Two types of medication for hives are being tested. The manufacturer claims that the new medication B is more effective than the standard medication A and undertakes a comparison to determine if medication B produces relief for a higher proportion of adult patients within a 30-minute time window. 20 out of a random sample of 200 adults given medication A still had hives 30 minutes after taking the medication. 12 out of another random sample of 200 adults given medication B still had hives 30 minutes after taking the medication. The hypothesis test is to be carried out at a 1% level of significance.

A) State the null and alternative hypotheses in words and in statistical symbols.

B) What statistical test is appropriate to use? Explain the rationale for your answer.

C) Would the test be right-tailed, left-tailed or two-tailed? Explain the rationale for your answer.

D) Describe an outcome that would result in a Type I error. Explain the rationale for your answer.

E) Describe an outcome that would result in a Type II error. Explain the rationale for your answer.

In what ways do advertisers in magazines use sexual imagery to appeal to youth? One study classified each of 1509 full-page or larger ads as "not sexual" or "sexual," according to the amount and style of the dress of the male or female model in the ad. The ads were also classified according to the target readership of the magazine. Here is the two-way table of counts.

(a) Summarize the data numerically and graphically. (Compute the conditional distribution of model dress for each audience. Round your answers to three decimal places.)

(b) Perform the significance test that compares the model dress for the three categories of magazine readership. Summarize the results of your test and give your conclusion. (Use α = 0.01. Round your value for χ²to two decimal places, and round your P-value to four decimal places.)

Conclusion

Fail to reject the null hypothesis. There is significant evidence of an association between target audience and model dress.

Reject the null hypothesis. There is not significant evidence of an association between target audience and model dress.     

Fail to reject the null hypothesis. There is not significant evidence of an association between target audience and model dress.

Reject the null hypothesis. There is significant evidence of an association between target audience and model dress.

(c) All of the ads were taken from the March, July, and November issues of six magazines in one year. Discuss this fact from the viewpoint of the validity of the significance test and the interpretation of the results.

This is not an SRS. This gives us reason to believe our conclusions might be suspect.

This is not an SRS. This gives us no reason to believe our conclusions are suspect.     

This is an SRS. This gives us reason to believe our conclusions might be suspect.This is an SRS.

This gives us no reason to believe our conclusions are suspect.

1. Calculate the probability of the following events. Make sure to reduce your answers to decimals
   1. You have a box of 100 raffle tickets. There will be two tickets drawn to determine the winners of the raffle. What’s the probability that you and your best friend both win the raffle? Assume that the drawing will be conducted without replacement.
   2. You have a box of 200 raffle tickets. There will be one ticket drawn to determine the winner of the raffle. What’s the probability that you or your best friend, or your roommate wins the raffle?
   3. You have a bag of marbles. In the bag are 3 green marbles, 2 blue marbles, and 6 red marbles. What is the probability of drawing 4 red marbles in 4 consecutive draws from the bag? Assume you’re sampling randomly and with replacement.

We assume that our wages will increase as we gain experience and become more valuable to our employers. Wages also increase because of inflation. By examining a sample of employees at a given point in time, we can look at part of the picture. How does length of service (LOS) relate to wages? The data here (data9.dat) is the LOS in months and wages for 60 women who work in Indiana banks. Wages are yearly total income divided by the number of weeks worked. We have multiplied wages by a constant for reasons of confidentiality.

(a) Plot wages versus LOS. Consider the relationship and whether or not linear regression might be appropriate.

(b) Find the least-squares line. Summarize the significance test for the slope. What do you conclude?

(c) State carefully what the slope tells you about the relationship between wages and length of service.

