The ages of 30 patients admitted in a certain hospital during a particular week were as follows:

48, 31, 54, 37, 18, 64, 61, 43, 40, 71, 51, 12, 52, 65, 53,

42, 39, 62, 74, 48, 29, 67, 30, 49, 68, 35, 57, 26, 27, 58

Find:

1. Mean
2. Median
3. Mode
4. Geometric Mean
5. Harmonic Mean
6. Mid Range
7. Lower Quartile
8. Upper Quartile
1. First Decile
10. Ninth Decile
11. Fourth Percentile
50. Ninety Sixth Percentile
1000. Range
14. Inter Quartile Range
15. Quartile Deviation
16. Mean of Absolute Deviations
17. Variance
18. Standard Deviation
19. Coefficient of Range
20. Coefficient of Quartile Deviation
21. Coefficient of Variation
5. Coefficient of Skewness

Your boss tells you to analyze the comparative effectiveness of two existing UAVs at detecting particular “events” on the ground in the CENTCOM area of responsibility (AOR). To do so, you are given data from a series of experiments during which UAV “A” and UAV “B” were tested under controlled conditions. For each experiment, both UAVs were flown. The data is shown below. Target UAV “A” Score UAV “B” Score 1 72 93 2 77 88 3 42 36 4 18 29 5 11 10 Assume that the data are paired (i.e. tested against the same targets). At a significance level of? = 0.05, test the hypothesis that the two types of UAVs perform the same versus the alternative hypothesis that they are different. Hint: think paired data! Conduct a formal hypothesis test and report your conclusions.

A) Parameter of interest: From the problem context, identify the parameter of interest.

B) Null hypothesis, H0: State the null hypothesis, H0 in terms of the parameter of interest

C) Alternative hypothesis, H1: Specify an appropriate alternative hypothesis, H1.

D) Test Statistic: Determine an appropriate test statistic (equation; state degrees if freedom if necessary).

E) Reject H0 if: State the rejection criteria for the null hypothesis for the given level of α.

F) Computations: Compute any necessary sample quantities, substitute these into the equations for the test statistic, and compute that value. Perform P-Value calculations.

G) Draw conclusions: Decide whether or not H0 should be rejected and report that in the problem context. Make a “real-world” statement about the outcome of the test (cannot just say “reject the null hypothesis”)

The Thomas Supply Company Inc. is a distributor of gas-powered generators. As with any business, the length of time customers take to pay their invoices is important. Listed below, arranged from smallest to largest, is the time, in days, for a sample of the Thomas Supply Company Inc. invoices.

(Round your answers to 2 decimal places.)

1. Determine the first and third quartiles.
2. Determine the second decile and the eighth decile.
3. Determine the 67^th percentile.

A 2010 survey by a reputable automotive website found that 62​% of vehicle owners avoided automotive maintenance and repairs. Suppose a company would like to perform a hypothesis test to challenge this finding. From a random sample of 150 vehicle​ owners, it was found that 103 avoid maintenance repairs. Using alpha equals 0.05​, complete a-c below,

a.) What is the critical value(s)?

b.) What is the test statistic?

c.) What is the p-value?

A study was performed among 40 boys in a school in Edinburgh to look at the presence of spermatozoa in urine samples according to age [15]. The boys entered the study at 8−11 years of age and left the study at 12−18 years of age. A 24-hour urine sample was supplied every 3 months by each boy. Table 10.28 gives the presence or absence of sperm cells in the urine samples for each boy together with the ages at entrance and exit of the study and the age at the first sperm-positive urine sample. For all parts of this question, exclude boys who exited this study without 1 sperm-positive urine sample (i.e., boys 8, 9, 14, 25, 28, 29, 30). 10.47 Provide a stem-and-leaf plot of the age at first sperm-positive urine specimen. *10.48 If we assume that all boys have no sperm cells at age 11 (11.0 years) and all have sperm cells at age 18, then estimate the probability of first developing sperm cells at ages 12 (i.e., between 12.0 and 12.9 years), 13, 14, 15, 16, and 17. *10.49 Suppose mean age at spermatogenesis = 13.67 years, with standard deviation = 0.89 years and we assume that the age at spermatogenesis follows a normal distribution. The pediatrician would like to know what is the earliest age (in months) before which 95% of boys experience spermatogenesis because he or she would like to refer boys who haven’t experienced spermatogenesis by this age to a specialist for further follow-up. Can you estimate this age from the information provided in this part of the problem? *10.50 Suppose we are uncertain whether a normal distribution provides a good fit to the distribution of age at spermatogenesis. Answer this question using the results from Problems 10.47−10.49. (Assume that the large-sample method discussed in this chapter is applicable to these data.)

