Short Response Question (30%)
Is the unipolar world more peaceful than a multipolar and/or bipolar world? Why or why not? What effects does unipolarity have on the unipole itself and on other states in the international system?
In: Economics
Design a state machine to recognize if a binary string contains any occurrence of the sequence “10101”. Is it possible to design this state machine with less states? By using Finite State machine designer.
http://madebyevan.com/fsm/
In: Electrical Engineering
In: Biology
Question 29.6 on p. 982 of the textbook university physics 13th edition states that a magnet would reach terminal velocity even if there is no air resistance. How would air resistance change the situation? Is it significant?
In: Physics
| Chief Complaint: Nausea, vomiting, progressive weakness, and weight loss |
| History of Present Illness: 68-year-old female presented to Emergency Department with nausea and vomiting for several days following weeks of poor appetite and increasing weakness. Patient is dehydrated and complains of generalized abdominal pain. CT of abdomen shows mass in right lower quadrant of abdomen. |
| Allergies: Sensitivity to penicillin and cephalosporins |
| Past Medical History: Patient has a history of rectal polyps, atrial fibrillation for the past 8 years and severe osteoarthritis for the past 20 years. |
| Surgical History: Arthroscopic knee surgery approximately 20 years ago for right knee pain. Patient states she had Meperidine for postop pain control at that time. Requests Meperidine at this time for pain management. |
| Gyn History: Postmenopausal for approximately 18 years |
| OB History: Gravida 3, para 2, has two healthy adult children. Had one miscarriage at about 6 weeks gestation. |
| Social History: Born in Thailand. Married American GI and moved to the United States. Patient is primary caregiver for her 84-year-old mother. Patient is college educated in the U.S., but the language used at home is primarily Thai. Patient denies smoking or alcohol use. Patient denies illicit drug use. |
| Family History: Father died at age 70 from colon cancer. Sister, age 66, is currently being treated for ovarian cancer. Mother has debilitating arthritis and scoliosis, but is otherwise healthy. |
| Medications: Home medications include Digoxin 0.125 mg daily, Warfarin 5 mg daily, Celecoxib 200 mg every 12 hours. Specifically requests Meperidine for pain control. |
| Review of Systems: HEENT: Denies headaches, vertigo, syncope, changes in vision or hearing. No problems with chewing or swallowing. Cardiovascular system: Denies chest pain or palpitations. Patient states she has had A-fib for a number of years. Denies problems with circulation. Respiratory system: No shortness of breath or pain with breathing. Denies cough. No recent colds. Gastrointestinal system: Reports ribbon-like stools and blood in stools recently--has not been able to have BM for last several days. C/O nausea, pain, and decreased appetite. Musculoskeletal system: Complains of joint pain in knees--states she has arthritis. Sometimes this makes ambulation difficult. No other complaints. |
| Physical Exam: GENERAL: Well-developed 68-year-old Asian female |
| VITAL SIGNS: BP 108/60 P 110 R 20 T 98.8 F O2 sat 95% on 2L via nasal cannula |
| HEENT: Normal, pupils round, reactive to light. No lymph nodes palpable. |
| LUNGS: Breath sounds are clear, somewhat diminished in the bases. Patient is somewhat tachypneic due to abdominal pain. Equal expansion, patient denies cough. |
| HEART: Irregular rhythm, S1 S2, no S3, no murmurs, clicks or rubs. Pulses 1+, no edema or jugular venous distention. Digoxin level of 2.1 on admission labs. |
| ABDOMEN: Abdomen firm and distended. Patient complains of generalized tenderness and pain with palpation. Bowel sounds are hyperactive. |
| EXTREMITIES: Moves all extremities. Scar noted right knee status post arthroscopy. Some joint deformity noted in hands. |
| SKIN: Intact, no redness or bruising noted |
| BACK: Negative assessment except for slight stooping of the shoulders |
| GENITALIA: External genitalia normal |
| NEUROLOGIC: Awake, alert, no neurologic deficits noted. Cranial nerves I-XII intact. |
| Impression: 68-year-old female in moderate distress due to bowel obstruction, possibly secondary to abdominal mass. |
| Plan: 1. Decompress abdomen. 2. Correct electrolyte imbalance. 3. Reverse anticoagulation. 4. Surgical removal of abdominal mass. |
question
Medical Surgical Nursing Clinical – Concept Map
|
Patient info: Vital Signs and Labs Report: |
Pathophysiology: |
|
Priority 1 Nursing Diagnosis: |
|
Goal: Outcome: |
|
Nursing Interventions: |
|
Evaluation: (Expected Outcome) |
|
Patient Education with explanation related to the patient’s health status and health promotion: (Please list in bullet points, type your answers in black fonts): |
References:
In: Nursing
| Chief Complaint: Nausea, vomiting, progressive weakness, and weight loss |
| History of Present Illness: 68-year-old female presented to Emergency Department with nausea and vomiting for several days following weeks of poor appetite and increasing weakness. Patient is dehydrated and complains of generalized abdominal pain. CT of abdomen shows mass in right lower quadrant of abdomen. |
| Allergies: Sensitivity to penicillin and cephalosporins |
| Past Medical History: Patient has a history of rectal polyps, atrial fibrillation for the past 8 years and severe osteoarthritis for the past 20 years. |