(d) Give a 95% confidence interval for the slope.
(_______ ,_______ )

worker  wages   los     size
1       51.7094 69      Large
2       71.0128 60      Small
3       70.07   202     Small
4       49.9388 89      Small
5       51.4523 81      Large
6       61.5483 94      Small
7       45.4168 55      Large
8       53.4017 88      Large
9       42.3147 155     Large
10      46.2871 86      Small
11      63.229  112     Large
12      57.062  72      Small
13      45.8663 32      Small
14      42.0388 35      Large
15      43.3518 57      Large
16      54.3362 39      Large
17      62.1635 47      Large
18      42.8431 89      Small
19      68.4515 42      Large
20      44.4342 65      Large
21      43.6074 62      Large
22      40.2586 28      Small
23      58.7744 75      Large
24      51.7969 67      Small
25      73.4367 168     Large
26      46.8493 86      Small
27      49.9769 44      Small
28      44.8422 93      Large
29      44.7397 113     Large
30      51.0961 25      Large
31      76.9333 118     Small
32      49.2112 109     Large
33      49.1286 43      Large
34      56.6601 74      Small
35      59.466  85      Large
36      37.9853 146     Large
37      39.2893 88      Large
38      37.1191 81      Small
39      53.4795 57      Large
40      68.418  88      Small
41      55.6763 45      Small
42      60.8119 73      Small
43      61.1519 113     Large
44      52.1887 47      Small
45      64.3686 33      Large
46      77.7875 188     Small
47      98.2949 75      Large
48      70.8228 81      Large
49      48.0061 70      Small
50      44.3186 22      Large
51      55.4166 59      Large
52      47.1434 58      Large
53      49.7145 78      Large
54      59.1692 57      Small
55      48.7496 45      Small
56      61.6285 71      Large
57      73.1227 26      Small
58      44.0953 65      Large
59      51.2836 30      Small
60      37.4581 55      Large

One of the major misconceptions about correlation is that a relationship between two variables means causation; that is, one variable causes changes in the other variable. There is a particular tendency to make this causal error when the two variables seem to be related to each other.

What is one instance where you have seen correlation misinterpreted as causation? Please describe.

Total plasma volume is important in determining the required plasma component in blood replacement therapy for a person undergoing surgery. Plasma volume is influenced by the overall health and physical activity of an individual. Suppose that a random sample of 50 male firefighters are tested and that they have a plasma volume sample mean of x = 37.5 ml/kg (milliliters plasma per kilogram body weight). Assume that σ = 7.20 ml/kg for the distribution of blood plasma.

(a)

Find a 99% confidence interval for the population mean blood plasma volume in male firefighters. What is the margin of error? (Round your answers to two decimal places.)

lower limitupper limitmargin of error

(b)

What conditions are necessary for your calculations? (Select all that apply.)

σ is knownthe distribution of weights is uniformn is largethe distribution of weights is normalσ is unknown

(c)

Interpret your results in the context of this problem.

1% of the intervals created using this method will contain the true average blood plasma volume in male firefighters.99% of the intervals created using this method will contain the true average blood plasma volume in male firefighters.     The probability that this interval contains the true average blood plasma volume in male firefighters is 0.99.The probability that this interval contains the true average blood plasma volume in male firefighters is 0.01.

(d)

Find the sample size necessary for a 99% confidence level with maximal margin of error E = 2.20 for the mean plasma volume in male firefighters. (Round up to the nearest whole number.)

male firefighters

One manufacturer has developed a quantitative index of the​ "sweetness" of orange juice.​ (The higher the​ index, the sweeter the​ juice). Is there a relationship between the sweetness index and a chemical measure such as the amount of​water-soluble pectin​ (parts per​ million) in the orange​ juice? Data collected on these two variables for 24 production runs at a juice manufacturing plant are shown in the accompanying table. Suppose a manufacturer wants to use simple linear regression to predict the sweetness​ (y) from the amount of pectin​ (x).

Run Sweetness Index Pectin​ (ppm)

1 5.2 220

2 5.5 229

3 5.9 256

4 . 5.9 209

5 5.9 223

6 6.1 217

7 5.9 230

8 5.6 270

9 5.7 238

10 5.9 214

11 5.4 408

12 . 5.6 259

13 5.8 304

14 5.5 258

15 5.3 282

16 5.4 383

17 5.7 269

18 5.4 267

19 5.6 225

20 5.4 260

21 5.9 231

22 5.8 218

23 5.8 248

24 5.9 241

a. Find the least squares line for the data.