Age at
Boy Entrance First positive Exit Observations
1 10.3 13.4 16.7 − − − − − − − − − − + + − − − − + + + − − 2 10.0 12.1 17.0 − − − − − − − − + − − + + − + − − + − + − − − − − + + 3 9.8 12.1 16.4 − − − − − − − − + − + + − + + + + + + − − + + − + 4 10.6 13.5 17.7 − − − − − − − − − − − + + − − − + − − − − 5 9.3 12.5 16.3 − − − − − − − − − − − − + + − − − − + − − − − − − − − 6 9.2 13.9 16.2 − − − − − − − − − − − − − − − − − + − − − − − − − 7 9.6 15.1 16.7 − − − − − − − − − − − − − − − − − − − + − − − + 8 9.2 — 12.2 − − − − − − − − − − − − 9 9.7 — 12.1 − − − − − − − − − 10 9.6 12.7 16.4 − − − − − − − − − − − − + − + + + + + − − + + − + 11 9.6 12.5 16.7 − − − − − − − − − − + − − + − + − − + + + 12 9.3 15.7 16.0 − − − − − −− − − − − − − − − − − − − − − − − + + 14 9.6 — 12.0 − − − − − − − − − 16 9.4 12.6 13.1 − − − − − − − − − − + + + + 17 10.5 12.6 17.5 − − − − − − − + − + + + + + + + + − − + − − + + 18 10.5 13.5 14.1 − − − − − − − − − − + − − 19 9.9 14.3 16.8 − − − − − − − − − − − − − − − + − − − − − + − + 20 9.3 15.3 16.2 − − − − − − − − − − − − − − − − − − − − − + + + 21 10.4 13.5 17.3 − − − − − − − − + + − + − + + − + − + + + 22 9.8 12.9 16.7 − − − − − − − − − − − + + + + − + + + + − + + − + − − 23 10.8 14.2 17.3 − − − − − − − − − − − − + − − + + + − + 24 10.9 13.3 17.8 − − − − − − − − + + + + − + + + + + − + + − − 25 10.6 — 13.8 − − − − − − − − − − − 26 10.6 14.3 16.3 − − − − − − − − − − − − − + − − − + − − − 27 10.5 12.9 17.4 − − − − − − − − + − + + + + − − − + + − − + + + + 28 11.0 — 12.4 − − − − − − 29 8.7 — 12.3 − − − − − − − − − − − − − − 30 10.9 — 14.5 − − − − − − − − − − − − − 31 11.0 14.6 17.5 − − − − − − − − − − − − + + + + + + + + + + − + 32 10.8 14.1 17.6 − − − − − − − − − − − + + − − + − − − − − − 33 11.3 14.4 18.2 − − − − − − − − − − − + + − + + − − + − − − − − 34 11.4 13.8 18.3 − − − − − − − + − − − + − − − + + + − − + − + 35 11.3 13.7 17.8 − − − − − − − + + + − + − − − + + + − + + 36 11.2 13.5 15.7 − − − − − − − − − + − − − − − − − − 37 11.3 14.5 16.3 − − − − − − − − − − − + − + + − − − 38 11.2 14.3 17.2 − − − − − − − − − − − + − − + − + + + + + + − 39 11.6 13.9 14.7 − − − − − + − − − 40 11.8 14.1 17.9 − − − − + − + − + − + + + + − − − − 41 11.4 13.3 18.2 − − − − + + + − + − − − − − + + + + + − − 42 11.5 14.0 17.9 − − − − − − − + + − − − − − − − + + − + −

Are America's top chief executive officers (CEOs) really worth all that money? One way to answer this question is to look at row B, the annual company percentage increase in revenue, versus row A, the CEO's annual percentage salary increase in that same company. Suppose that a random sample of companies yielded the following data: B: Percent for company 21 11 16 20 5 8 4 22 A: Percent for CEO 18 5 14 22 10 12 1 17 Do these data indicate that the population mean percentage increase in corporate revenue (row B) is different from the population mean percentage increase in CEO salary? Use a 1% level of significance. What is the value of the test statistic? Select one: a. -0.730 b. -0.683 c. 0.683 d. 0.730 e. -0.639

A certain system can experience three different types of defects. Let A_i (i = 1,2,3) denote the event that the system has a defect of type i. Suppose that the following probabilities are true.

P(A₁) = 0.10      P(A₂) = 0.07     P(A₃) = 0.05
P(A₁ ∪ A₂) = 0.11      P(A₁ ∪ A₃) = 0.12
P(A₂ ∪ A₃) = 0.10      P(A₁ ∩ A₂ ∩ A₃) = 0.01

(a) Given that the system has a type 1 defect, what is the probability that it has a type 2 defect? (Round your answer to four decimal places.)


(b) Given that the system has a type 1 defect, what is the probability that it has all three types of defects? (Round your answer to four decimal places.)


(c) Given that the system has at least one type of defect, what is the probability that it has exactly one type of defect? (Round your answer to four decimal places.)


(d) Given that the system has both of the first two types of defects, what is the probability that it does not have the third type of defect? (Round your answer to four decimal places.)

