In a March 26-28, 2010 Gallup poll, 473 respondents (out of 1033) approved of they way President Obama handled the health care debate (regardless of their opinions on health care policy).

a) What is your point estimate for this proportion?

b) Construct a 95% confidence interval for this opinion. did a significant majority disapprove of how he handled the debate?

A company would like to estimate its total cost equation. It has collected 48 months of monthly production output and corresponding total production costs.  The collected data is in the file Production Cost Data Only.xlsx. Recall that

TOTAL COST = Fixed Costs + Variable Cost per Unit *Output.

Use the data to estimate a function that describes total cost for this company. (Round answers to 2 decimal places)

Develop a scatterplot of the two variables: monthly output and monthly total costs. Describe the relationship.

Estimate a total cost curve for this company.  State the estimated total cost function.

Based on your estimated total cost curve what is the estimated Fixed Cost for the Company?

Based on your estimated total cost curve what is the estimated average unit variable cost for the Company?

Develop a 95% confidence interval for the true average variable cost per unit.

What percent of the variation in monthly production costs is “explained” by the monthly production output?

Suppose the plant manager is interested in mean costs for several monthswhere output averages 30,000 units (i.e., Xp = 30). What is the predicted monthly total costs when output averages 30?  Construct a 95% confidence interval for the mean production costs for months that average 30,000 units of output.

We are to distribute 10 colored balls into five containers. Do the following:

1. Compute the probability of having at least one ball in each bucket if the balls are all different and the buckets are all labeled differently.

2. Compute the probability of having at least one ball in each bucket if the balls are all different and the buckets are all gray.

3. Compute the probability of having at least one ball in each bucket if the balls are gray and the buckets are all labeled differently.

4. Which one gives the most likelihood?

You are curious about the role of graduate status on work-life balance in Tech students. In a sample of 100 undergraduate students, 37 reported having trouble balancing school and work. In a separate sample of 75 graduate students, 50 had trouble with this balance. Test the null hypothesis that undergraduate or graduate students are equally likely to have trouble with work-life balance (alpha=0.05).

For independent sample t-tests: mean values for each sample, the variances for each sample, estimated standard error of the difference in means, the t-ratio, degrees of freedom, the t-critical value, and your decision to reject or retain the null.

For dependent sample t-tests: mean values for each sample, standard deviation for the difference between groups, standard error of the difference between the means, the t-ratio, degrees of freedom, the t-critical value, and your decision to reject or retain the null.

For proportions: sample proportions, combined proportions, standard error of the difference, z-score, critical z-score, and your decision to reject or retain the null.

Thirty small communities in Connecticut (population near 10,000 each) gave an average of x = 138.5 reported cases of larceny per year. Assume that σ is known to be 45.1 cases per year.

(a) Find a 90% confidence interval for the population mean annual number of reported larceny cases in such communities. What is the margin of error? (Round your answers to one decimal place.)

lower limit:

upper limit:

margin of error:

(b) Find a 95% confidence interval for the population mean annual number of reported larceny cases in such communities. What is the margin of error? (Round your answers to one decimal place.)

lower limit:

upper limit:

margin of error:

(c) Find a 99% confidence interval for the population mean annual number of reported larceny cases in such communities. What is the margin of error? (Round your answers to one decimal place.)

lower limit:

upper limit:

margin of error:

(d) Compare the margins of error for parts (a) through (c). As the confidence levels increase, do the margins of error increase?

As the confidence level increases, the margin of error increases.

As the confidence level increases, the margin of error remains the same.

As the confidence level increases, the margin of error decreases.

(e) Compare the lengths of the confidence intervals for parts (a) through (c). As the confidence levels increase, do the confidence intervals increase in length?

As the confidence level increases, the confidence interval decreases in length.

As the confidence level increases, the confidence interval remains the same length.

As the confidence level increases, the confidence interval increases in length.

Consider the Nomad Machine Company (NMC) problem from class. As the problem stated: "you should develop two flow models" because NMC has sought "to build a new factory to expand its production capacity". Your task was to determine which factory to recommend.