ModifyingAbove y with caretyequals=6.25546.2554plus+left parenthesis nothing right parenthesis−0.0023negative 0.0023x (Round to four decimal places as​ needed.) CORRECT ANSWER

b. Interpret β0 and β1 in the words of the problem. Interpret β0 in the words of the problem.

A.The regression coefficient β0 is the estimated sweetness index for orange juice that contains 0 ppm of pectin.

B.The regression coefficient β0 is the estimated increase​ (or decrease) in amount of pectin​ (in ppm) for each​ 1-unit increase in sweetness index.

C.The regression coefficient β0 is the estimated amount of pectin​ (in ppm) for orange juice with a sweetness index of 0.

D.The regression coefficient β0 does not have a practical interpretation.

A population of values has a normal distribution with μ=139.8μ=139.8 and σ=23σ=23. You intend to draw a random sample of size n=12n=12.

Find the probability that a single randomly selected value is greater than 155.1.
P(X > 155.1) =

Find the probability that a sample of size n=12n=12 is randomly selected with a mean greater than 155.1.
P(M > 155.1) =

Suppose that the distance of fly balls hit to the outfield (in baseball) is normally distributed with a mean of 261 feet and a standard deviation of 41 feet. Let X be the distance in feet for a fly ball.

a. What is the distribution of X? X ~ N(___,___)

b. Find the probability that a randomly hit fly ball travels less than 336 feet. Round to 4 decimal places. ____

c. Find the 75th percentile for the distribution of distance of fly balls. Round to 2 decimal places. ____ feet

Consider the following data:

Step 1 of 5:

Determine the three-period moving average for the next time period. If necessary, round your answer to one decimal place.

Step 2 of 5:

What are the MAD, MSE and MAPE scores for the three-period moving average? Round any intermediate calculations, if necessary, to no less than six decimal places, and round your final answer to one decimal place.

Step 3 of 5:

Determine the exponentially smoothed forecast for the next time period using a smoothing constant, αα, of 0.350.35. If necessary, round your answer to one decimal place.

Step 4 of 5:

What are the MAD, MSE and MAPE scores for the exponentially smoothed forecast? Round any intermediate calculations, if necessary, to no less than six decimal places, and round your final answer to one decimal place.

Step 5 of 5:

Based on the MAPE scores, which forecast is best?

An investigator wishes to conduct a test of the effect of alcohol consumption on the performance of automobile drivers, possibly to gain more information about the legal maximum for DUI arrests. Before the driving test, subjects drink a glass of orange juice laced with controlled amounts of vodka. Their performance is measured by the number of errors on a driving simulator. Five subjects are randomly assigned to each of five groups receiving different amounts of vodka (either 0, 1, 2, 4 or 6 ounces), and the following results were obtained: DRIVING ERRORS AS A FUNCTION OF ALCOHOL CONSUMPTION (OUNCES)

ZERO ONE TWO FOUR SIX

1 4 6 15 20

1 3 1 6 25

3 1 2 9 10

6 7 10 17 10

4 5 7 9 9

Total: 15 20 26 56 74

SumX= G= 191 SumX2 = 2371

A) Using SPSS, conduct an ANOVA, including post hoc tests if necessary.

B) Using either h2 or Coen’s d, calculate effect size. What kind of effect does this indicate?

C) What, in plain English, does all this mean?

The company Digital Trends reported that 48% of Americans have shared passwords for TV and movie streaming.† For purposes of this exercise, assume that the 48% figure is correct for the population of adult Americans.

(a)

A random sample of size

n = 500

will be selected from this population and p̂, the proportion who have shared TV and movie streaming passwords, will be calculated. What are the mean and standard deviation of the sampling distribution of p̂? (Round your standard deviation to four decimal places.)

mean

standard deviation

(b)

Is the sampling distribution of p̂ approximately normal for random samples of size

n = 500?

Explain.

