is assumed that the amount of gasoline purchased per car is symmetric, there is approximately a 68.26% chance that a random sample of 16 cars will have a sample mean between $14 and $16.

ANSWER:

True

TYPE: TF DIFFICULTY: Moderate

KEYWORDS: sampling distribution, mean, probability

39. True or False: If the amount of gasoline purchased per car at a large service station has a population mean of

$15 and a population standard deviation of $4, and a random sample of 64 cars is selected, there is approximately a 95.44% chance that the sample mean will be between $14 and $16.

ANSWER:

True

TYPE: TF DIFFICULTY: Difficult

KEYWORDS: sampling distribution, mean, probability

40. True or False: As the sample size increases, the effect of an extreme value on the sample mean becomes smaller.

ANSWER:

True

TYPE: TF DIFFICULTY: Moderate

KEYWORDS: sampling distribution, law of large numbers

41. True or False: If the population distribution is skewed, in most cases the sampling distribution of the mean can be approximated by the normal distribution if the samples contain at least 30 observations.

ANSWER:

True

TYPE: TF DIFFICULTY: Easy

KEYWORDS: sampling distribution, mean, central limit theorem

42. True or False: A sampling distribution is a probability distribution for a statistic.

ANSWER:

True

TYPE: TF DIFFICULTY: Moderate KEYWORDS: sampling distribution

43. True or False: Suppose  = 50 and ² = 100 for a population. In a sample where n = 100 is randomly taken, 95% of all possible sample means will fall between 48.04 and 51.96.

ANSWER:

True

TYPE: TF DIFFICULTY: Moderate

KEYWORDS: sampling distribution, mean, probability

44. True or False: Suppose  = 80 and ² = 400 for a population. In a sample where n = 100 is randomly taken, 95% of all possible sample means will fall above 76.71.

ANSWER:

True

TYPE: TF DIFFICULTY: Moderate

KEYWORDS: sampling distribution, mean, probability

45. True or False: Suppose  = 50 and ² = 100 for a population. In a sample where n = 100 is randomly taken, 90% of all possible sample means will fall between 49 and 51.

ANSWER:

False

TYPE: TF DIFFICULTY: Moderate

KEYWORDS: sampling distribution, mean, probability

46. True or False: The Central Limit Theorem ensures that the sampling distribution of the sample mean approaches normal as the sample size increases.

ANSWER:

True

TYPE: TF DIFFICULTY: Easy KEYWORDS: central limit theorem

47. True or False: The standard error of the mean is also known as the standard deviation of the sampling distribution of the sample mean.

ANSWER:

True

TYPE: TF DIFFICULTY: Easy KEYWORDS: standard error, mean

48. True or False: A sampling distribution is defined as the probability distribution of possible sample sizes that can be observed from a given population.

ANSWER:

False

TYPE: TF DIFFICULTY: Easy KEYWORDS: sampling distribution

49. True or False: As the size of the sample is increased, the standard deviation of the sampling distribution of the sample mean for a normally distributed population will stay the same.

ANSWER:

False

TYPE: TF DIFFICULTY: Easy

KEYWORDS: standard error, properties

50. True or False: For distributions such as the normal distribution, the arithmetic mean is considered more stable from sample to sample than other measures of central tendency.

ANSWER:

True

TYPE: TF DIFFICULTY: Moderate KEYWORDS: sampling distribution, mean

= 25 grams. A sample of 25 vitamins is to be selected. So, the middle 70% of all sample means will fall between what two values?

ANSWER:

104.8 and 115.2

TYPE: PR DIFFICULTY: Difficult

KEYWORDS: sampling distribution, mean, value

57. The amount of time required for an oil and filter change on an automobile is normally distributed with a mean of 45 minutes and a standard deviation of 10 minutes. A random sample of 16 cars is selected. What would you expect the standard error of the mean to be?