In this discussion assignment, upload the JPG outcome for the optimal solution. Your uploaded solution should have a similar 'look and feel' professionalism as the JPG file on the external site (figure TP-F8). Make sure to use solver so the pink cells are populated. Also, using this optimal site selection, provide the operating costs for Cincinnati, Kansas City, and Pittsburgh.

A student group believes that less than 50% of students find their college experience extremely rewarding. They decide to test this hypothesis using a significance level of .05. They conduct a random sample of 100 students and 34 say they find their college experience extremely rewarding.

Based on the type of test this is (right, left, or two-tailed); determine the following for this problem.

4. Critical Value(s): _______________________

5. P-value Table A.3 _______________________ P-value Calculator:________________

P-value Table A.2 _______________

6: Can you reject? _______________________

7. Conclusion: Can we conclude or can we not conclude less than 50% of students find their college experience extremely rewarding? (write the conclusion in a sentence)

A survey found that​ women's heights are normally distributed with mean 62.3 in. and standard deviation 3.6 in. The survey also found that​ men's heights are normally distributed with mean 67.7 in. and standard deviation 3.3 in. Consider an executive jet that seats six with a doorway height of 55.7 in. Complete parts​ (a) through​ (c) below.

a. What percentage of adult men can fit through the door without​ bending?

The percentage of men who can fit without bending is

___.

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

b. Does the door design with a height of

55.755.7

in. appear to be​ adequate? Why​ didn't the engineers design a larger​ door?

A.

The door design is​ inadequate, but because the jet is relatively small and seats only six​ people, a much higher door would require major changes in the design and cost of the​ jet, making a larger height not practical.

B.

The door design is​ adequate, because the majority of people will be able to fit without bending.​ Thus, a larger door is not needed.

C.

The door design is​ adequate, because although many men will not be able to fit without​ bending, most women will be able to fit without bending.​ Thus, a larger door is not needed.

D.

The door design is​ inadequate, because every person needs to be able to get into the aircraft without bending. There is no reason why this should not be implemented.

c. What doorway height would allow​ 40% of men to fit without​ bending?

The doorway height that would allow​ 40% of men to fit without bending is

__

in.

​(Round to one decimal place as​ needed.)

Let x be a random variable that represents white blood cell count per cubic milliliter of whole blood. Assume that x has a distribution that is approximately normal, with mean μ = 6950 and estimated standard deviation σ = 2650. A test result of x < 3500 is an indication of leukopenia. This indicates bone marrow depression that may be the result of a viral infection. (a) What is the probability that, on a single test, x is less than 3500? (Round your answer to four decimal places.) (b) Suppose a doctor uses the average x for two tests taken about a week apart. What can we say about the probability distribution of x? The probability distribution of x is not normal. The probability distribution of x is approximately normal with μx = 6950 and σx = 1873.83. The probability distribution of x is approximately normal with μx = 6950 and σx = 1325.00. The probability distribution of x is approximately normal with μx = 6950 and σx = 2650. What is the probability of x < 3500? (Round your answer to four decimal places.) (c) Repeat part (b) for n = 3 tests taken a week apart. (Round your answer to four decimal places.) (d) Compare your answers to parts (a), (b), and (c). How did the probabilities change as n increased? The probabilities decreased as n increased. The probabilities stayed the same as n increased. The probabilities increased as n increased. If a person had x < 3500 based on three tests, what conclusion would you draw as a doctor or a nurse? It would be a common event for a person to have two or three tests below 3,500 purely by chance. The person probably does not have leukopenia. It would be a common event for a person to have two or three tests below 3,500 purely by chance. The person probably has leukopenia. It would be an extremely rare event for a person to have two or three tests below 3,500 purely by chance. The person probably has leukopenia. It would be an extremely rare event for a person to have two or three tests below 3,500 purely by chance. The person probably does not have leukopenia.