The sampling distribution of p̂ is approximately normal because np is less than 10.The sampling distribution of p̂ is approximately normal because np and n(1 − p) are both at least 10.    The sampling distribution of p̂ is not approximately normal because np is less than 10.The sampling distribution of p̂ is not approximately normal because np and n(1 − p) are both at least 10.The sampling distribution of p̂ is not approximately normal because n(1 − p) is less than 10.

(c)

Suppose that the sample size is

n = 125

rather than

n = 500.

What are the values of the mean and standard deviation when

n = 125?

(Round your standard deviation to four decimal places.)

mean

standard deviation

Does the change in sample size affect the mean and standard deviation of the sampling distribution of p̂? If not, explain why not. (Select all that apply.)

When the sample size decreases, the mean increases.

When the sample size decreases, the mean decreases

When the sample size decreases, the mean stays the same. The sampling distribution is always centered at the population mean, regardless of sample size.When the sample size decreases, the standard deviation increases.When the sample size decreases, the standard deviation decreases.When the sample size decreases, the standard deviation stays the same. The standard deviation of the sampling distribution is always the same as the standard deviation of the population distribution, regardless of sample size.

(d)

Is the sampling distribution of p̂ approximately normal for random samples of size

n = 125?

Explain.

The sampling distribution of p̂ is approximately normal because np is less than 10.The sampling distribution of p̂ is approximately normal because np and n(1 − p) are both at least 10.    The sampling distribution of p̂ is not approximately normal because np is less than 10.The sampling distribution of p̂ is not approximately normal because np and n(1 − p) are both at least 10.The sampling distribution of p̂ is not approximately normal because n(1 − p) is less than 10.

Sample size = 10. Population is normal. The sample mean is 1.3 million. Sample standard deviation is 0.9million. Alpha ( level of significance) is 1%. Ho: Mu > or equal to 1.5 million Ha: Mu< 1.5 million a. What is the critical value? B. What is the test statistics t score? C . Do you reject or accept the null hypothesis?

explain how process improvements using control charts are used to support areas of industry research, academic research, and scientific research. explain in detail