| Surgical History: Arthroscopic knee surgery approximately 20 years ago for right knee pain. Patient states she had Meperidine for postop pain control at that time. Requests Meperidine at this time for pain management. |
| Gyn History: Postmenopausal for approximately 18 years |
| OB History: Gravida 3, para 2, has two healthy adult children. Had one miscarriage at about 6 weeks gestation. |
| Social History: Born in Thailand. Married American GI and moved to the United States. Patient is primary caregiver for her 84-year-old mother. Patient is college educated in the U.S., but the language used at home is primarily Thai. Patient denies smoking or alcohol use. Patient denies illicit drug use. |
| Family History: Father died at age 70 from colon cancer. Sister, age 66, is currently being treated for ovarian cancer. Mother has debilitating arthritis and scoliosis, but is otherwise healthy. |
| Medications: Home medications include Digoxin 0.125 mg daily, Warfarin 5 mg daily, Celecoxib 200 mg every 12 hours. Specifically requests Meperidine for pain control. |
| Review of Systems: HEENT: Denies headaches, vertigo, syncope, changes in vision or hearing. No problems with chewing or swallowing. Cardiovascular system: Denies chest pain or palpitations. Patient states she has had A-fib for a number of years. Denies problems with circulation. Respiratory system: No shortness of breath or pain with breathing. Denies cough. No recent colds. Gastrointestinal system: Reports ribbon-like stools and blood in stools recently--has not been able to have BM for last several days. C/O nausea, pain, and decreased appetite. Musculoskeletal system: Complains of joint pain in knees--states she has arthritis. Sometimes this makes ambulation difficult. No other complaints. |
| Physical Exam: GENERAL: Well-developed 68-year-old Asian female |
| VITAL SIGNS: BP 108/60 P 110 R 20 T 98.8 F O2 sat 95% on 2L via nasal cannula |
| HEENT: Normal, pupils round, reactive to light. No lymph nodes palpable. |
| LUNGS: Breath sounds are clear, somewhat diminished in the bases. Patient is somewhat tachypneic due to abdominal pain. Equal expansion, patient denies cough. |
| HEART: Irregular rhythm, S1 S2, no S3, no murmurs, clicks or rubs. Pulses 1+, no edema or jugular venous distention. Digoxin level of 2.1 on admission labs. |
| ABDOMEN: Abdomen firm and distended. Patient complains of generalized tenderness and pain with palpation. Bowel sounds are hyperactive. |
| EXTREMITIES: Moves all extremities. Scar noted right knee status post arthroscopy. Some joint deformity noted in hands. |
| SKIN: Intact, no redness or bruising noted |
| BACK: Negative assessment except for slight stooping of the shoulders |
| GENITALIA: External genitalia normal |
| NEUROLOGIC: Awake, alert, no neurologic deficits noted. Cranial nerves I-XII intact. |
| Impression: 68-year-old female in moderate distress due to bowel obstruction, possibly secondary to abdominal mass. |
| Plan: 1. Decompress abdomen. 2. Correct electrolyte imbalance. 3. Reverse anticoagulation. 4. Surgical removal of abdominal mass. |
question
Medical Surgical Nursing Clinical – Concept Map
|
Patient info: Vital Signs and Labs Report: |
Pathophysiology: |
|
Priority 1 Nursing Diagnosis: |
|
Goal: Outcome: |
|
Nursing Interventions: |
|
Evaluation: (Expected Outcome) |
|
Patient Education with explanation related to the patient’s health status and health promotion: (Please list in bullet points, type your answers in black fonts): |
References:
In: Nursing
Case Study: Supply Chain Trends The Do-Green Solar Systems case addresses challenges faced by a Canadian manufacturer as a result of the CUSMA trade agreement. As you read through the case, think abou the challenges, risks and complexities in changing their supply chain from North Americanto Internationalmarkets. Do-Green Solar Systems Taylor Douglas, V.P of Do-Green Solar Systems, was evaluating the strategic position of the company. With the new Canada-United States-Mexico (CUSMA) agreement in place and the uncertainty around future trade with the United States Taylor was pondering the future direction of Do-Green. Do-Green’s History Taylor grew up in the family business. Established in 2000 Do-Green began as a family run electrical contracting company. Their core business focused on providing residential electrical contracting for new home construction as well as renovations and electrical upgrades to existing homes. As the business grew Taylor began to deal more and more with requests from customers for solar power options for their homes. Taylor realized that the market for residential solar power was growing. Supply agreement/partnership attempts with solar component suppliers proved to be unreliable. It was at that point Taylor decided to purchase a facility to begin manufacturing solar power components for residential use. In 2004 Do-Green Solar Systems was formed. Do-Green was now involved in both the manufacture and installation of solar power systems for residential use. The business saw steady growth through 2006. Do-Green had established a lucrative business niche for itself. New Opportunities At the same time that Do-Green was establishing itself, Canadian’s saw the expansion of big box home improvement retailers and the proliferation of the “do-it-yourself” craze. In 2008 Taylor Douglas approached several home improvement retailers and in 2009 Do-Green signed a supply agreement with a big box home improvement retailer to stock their