The mass of plants in a botany lab are normally distributed with a mean of 54 grams and a standard deviation of 6.5 grams. Use this to compute : a. The probability that one randomly chosen plant will have a mass that less than 49.25 grams b. The probability that one randomly chosen plant will have a mass that is between 52 grams and 62 grams. c. The mass of a plant which weighs less than the top 15% of plants in the lab. d. The probability that 25 randomly chosen plants will have a mean mass of no more than 50 grams

What is the critical F value when the sample size for the numerator is sixteen and the sample size for the denominator is ten? Use a two-tailed test and the 0.02 significance level. (Round your answer to 2 decimal places.)

An evaluation was recently performed on brands and data were collected that classified each brand as being in the technology or financial institutions sector and also reported the brand value. The results in terms of value​ (in millions of​ dollars) are shown in the accompanying data table. Complete parts​ (a) through​ (c).

BRAND VALUES:

Technology: 281, 377, 491, 429, 406, 584, 641, 624

Financial Institutions: 517, 832, 819, 804, 937, 995, 1035, 1094

a.)

Assuming the population variances are​ equal, is there evidence that the mean brand value is different for the technology sector than for the financial institutions​ sector? (Use α=0.05​.)

Determine the hypotheses. Let μ1 be the mean brand value for the technology sector and μ2 be the mean brand value for the financial institutions sector. Choose the correct answer below.

A.) H0: μ1= μ2

H1: μ1≠μ2

B.) H0: μ1≤ μ2

H1: μ1 > μ2

C.) H0: μ1≥ μ2

H1: μ1< μ2

D.) H0: μ1≠μ2

H1: μ1=μ2

-Find the test statistic.

^tSTAT=___

-Choose the correct answer below.

A. Reject H0. There is sufficient evidence that the mean brand value for the technology sector is different from the mean brand value for the financial institutions sector.

B. Reject H0. There is insufficient evidence that the mean brand value for the technology sector is different from the mean brand value for the financial institutions sector.

C. Do not reject H0. There is sufficient evidence that the mean brand value for the technology sector is different from the mean brand value for the financial institutions sector.

D.Do not reject H0. There is insufficient evidence that the mean brand value for the technology sector is different from the mean brand value for the financial institutions sector.

b.)

-Repeat​ (a), assuming that the population variances are not equal.

A.) H0: μ1= μ2

H1: μ1≠μ2

B.) H0: μ1≤ μ2

H1: μ1 > μ2

C.) H0: μ1≥ μ2

H1: μ1< μ2

D.) H0: μ1≠μ2

H1: μ1=μ2

-FInd the test statistic.

^tSTAT=____

-Choose the correct answer below.

A. Reject H0. There is sufficient evidence that the mean brand value for the technology sector is different from the mean brand value for the financial institutions sector.

B. Reject H0. There is insufficient evidence that the mean brand value for the technology sector is different from the mean brand value for the financial institutions sector.

C. Do not reject H0. There is sufficient evidence that the mean brand value for the technology sector is different from the mean brand value for the financial institutions sector.

D.Do not reject H0. There is insufficient evidence that the mean brand value for the technology sector is different from the mean brand value for the financial institutions sector.

c.)

-Compare the results of (a) and (b).

A. The conclusions for parts​ (a) and​ (b) are different. Reject the null hypothesis in​ (b) and do not reject it in​ (a).

B.The conclusions for parts​ (a) and​ (b) both reject the null hypothesis.

C.The conclusions for parts​ (a) and​ (b) both do not reject the null hypothesis.

D. The conclusions for parts​ (a) and​ (b) are different. Reject the null hypothesis in​ (a) and do not reject it in​ (b).

The following counts of vehicles arriving at a toll station, over 1-minute intervals, were made by an engineering student: 2,0,4,1,2,4,2,1,6,2,5,4,4,3,3,2,2,1,2,3,2,5,2,0,1,2,0,3,1,1, 2,3,5,6,3,4,3,4,0,6,3,2,1,4,4,0,1,4,3,7,0,0,2,3,2,4,3,2,4,5 (a) Assuming the vehicle arrivals were generated by a Poisson process, compute the maximum likelihood estimate of the arrival rate. (b) Test the hypothesis that the arrival counts follow a Poisson distribution, at the α=0.05 significance level.