The Relationship Between State Agencies and Nonprofit Organizations Introduction The relationship between government agencies and nonprofit organizations is the focus of increasing attention within the public administration community. Practitioners recognize that the organization of public services relies to a substantial degree upon what we have come to call third-party government (Salamon, 1981). Nongovernmental actors not only deliver government-funded services but also actively participate throughout the policy process. Often the third-party is a nonprofit organization. In the last decade or so, researchers from a variety of disciplines have examined this evolutionary development more closely (Kramer, 1981; Salamon and Abramson, 1982; Salamon, 1987; Gronbjerg, 1987; Ostrander, Langton, and Van Til, 1987; Lipsky and Smith, 1989-90; Wolch, 1990; Provan and Milward, 1990). A 1989 National Academy of Public Administration report, Privatization: The Challenge to Public Management, urged that public administrators and policymakers in general acknowledge the significant management challenges posed by government programs that involve such "tools of government action" as contracting out, loan guarantees, government sponsored enterprises, and vouchers (Salamon, 1989b). Within this context of extensive sharing of responsibility between governmental and nongovernmental actors for operating public programs, the government/nonprofit relationship is widely acknowledged as a critical element. The shrinking capacity of public organizations, increasing demand for services, and continuing trend toward decentralized program delivery underscore its importance. At the same time, an understanding of the precise character of the state/voluntary sector relationship and the degree of interdependence between public agencies and nonprofit organizations requires additional empirical investigation. Research findings reported here describe that relationship in terms of the dependence of public agencies and nonprofit organizations on each other for resources and their resulting interdependence. The framework laid out in this study emerged from a synthesis of three sources: (1) the perspectives of organization theory, especially power/dependence and resource dependence, and bureaucratic politics; (2) a series of exploratory model refinement interviews with four public-sector and five nonprofit- sector participants in an earlier policy study (Dawes and Saidel, 1988); and (3) a field pretest in June-July 1989, with 20 state agency and 20 nonprofit administrators from four service areas. Emerson's (1962) theory of reciprocal power-dependence relations provided the building blocks for the framework used in this research. He reasoned that the power of A over B is equal to, and based upon, the dependence of B upon A. Recognizing the reciprocity of social relations, we can represent a power-dependence relation as a pair of equations: Pab = Dba Pba = Dab (Emerson, 1962, p. 33). For the purposes of this study, if a becomes s for state agencies and b becomes n for nonprofit organizations, the equations can be read as follows: The power of state agencies over nonprofit organizations equals the dependence of nonprofit organizations on state agencies for resources (Psn = Dns). The power of nonprofit organizations over state agencies equals the dependence of state agencies on nonprofit organizations for resources (Pns = Dsn). The use of Dsn and Dns yields two measures of resource dependence that, taken together, delineate a current picture of resource interdependence between state and nonprofit organizations. What resources, common across service areas, are exchanged between state government bureaucracies and public benefit nonprofit organizations? Resources that flow from state agencies to nonprofit organizations are: revenues; information, including expertise and technical assistance; political support and legitimacy, in the sense of external validation (Galaskiewicz, 1985); and access to the nonlegislative policy process (Rourke, 1984). Nonprofit organizations supply their service-delivery capacity, information, political support, and legitimacy to state agencies. Nonprofit organization service-delivery capacity was documented as a substantial resource to government in the Urban Institute Nonprofit Sector Project finding that "nonprofit organizations actually deliver a larger share of the services government finances than do government agencies themselves" (Salamon, 1987, p. 30). Three resource dependence criteria or dimensions of dependence (Bacharach and Lawler, 1981) can be specified: (1) the importance of the resource; (2) the availability of alternatives; and (3) the ability to compel provision of the resource. The importance, or essentiality of a resource to an organization, consists of the organization's need for the resource in order to function, to operate, or to deliver programs or services (Levine and White, 1961; Emerson, 1962; Blau, 1964; Jacobs, 1974; Thompson, 1967; Cook, 1977; Pfeffer and Salancik, 1978; Brudney, 1978; Aldrich, 1979; Provan, Beyer, and Kruytbosch, 1980; Provan and Skinner, 1989). The importance dimension incorporates the elements of substitutability and criticality or the organization's ability to forego the resource and still continue operating (Jacobs, 1974; Pfeffer and Salancik, 1978; Aldrich, 1979). The availability of the same resource from another supplier is widely acknowledged as a dimension of dependence (Levine and White, 1961; Emerson, 1962; Blau, 1964; Thompson, 1967; Jacobs, 1974; Cook, 1977; Pfeffer and Salancik, 1978; Brudney, 1978; Provan and Skinner, 1989). Cook's explanation is representative: "To the extent that alternative sources are available to an organization in an exchange network, dependence is less..." (1977, p. 66). Insofar as organization A can compel, pressure, or force organization B to provide needed resources, A is less dependent on B. In contrast to the availability of alternatives, this dimension appears much less frequently in the literature (Blau, 1964; Aldrich, 1979; Provan, Beyer, and Kruytbosch, 1980). In the context of third-party government research, the ability to compel provision of a resource includes statutory and regulatory sanctions as well as the use of less formal kinds of pressure to force resource provision. This expanded scope is appropriate to the complex political environment within which inter-organizational resource exchanges occur across sectors. In order to determine the degree of