products in 25 stores across Ontario. People could now purchase and install their own residential solar power systems and Do-Green’s business profile evolved into that of a manufacturer/distributor. To meet the increased production demands Do-Green acquired a local mid-size manufacturing facility. For the next two years Do-Green settled into its new business model as installer, manufacturer and retail distributor of solar power systems for residential use. Do-Green Becomes Leaner and Looks to New Markets Not one to rest on past successes, Taylor began to look at ways to grow the business. It was now 2011. The Canadian dollar was at par with the U.S. dollar and Taylor wanted to break into the U.S. market. To do that additional capacity needed to be purchased or Do-Green needed to find ways to run their operation more effectively and efficiently. Taylor decided to look within the company for capacity improvement opportunities. Do-Green increased their capacity through several initiatives. They invested in an ERP system which allowed then to increase productivity and fully integrate the ordering and procurement process. Supply chain visibility increased. Do-Green could now receive replenishment orders from retailers directly into their system. This enabled them to reduce raw material, work in process and finished goods inventories by a combined 20%. Do-Green also implemented lean process integration throughout their operation. This accounted for an additional 15% increase in production capacity. Once fully implemented these initiatives accounted additional capacity of 30%. Delivery times were reduced from three days to one. With the newly found capacity Taylor approached the U.S. affiliate of the Canadian home improvement retailer. In 2012 Do-Green signed a contract to supply 30 U.S. based stores throughout the North East states. For the next several years Do-Green established themselves as a major stakeholder in the residential solar power industry. The Canadian Dollar Loses Value In 2014 the Canadian dollar began to lose value against the U.S. dollar. Taylor and the Do-Green management team looked to further streamline their manufacturing and distribution network. Profits began to shrink as the devalued Canadian dollar began to become a real issue for Do-Green shareholders. However, even with the exchange rate being what it was, the company remained strong and profits were steady. Do-Green Goes Green With consistent demand and a reliable and robust supply/distribution system in place in both Canada and the U.S. Taylor began to focus on sustainability issues within the supply chain. Much of the dunnage and packaging Do-Green used to ship their product to retail distributors could be reused. Taylor began to develop a reverse supply chain where packaging and dunnage was returned to the Do-Green manufacturing facility to be used again. This initiative helped to further Do-Greens reputation of being a sustainable and environmentally conscious organization. Cost savings were also realized through the reverse supply chain program which helped offset the ongoing disparity between the Canadian and U.S. dollar. The New Frontier As Taylor Douglas pondered the new strategic direction of Do-Green, Taylor knew the exact date that Do-Green’s future was in jeopardy. On November 30, 2018 the (CUSMA) Canada United States Mexico agreement was signed. This new trade agreement took the place of the long standing NAFTA trade agreement. Under the CUSMA agreement Do-Green now faced higher tariffs to export into the U.S. This combined with an even weaker Canadian dollar meant that Do-Green had to change direction. The U.S. market was no longer viable. Taylor and the Do-Green management team knew there were market entry opportunities offshore. With 1.4 billion people and 18% of the world’s population, China was the obvious choice. Do-Green had to develop a new international supply strategy if they wanted to do business in China. Issues and Concerns Concerns regarding exporting to China were many. Taylor knew there would be logistical issues. Currently trucks left their facility and delivered directly to retail stores in both Canada and the U.S. International supply chains required multi-tiered distribution systems. There would be currency issues to consider as well as the potential for theft of products, product design and company intelligence. ERP and technology compatibility with Chinese distribution partners was of concern. Do-Green’s operational concept of being a lean organization would be taxed. The longer the supply chain the more inventory investment was required. With a longer more diverse supply chain Taylor knew that risk would increase, supply chain visibility would decrease and overall control reduced. As a green company Do-Green would incur added cost to retain its circular supply chain. Taylor knew that reclaiming packaging from China posed significant logistical and cost considerations. Among other things to consider there was the risk of natural disasters, terrorism and labour disputes potentially disrupting the supply chain. Where to go From Here Taylor and the Do-Green management team had some significant strategic planning issues to consider. They understood supply chain trends were heading toward more diverse and complex systems in the delivery of products and services worldwide. They realized that they needed to resolve a significant number of issues if Do-Green wanted to compete in the global supply chain.