Drive-through Service Time at McDonald’s When you are on the go and looking for a quick meal, where do you go? If you are like millions of people every day, you make a stop at McDonald’s. Known as “quick service restaurants” in the industry (not “fast food”), companies such as McDonald’s invest heavily to determine the most efficient and effective ways to provide fast, high quality service in all phases of their business. Drive-through operations play a vital role. It’s not surprising that attention is focused on the drive-through process. After all, over 60% of the individual restaurant revenues in the United States come from the drive-through operations. Yet understanding the process is more complex than just counting cars. Marla King, professor at the company’s international training center, Hamburger University, got her start 25 years ago working at a McDonald’s drive-through. She now coaches new restaurant owners and managers. “Our stated drive-through service time is 90 seconds or less. We train every manager and team member to understand that a quality customer experience at the drive-through depends on them,” says Marla. Some of the factors that affect a customers’ ability to complete their purchases with 90 seconds include restaurant staffing, equipment layout in the restaurant, training, and efficiency of the grill team, and frequency of customer arrivals to name a few. Customer order patterns also play a role. Some customers will just order drinks, while others seem to need enough food to feed an entire soccer team. And then there are the special orders. Obviously, there is plenty of room for variability here. Yet that doesn’t stop the company from using statistical techniques to better understand the drive-through action. In particular, McDonald’s utilizes numerical measures of the center (mean) and spread (variance) in the data and to help transform the data into useful information. In order for restaurant managers to achieve the goal in their own restaurants, they need training in proper restaurant and drive-through operations. Hamburger University, McDonald’s training center located near Chicago, Illinois, satisfies that need. In the mock-up restaurant service lab, managers go through a “before and after” training scenario. In the “before” scenario, they run the restaurant for thirty minutes as if they were back in their home restaurants. Managers in the training class are assigned to be crew, customers, drive-through cars, special needs guests (such as hearing impaired), or observers. Statistical data about the operations, revenues, and service times are collected and analyzed. Without the right training, the restaurant’s operation usually starts breaking down after 10-15 minutes. After debriefing and analyzing the data collected, the managers make suggestions for adjustments and head back to the service lab to try again. This time, the results usually come in well within standards. “When presented with the quantitative results, managers are pretty quick to make the connections between better operations, higher revenues, and happier customers,” Marla states. When managers return to their respective restaurants, the training results and techniques are shared with staff who are charged with implementing the ideas locally. The results of the training eventually are measured when McDonald’s conducts a restaurant operations improvement process study, or ROIP. The goal is simple: improved operations. When the ROIP review is completed, statistical analyses are performed and managers are given their results. Depending on the results, decisions might be made that require additional financial resources, building construction, staff training, or reconfiguring layouts. Yet one thing is clear: Statistics drive the decisions behind McDonald’s drive-through service operations.

Questions:

1. After returning from the training session at Hamburger University, a McDonald’s store owner
selected a random sample of 323 drive-through customers and carefully measured the time it took
from when a customer entered the McDonald’s property until the customer had received the order at
the drive-through window.
These data are provided, using Excel spreadsheet. Note that the owner
selected some customers during the breakfast period, others during lunch or dinner time. For the
overall sample, compute the key measures of the central tendency and variation.
Based on these measures, what conclusion might the owner reach with respect to how well his store is
doing in meeting the 90 second customer service goal? Support your argument with appropriate
hypothesis testing.

2. Compute the key measures of central tendency and variation for drive-through times broken down by
breakfast, lunch, and dinner time periods. Based on these calculations, does it appear that the store is
doing better at one of these time periods than the others in providing shorter drive-through waiting
times? Support your argument with appropriate hypothesis testing.

3. Determine if there are any outliers in the sample data. Discuss.

show the steps of doing it in excel when answering it please

Share a link to a news article or study that you think exhibits a sampling error or bias. Briefly describe what you think the issue is and how you might fix it.