USA spends about 54% of its total federal budget on the Military. Military spending in the USA is far greater than any nation in the world. (USA spends more on military than the next 10 highest spenders combined, 4 times China who is second on the list etc) Why does USA need to spend so much more on its military than other countries (cause), and what is/are the results of all this spending (effect)?

The essays should not be a simple list of causes or effects, but an essay that takes a position on why a condition exists and or argues the results of this condition. Some writers may even be able to create a strong focused thesis by suggesting a solution or arguing for some clear change of policy. The goal is to create an essay that allows you to develop a position or argument in a cause and effect essay. A strong thesis statement is very important and students should underline it in the final draft of their essay. Essay should be 4 pages long, double spaced , and it must have a minimum of 3 academic sources—no Wikipedia, encyclopedias, dictionaries etc. Use the library resources to find legitimate, academic sources

Suppose you need to examine the relationship between wages (in $1,000) and the variables: experience in the field (Exper), number of academic degrees (Degrees), and number of previous jobs in the field (Prevjobs). Experience in the field is measured in years.

You took a sample of 20 employees and obtained the following output ( Must show your work otherwise you get half credit):

                         Coeff                   StdError              t Stat              p-value

Intercept               -7.23                     2.52                 -2.87               0.011

Exper                   -0.15                     0.18                ?????                 0.41

Degrees                 ????                     0.80                    9.1               0.000

Prevjobs               -0.65                     0.52                   -1.25               ????

a)  Compute the t statistic for the experience in the field (Expr).                                                    

b)  Interpret the coefficient for Expr.  Is it reasonable?                                                                 

c) Compute the coefficient  for the number of academic degrees (Degrees)                               

d) State the multiple regression equation.                                                                                       

e) Estimate the p_value of the number of previous jobs in the field (Prevjobs)                        

f) Interpret the coefficient of multiple determination    (R_square)                                               (3  marks)

g) Predict the wage for a person with 6 years of experience, 3 degrees, and 2 previous jobs (interpret the results).                                                                                                                                                                                                           

h)  Use the p_values to confirm which variables are significant at α=0.05?  (do not conduct the 5 steps. Just state why).                                                                                                           

Capstone Case H: Cost-Effectiveness Analysis of Type II Diabetes

Diabetes is a major health problem, particularly for the millions of Americans with undiagnosed diabetes, for whom treatment and glycemic control could substantially reduce the onset of complications of this disease. The CDC Diabetes Cost-Effectiveness Group has published a number of articles based on cost-effectiveness analyses (CEA) using a sophisticated Markov simulation model. This probability- based model predicts the onset of diabetes in a hypothetical cohort of patients and follows them as they transition into the various disease states associated with complications and ultimately death. The first analysis (1998) estimates the cost-effectiveness of one-time opportunistic screening (i.e., done during routine contact with a health system). Two cohorts were used in this study, (1) a hypothetical population without diabetes assigned to either opportunistic screening or current clinical practice, and (2) a hypothetical cohort of 10,000 newly diagnosed diabetics who are followed for the development of major complications under the two screening alternatives. The second analysis (2002) estimates the cost-effectiveness of three interventions for the hypothetical cohort of 10,000 newly diagnosed diabetics: (1) intensive glycemic control; (2) intensive hypertension control; and (3) reduction in serum cholesterol. Hoerger and colleagues (2004) use the CDC Markov model to estimate the cost-effectiveness of two screening strategies: (1) diabetes screening targeted at those individuals with hypertension and (2) universal diabetes screening.

Questions

1. What is the difference between cost–benefit, cost-effectiveness, and cost–utility analysis?

2. What is the relationship between cost and effectiveness? Does more effectiveness always cost more money?

3. When doing CEA it is important to identify the perspective from which the analysis is conducted. In other words, from whose perspective are the costs and benefits recognized? What are the different perspectives? With the diabetes CEA, a single-payer perspective is assumed. What does this mean, and what kinds of costs are ignored?

4. What kinds of costs are usually included in a CEA? The diabetes CEA included screening costs, treatment costs, diabetes intervention costs, and diabetes complication costs. Under what category of costs would screening and treatments fall?

An economist with a major bank wants to learn, quantitatively, how much spending on luxury goods and services can be explained based on consumers’ perception about the current state of the economy and what do they expect in the near future (6 months ahead).  Consumers, of all income and wealth classes, were surveyed.  Every year, 1500 consumers were interviewed.  The bank having all of the data from the 1500 consumers interviewed every year, computed the average level of consumer confidence (an index ranging from 0 to 100, 100 being absolutely optimistic) and computed the average dollar amount spent on luxuries annually.  Below is the data shown for the last 24 years.