1. Name and explain at least three risks the company faces and what dimensions of supply chain risk these fall under.
In: Operations Management
This Homework would have two python files for a cheap online ticket seller. (You are free to imagine a new scenario and change any part of the question.)
The main purpose of the homework is to use basic concepts. You should try to use basic python elements like variables, if-else, loops, lists, dictionary, tuple, functions in this homework.
***
Price list to calculate the price for various destinations---
Price For Delta $ 200.00 (Economy), 300.00(Business), 400(First class)
United 220.00 (Economy), 330.00(Business), 410(First class)
Lufthansa 250.00(Economy), 340.00(Business), 410(First class)
American 260.00(Economy), 300.00(Business), 420(First class)
Price for Delta $ 250.00(Economy), 300.00(Business), 400(First class)
United $270.00 (Economy), 300.00(Business), 400(First class)
Lufthansa $ 290.00, (Economy), 300.00(Business), 400(First class)
American $ 280.00(Economy), 300.00(Business), 400(First class)
Price for Delta $ 350.00(Economy), 410.00(Business), 500.00(First class),
United 360.00(Economy), 4200.00(Business), 510(First class),
Lufthansa 370.00(Economy), 440.00(Business), 530(First class),
American 370.00(Economy), 430.00(Business), 510(First class)
Price For Delta $ 370.00(Economy), 410.00(Business), 520(First class),
United 380.00(Economy), 430.00(Business), 550(First class),
Lufthansa 390.00(Economy), 460.00(Business), 600.00(First class),
American 400.00(Economy), 470.00(Business), 590.00(First class)
For 6.00 AM and 11.00 AM flight, nothing to extra charge but 2.00 PM and & 7.00 flight has a $ 50.00 extra charge to book. However, the 11.00 PM flight has an extra charge of $ 70
***
Output: Do you Want to buy a ticket?
Enter Option 1 for buy ticket 2 for no:
How many people are you going to travel with? (Choose option Maximum 5):
Choose your destination from the below options menu:
What airline company do you want to travel with for destination:
Choose an option from the menu:
What class do you want to travel to? :
Choose an option from the menu:
What time would you like to travel to a destination? :
Choose an option from the menu:
The total price for your travel ticket is:
Enter Option 1 for buy ticket 2 for no:
In: Computer Science
1. If a researcher rejects a null hypothesis, what
type of error might they be committing?
a. Type I, alpha
b. Type II, beta
c. Type III, sigma
d. Type IV, gamma
2. The normal curve:
a. Is asymmetrical and asymptoptic
b. Has a mean, median and mode equal to zero
c. Has a standard deviation of zero
d. Can only be used as a descriptive statistic
3. The Central Limit Theorem:
a. Can only be applied to characteristics with a normal
distribution
b. Assumes a population distribution is normal
c. Allows us to use inferential statistics methods even
if a population distribution is not normal
d. States hypothesis testing should only be used if the
variables are continuous with normal distributions
4. A researcher runs a test for statistical
significance and gets a p-value of .003. What statements can be
made from this output?
a. There is a significant positive relationship between
the variables.
b. A beta error has been committed.
c. There is no relationship between the
variables.
d. There is a significant relationship between the
variables.
5. A researcher finds support for the following
hypothesis: There is a negative relationship between average
education level and divorce rates in states. Which of the following
interpretations could be made?
a. As average education level in states increase,
divorce rates increase.
b. As divorce rates in states increase, average
education levels increase.
c. As average education levels in states increase,
divorce rates decrease.
d. As divorce rates in states decrease, average
education levels decrease.