Why are the costs different between the traditional method and the activity-based method?

1.Weights, in pounds, of ten-year-old girls are collected from a neighborhood. A sample of 26 is given below. Assuming normality, use Excel to find the 98% confidence interval for the population mean weight μ. Round your answers to three decimal places and use increasing order.Weight
66.4
86.3
71.3
52.8
68.0
85.0
66.2
79.2
93.5
84.5
71.1
74.5
65.0
58.5
59.8
80.2
69.2
92.9
78.9
59.4
63.6
66.5
60.7
80.1
60.4
74.5

2.

Julia wants to estimate the percentage of people who submit their tax returns online. She surveys 330 individuals and finds that 65 submit their tax returns online.

Find the margin of error for the confidence interval for the population proportion with a 95% confidence level.

Use the table of common z-scores above.

• Round the final answer to three decimal places

Consider the following sample data for the relationship between advertising budget and sales for Product A: Observation 1 2 3 4 5 6 7 8 9 10 Advertising ($) 90,000 100,000 100,000 110,000 120,000 120,000 130,000 140,000 140,000 150,000 Sales ($) 535,000 626,000 625,000 674,000 712,000 725,000 809,000 832,000 845,000 919,000 What is the predicted sales quantity for an advertising budget of $119,000? Please round your answer to the nearest integer.

Some sources report that the weights of​ full-term newborn babies in a certain town have a mean of

99

pounds and a standard deviation of

0.60.6

pounds and are normally distributed.a. What is the probability that one newborn baby will have a weight within

0.60.6

pounds of the

meanlong dash—that

​is, between

8.48.4

and

9.69.6

​pounds, or within one standard deviation of the​ mean?b. What is the probability that the average of

ninenine

​babies' weights will be within

0.60.6

pounds of the​ mean; will be between

8.48.4

and

9.69.6

​pounds?

c. Explain the difference between​ (a) and​ (b).

a. The probability is

nothing.

​(Round to four decimal places as​ needed.)

A textile manufacturing process finds that on average, three flaws occur per every 220 yards of material produced.

What is the probability of no more than two flaws in a 220-year piece of material? (Round your final answer to 4 decimal places.)

$1M is available to invest in S or B. The percentage yield on each investment depends on whether the econ has a good or bad year.

Econ has a Good year    Econ has a Bad year

Yield on S    22% of 1M 10% of 1M

   (i.e. $220,000)    ($100,000)

Yield on B 16% of 1M 14% of 1M

($160,000) ($140,000)

It is equally likely (50%) that the econ will have a good or bad year.

For $10,000, a firm can be hired to forecast the state of the econ. The firm's forecasts have the following probabilities:

p(Good forecast | Econ is good) = .8

p(GF | EIB) = .2

It is equally likely (50%) for EIG & EIB to occur

a) Calculate the following:

p(EIG | GF) =

p(EIB | GF) =

p(EIG | BF) =

p( EIB | BF) =

b) Draw a decision tree to determine to invest in S or B to maximize expected profits. Should the firm be hired?

c) What are the values of the EVSI and EVPI?

16. Assume that human body temperatures are normally distributed with a mean of 98.22°F and a standard deviation of 0.64°F. a. A hospital uses 100.6°F as the lowest temperature considered to be a fever. What percentage of normal and healthy persons would be considered to have a​ fever? Does this percentage suggest that a cutoff of 100.6°F is​ appropriate? b. Physicians want to select a minimum temperature for requiring further medical tests. What should that temperature​ be, if we want only​ 5.0% of healthy people to exceed​ it? (Such a result is a false​ positive, meaning that the test result is​ positive, but the subject is not really​ sick.)

A. The percentage of normal and healthy persons considered to have a fever is ____%.

​(Round to two decimal places as​ needed.)

Does this percentage suggest that a cutoff of 100.6 degrees Upper F is​ appropriate?

A. No, because there is a large probability that a normal and healthy person would be considered to have a fever.

B. No, because there is a small probability that a normal and healthy person would be considered to have a fever.

C. Yes, because there is a small probability that a normal and healthy person would be considered to have a fever.

D. ​Yes, because there is a large probability that a normal and healthy person would be considered to have a fever.

b. The minimum temperature for requiring further medical tests should be _____ F if we want only​ 5.0% of healthy people to exceed it.

​(Round to two decimal places as​ needed.)

Carpetland salespersons average $8000 in sales per week. Steve Contois, the firm’s vice president, proposes a compensation plan with new selling incentives. Steve hopes that the results of a trial selling period will enable him to test whether the compensation plan would be effective. Before answering the following questions, you will need to first formulate the appropriate null and alternative hypotheses. a. What is the Type I error in this situation? What are the consequences of making this error? b. What is the Type II error in this situation? What are the consequences of making this error? c. Since there is money involved in the proposed plan, Steve would like to have a more stringent test by imposing a lower significance level. As a student of Statistics, do you think this is a good idea? Explain.