ANSWER:

2.5 minutes

TYPE: PR DIFFICULTY: Easy KEYWORDS: standard error, mean

58. The amount of time required for an oil and filter change on an automobile is normally distributed with a mean of 45 minutes and a standard deviation of 10 minutes. A random sample of 16 cars is selected. What is the probability that the sample mean is between 45 and 52 minutes?

ANSWER:

0.4974

TYPE: PR DIFFICULTY: Moderate

KEYWORDS: sampling distribution, mean, probability

59. The amount of time required for an oil and filter change on an automobile is normally distributed with a mean of 45 minutes and a standard deviation of 10 minutes. A random sample of 16 cars is selected. What is the probability that the sample mean will be between 39 and 48 minutes?

ANSWER:

0.8767

TYPE: PR DIFFICULTY: Moderate

KEYWORDS: sampling distribution, mean, probability

60. The amount of time required for an oil and filter change on an automobile is normally distributed with a mean of 45 minutes and a standard deviation of 10 minutes. A random sample of 16 cars is selected. So, 95% of all sample means will fall between what two values?

ANSWER:

40.1 and 49.9 minutes

TYPE: PR DIFFICULTY: Difficult

KEYWORDS: sampling distribution, mean, probability

61. The amount of time required for an oil and filter change on an automobile is normally distributed with a mean of 45 minutes and a standard deviation of 10 minutes. A random sample of 16 cars is selected. So, 90% of the sample means will be greater than what value?

ANSWER:

41.8 minutes

TYPE: PR DIFFICULTY: Difficult

KEYWORDS: sampling distribution, mean, probability

62. True or False: The amount of bleach a machine pours into bottles has a mean of 36 oz. with a standard deviation of 0.15 oz. Suppose we take a random sample of 36 bottles filled by this machine. The sampling distribution of the sample mean has a mean of 36.

ANSWER:

True

TYPE: TF DIFFICULTY: Easy

KEYWORDS: sampling distribution, mean, unbiased

63. True or False: The amount of bleach a machine pours into bottles has a mean of 36 oz. with a standard deviation of 0.15 oz. Suppose we take a random sample of 36 bottles filled by this machine. The sampling distribution of the sample mean has a standard error of 0.15.

ANSWER:

False

TYPE: TF DIFFICULTY: Easy

KEYWORDS: sampling distribution, mean, standard error

64. True or False: The amount of bleach a machine pours into bottles has a mean of 36 oz. with a standard deviation of 0.15 oz. Suppose we take a random sample of 36 bottles filled by this machine. The sampling distribution of the sample mean will be approximately normal only if the population sampled is normal.

ANSWER:

False

TYPE: TF DIFFICULTY: Easy

KEYWORDS: sampling distribution, mean, central limit theorem

65. The amount of bleach a machine pours into bottles has a mean of 36 oz. with a standard deviation of 0.15 oz.

Suppose we take a random sample of 36 bottles filled by this machine. The probability that the mean of the sample exceeds 36.01 oz. is __________.

ANSWER:

0.3446

TYPE: FI DIFFICULTY: Moderate

KEYWORDS: sampling distribution, mean, probability, central limit theorem

66. The amount of bleach a machine pours into bottles has a mean of 36 oz. with a standard deviation of 0.15 oz.

Suppose we take a random sample of 36 bottles filled by this machine. The probability that the mean of the sample is less than 36.03 is __________.

ANSWER:

0.8849

TYPE: FI DIFFICULTY: Moderate

KEYWORDS: sampling distribution, mean, probability, central limit theorem

In document QUIRALIDAD MOLECULAR Y SUPRAMOLECULAR DE SISTEMAS DE INTERÉS BIOLÓGICO Y ATMOSFÉRICO ESTUDIADAS MEDIANTE TÉCNICAS ESPECTROSCÓPICAS VIBRACIONALES SENSIBLES (VCD) Y NO SENSIBLES (IR Y RAMAN) A LA QUIRALIDAD COMBINADAS CON CÁLCULOS QUÍMICO CUÁNTICOS (página 157-163)