Date                 X                     Y (in thousands of dollars)

1994                79.1                 55.6

1995                79                    54.8

1996                80.2                 55.4

1997                80.5                 55.9

1998                81.2                 56.4

1999                80.8                 57.3

2000                81.2                 57

2001                80.7                 57.5

2002                80.3                 56.9

2003                79.4                 55.8

2004                78.6                 56.1

2005                78.3                 55.7

2006                78.3                 55.7

2007                77.8                 55

2008                77.7                 54.4

2009                77.6                 54

2010                77.6                 56

2011                78.5                 56.7

2012                78.3                 56.3

2013                78.5                 57.2

2014                78.9                 57.8

2015                79.8                 58.7

2016                80.4                 59.3

2017                80.7                 59.9

Questions:

1. Measure the strength of the linear association between consumers’ moods and the dollar amounts spent on luxury items.
2. Construct the linear regression model for the dollar amount spent on luxury goods and services.
3. Explain how you would interpret the slope and the intercept of the regression model.
4. How well does our model fit the data? Explain what it means.
5. Do you think that measuring the level of optimism is a good predictor for trying to forecast future spending on luxury items?  Explain why or why not.
6. How would you be able to improve on the model?  You must provide a minimum of two specific ways to go about improving the model.
7. If the economist expects that, by year’s end, the average level of consumer confidence will hit 81.5 points, how much will be expected by consumers to spend on luxury items?

4. There is data for you in the tab called EComSales. It comes from the Federal Reserve and represents quarterly e-commerce sales data in the U.S. for Quarter 4, 1999 to Quarter 4, 2019. Month 1=Q1, Month 4=Q2, Month 7=Q3, Month 10 = Q4. Run a regression forecasting sales for all 4 quarters in 2020. Print your regression results in a new tab. Rename that tab Answer Q4. In that cells below your regression results, forecast sales for Q1:2020, Q2:2020, Q3:2020, and Q4:2020. Round all answers to the nearest dollar in Excel and put a comma in so I can read it easier (do not round by hand or put the comma in by hand– set up excel to do the rounding and the comma for you).

IT IS NOT LETTING ME POST CORRECTLY, THE COLUMN OF 5553 IS FOR Q1, THE 6059 FOR Q2, THE 6892 FOR Q3 AND THE 5241 FOR Q4

PLEASE EXPLAIN STEP BY STEP AND PUT EXCEL FORMULAS! THANK YOU

Is the number of tornadoes increasing? In the last homework, data on the number of tornadoes in the United States between 1953 and 2014 were analyzed to see if there was a linear trend over time. Some argue that it’s not the number of tornadoes increasing over time, but rather the probability of sighting them because there are more people living in the United States. Let’s investigate this by including the U.S. census count (in thousands) as an additional explanatory variable (data in EX11-24TWISTER.csv).

Perform a multiple regression using both year and census count as explanatory variables. Write down the fitted model. Are year and census count respectively significant in the MLR model?

Is the number of tornadoes increasing? In the last homework, data on the number of tornadoes in the United States between 1953 and 2014 were analyzed to see if there was a linear trend over time. Some argue that it’s not the number of tornadoes increasing over time, but rather the probability of sighting them because there are more people living in the United States. Let’s investigate this by including the U.S. census count (in thousands) as an additional explanatory variable (data in EX11-24TWISTER.csv).

Fit one SLR model with year as the predictor, another SLR model with census count as the predictor. Write down the two models. Are year and census count significant, respectively?

Part 1

Calculate the missing values in the table below. Then answer the questions that follow it. GDP are in billions of dollars and the Consumer Price Index (CPI) is a percentage. CPI for 2001 is 98.6

Year Nominal GDP CPI RealGDP ri

2002 $10,469.58 Billion 100.0

2003 $10,971.34 Billion 102.3

2004 $11,734.30 Billion    105.0

2005 $12,601.00 Billion 108.6

All figures must be calculated to 2 decimal places and in the correct formats on a separate paper. You must show your work for all calculations in order to receive credit for the problem. DO NOT FILL IN THE TABLE. WORK PROBLEMS ON SEPARATE PAPER AND PRODUCE ANSWERS THERE!

a. Has there been any span of years, IN ONE YEAR INCREMENTS, within the table over which nominal GDP changed in one direction, but real GDP changed in the opposite direction? (Examples of what a span of years is, that are not included in this table would be 1990-1991 or 1996-1997.) Explain why or why not.

b. Has there been inflation over each span of years in the table? Explain why or why not.