reciprocal dependence, it is important to examine both state and non-profit administrators with respect to the level of dependence each group perceives both state and non-profits have on each other. While the literature has provided a framework for the factors that need investigation, it has not provided any evidence of actual differences in dependence (or the perception of dependence), either generally or in the case of specific service areas. The present study examined four key variables. The first was the general dependence of nonprofit organizations on states (the importance of the resource obtained from the state, the availability of the resource from alternative sources, and the ability to compel the provision of the resource from the state). The second was the general dependence of the state on nonprofit organizations (the importance of the resource obtained from the nonprofit organization, the availability of the resource from alternative sources, and the ability to compel the provision of the resource from the nonprofit organizations). These were derived from the work of Bacharach and Lawler (1981). The third was dependence of the state on the non-profit organizations by service sector (arts, health, developmental disabilities, and human services). The fourth was dependence of nonprofit organizations on the state by service sector (arts, health, developmental disabilities, and human services). The following hypotheses characterized the specific expectations of the study: [Itemize All Hypotheses Here] Method Sample The population of interest in this study was 1) nonprofit organizations in the State of New York in one of the designated service areas, and 2) divisions within the state system in each of the same service areas. An explanation of the study was sent to each sector (nonprofit and state) requesting responses from interested parties. The final sample of 80 nonprofit organizations consisted of a random sample of 20 organizations in each of the four service areas – arts, health, developmental disabilities, and human service areas – from those who indicated interest. The sample from the state was obtained by randomly selecting 20 persons from the same four service sectors from those who responded with interest. Tables 1 and 2, below, present the principle descriptive categorical and continuous information, respectively, for the two final groups: [Insert Table 1 Here] [Insert Table 2 Here] Measures To measure reciprocal resource dependence of state agencies and nonprofit organizations, 14 Likert-type scales were constructed with a number of items to which the respondent indicated intensity of agreement or disagreement on a six-point scale. Response categories were strongly disagree, generally disagree, disagree a little, agree a little, generally agree, strongly agree. The conceptual anchors of each scale were (1) independence and (6) dependence. That is, higher scores represented greater dependence in the area being assessed. Three scales (importance, alternative availability, and pressure) measured the dependence of state agencies on nonprofit organizations for resources. Three parallel scales measured the same dimensions for resource dependence in the other direction, that is, the dependence of nonprofit organizations on state agencies for resources. Items were predominantly attitudinal; some were behavioral. The following items are samples from the importance, alternative availability, and pressure scales, respectively. S1. State agencies often use ideas from nonprofit organizations to formulate policy recommendations. N1. Nonprofit organizations often use ideas from state agencies lO formulate policy recommendations. S2.There are certainly other supporters of agency interests as valuable as nonprofit organizations. N2. There are certainly other supporters of nonprofit organizations' interests as valuable as state agencies. S3.Agencies are in no position to force nonprofit organizations to implement their programs. N3. Nonprofit organizations are in no position to force agencies to fund their programs. Scale scores were the average of the item scores. Table 3 reports reliability results for the six scales. Alpha reliability coefficients are listed in bold-face type. Discriminant validity may be measured by the inter-scale correlation coefficients shown on the diagonal in that table. Table 3: Reliabilities of State and Nonprofit Scales Scale Reliability State Importance .67 State Alternative Availability .73 State Pressure .63 Nonprofit Importance .70 Nonprofit Alternative Availability .70 Nonprofit Pressure .75 The remaining eight scales were divided, four each for state departments and non-profit agencies, into individual service areas. These were arts, health, developmental disabilities, and human services. The first set of four examined dependence of the state departments on nonprofit agencies in the four areas; the second set of four scales assessed the dependence of nonprofits on state agencies in the same four areas. The average of the three individual scale scores measuring state agency dependence on nonprofit agencies became Dsn in the model. The average of the three individual scale scores measuring nonprofit agency dependence on state agencies became Dns. These reciprocal resource flows, understood together, became the basis for a g e n e r a l model of resource dependence between sectors. Design The design of the study was [Enter the Study Design Here]. The particular strengths of this design are [Enter the Design Strengths Here]. The design is weak in the areas of [Enter the Design Weaknesses Here]. Procedure The total number of study participants was 153: 80 nonprofit and 73 state agency managers. Public-sector respondents were 20 people, including commissioners, from each of the 4 state agencies and executive directors from 20 nonprofit organizations in each of the service areas. Of the 80 nonprofit agency respondents, 14 were top-level managers other than the executive director. All nonprofit and some state agency respondents participated in two-part, on-site interviews, including a self-administered survey completed immediately and an interview with demographic and open-ended questions. State agency commissioners designated two additional executive administrators for the research interview, and they, in turn, identified 17 other managers to receive a mailed survey.

1. Examining the independent and dependent variables, what are the key hypotheses in your study? List both research and null hypotheses. (10 points)