Part 2

Exchange rate sample problems:

STARTING RATE LATER AFTER TIME HAS PASSED

a. Rate I: USD $1.54 = GBP £1.00 Rate II: USD $1.39 = GBP £1.00   PJeans (US Export) = USD $35.00 PSuit (UK Export) = £180.00

b. Rate I: USD $1.28 = EUR €1.00 Rate II: USD $1.45 = EUR €1.00

PDesk (US Export) = USD $345.00 PCoffee Maker (EU Export) = €50.00

c. Rate I: USD $1.00 = CNY 9.20元 Rate II: USD $1.00 = CNY 8.75 元

PBushel of Corn (US Export) = USD $45.00 PFlat-Screen TV (Chinese Export) = 12,500.00 元

Calculate the price of each nation’s exported good in terms of the other nation’s currency for BOTH EXCHANGE RATES (THERE WILL BE 4 CALCULATIONS IN EACH SECTION a, b, and c AS A RESULT). For Each Problem, based upon how the prices change from rate I to rate II, determine for each nation the impact on Net Export Spending,Total Spending, GDP, and AD. Make sure TO USE THE APPROPRIATE CURRENCY SYMBOLS FOR THE BRITISH POUND, THE EURO, AND THE CHINESE YUAN RENMINBI.

Case study

Rachael Tomkins is 55 years old and is a certified practising accountant. She works part time and lives with her husband Paul, aged 64 and daughter Marie, aged 17. Her grandmother Jean aged 90, lives in a small flat at the back of their house and her mother Mary, aged 72 lives in an Over 55s housing unit nearby. In her early 20s Rachael’s father, a Vietnam Veteran, committed suicide. Rachael is described by her family as reliable and caring. She has a small group of friends from her local parish church. Rachael has regular contact with her GP to manage her Diabetes Type 2. She is prescribed metformin and has been trying to lose weight. She also sees a psychiatrist Dr Lianne Yu for management of her symptoms of schizophrenia. She is prescribed Olanzapine and Lithium. She was diagnosed with schizophrenia in her early 20’s when she was studying at university. She was hospitalised with acute psychosis several times before her symptoms were stabilised. She was able to complete her university degree and has worked part time. The last time she experienced acute psychosis was 17 years ago, just after the birth of her daughter. Her symptoms stabilised, and she has been maintained in recovery for almost 15 years. This year has been a particularly challenging year for Rachael. Both her husband’s parents passed away within months of each other, her daughter commenced Year 12 and her grandmother had an infection in her middle toe, which resulted in a series of trips to the doctor, hospitalization and finally amputation of the affected toe. Rachael has become irritable with her family, and has developed erratic sleeping patterns, a lack of interest in grooming, and avoided social interactions with her friends or family. She complained to them that her neighbors were spying on her. In the 48 hours before she was admitted to hospital two incidents escalated Rachael’s need for professional help. In the first episode, she yelled and threatened the neighbor across the fence. She accused him of spying on her with a ‘trackamanometer’. Her husband intervened and took her back into the house. In the second incident later that day, Rachael started screaming at her family to evacuate the house because they would be bombed. Rachael insisted the newsreader on the TV was giving her this important information and they must all get out of the house. Rachael ran onto the road. A concerned neighbor called the police, who were able to convince her to accompany them to the hospital. She was met by her psychiatrist Dr. Yu who reports the following -Rachael is disheveled, dressed in a pajama top and track pants, no shoes, she has an exacerbation of auditory hallucinations, with persecutory delusions and disorganized thinking. Rachael agrees to be admitted because she says ‘I’m frightened’. Rachael is admitted for inpatient psychiatric care. Faculty of Health | School of Nursing, Midwifery & Paramedicine In the hospital, Rachael is argumentative and resistive to staff interactions and interventions, and her family are frightened and bewildered by her dramatic deterioration.

Q. Rachael will be admitted to the mental health inpatient unit. Write a nursing care plan based on the nursing diagnosis.

Q. What are the risk factors?  Does Racheal have any protective factors? If so, what are they?

can you please provide answer to these questions from the above case study.

thank you.....