6. When an obtained value from a hypothesis test
falls in the critical region of a normal curve, any relationship
found between the variables is most likely due to:
a. Systematic influence
b. Chance
c. Beta error
d. Researcher error
7. Which of the following is an ordinal
variable?
a. Happiness measured as 1=happy or 2=not happy
b. Relationship status measured as 1=single,
2=divorced, 3=widowed, or 4=married
c. Life satisfaction measured as 1=very dissatisfied,
2= dissatisfied, 3= satisfied, 4=very satisfied
d. Happiness measured using an index score of 1 to
100
8. The most appropriate descriptive statistics to
report if happiness is measured as 1=happy and 2=not happy would
be:
a. Mean and standard deviation
b. Frequency distributions
c. Mean and ranges
d. None
9. Which of the following may be reported based on a
significance test?
a. Whether a relationship found between variables is
likely due to chance or not
b. Strength of a relationship between variables
c. Meaningfulness of a statistical relationship that is
found
d. Degree of influence of one variable on another
10. Inferential statistics allow us to:
a. Infer from a population to a sample
b. Infer from a sample to a population
In: Statistics and Probability
You will be performing an analysis on a dataset that contains data on fertility and life expectancy for 198 different countries. All data is from the year 2013. The fertility numbers are the average number of children per woman in each of the countries. The life expectancy numbers are the average life expectancy in each of the countries.
You will be turning in a paper that should include section headings, graphics and tables when appropriate and complete sentences which explain all analysis that was done in addition to all conclusions and results. There is not a specified length, however it is important that you follow all steps below and grade yourself using the rubric provided since it is the rubric that I will be using to grade your submissions. All work should be your own. Plagiarism will result in a project score of 0.
Steps (all statistical analysis to be done in Excel and/or StatCrunch):
Watch the TED talk by Hans Roling titled “The best stats you’ve ever seen”. You will need to include comments on this in your paper. Here is a link: http://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen?language=en
Create histograms of each of the variables (one histogram for fertility, one for life expectancy). Use the histograms to identify the shapes of the distribution. StatCrunch will be the easier tool to use for this particular task.
Calculate some descriptive statistics for each of the variables, including but not limited to the mean, median and standard deviation. Organize these numbers nicely in a table.
Using fertility as the predictor variable and life expectancy as the response variable, create a scatter diagram, come up with the least-squares regression line and calculate the linear correlation coefficient as well as the coefficient of determination. Make sure that you understand all interpretations and include them in your paper. Please carefully review the rubric below to see the full list of required interpretations.
Use the regression line to predict life expectancy for the United States given fertility and then compare this to the actual value in the United States.
Name some possible lurking variables that may be at work here.
Explain the difference between correlation and causation and why we cannot say that there is a cause and effect relationship in this situation.
Explain why we cannot use our regression model to predict the life expectancy of one particular individual.
Take a look at the website where this data was pulled from and comment on how the model might have been different if we used the data from 20, 40 or 60 years ago. Navigate to http://gapminder.org and click on “Gapminder World”. Use the x-axis and y-axis dropdown menus to ensure that ‘life expectancy (years)’ is selected on the y-axis and ‘children per woman (total fertility)’ is selected on the x-axis.
Put everything together into an organized paper and submit !!!
| Country | 2013 Fertility | 2013 Life Expectancy |
| Afghanistan | 4.9 | 56.2 |
| Albania | 1.771 | 75.8 |
| Algeria | 2.795 | 76.3 |
| Angola | 5.863 | 60.4 |
| Antigua and Barbuda | 2.089 | 75.2 |
| Argentina | 2.175 | 76 |
| Armenia | 1.74 | 73.8 |
| Aruba | 1.673 | 75.455 |
| Australia | 1.882 | 81.8 |
| Austria | 1.471 | 80.8 |
| Azerbaijan | 1.924 | 72.3 |
| Bahamas | 1.888 | 72.5 |
| Bahrain | 2.075 | 79 |
| Bangladesh | 2.177 | 69.5 |
| Barbados | 1.849 | 75.6 |
| Belarus | 1.494 | 70.2 |
| Belgium | 1.854 | 80.2 |
| Belize | 2.676 | 70 |
| Benin | 4.845 | 64.9 |
| Bhutan | 2.232 | 69.4 |
| Bolivia | 3.221 | 71.9 |
| Bosnia and Herzegovina | 1.283 | 77.5 |
| Botswana | 2.619 | 65.8 |
| Brazil | 1.801 | 75 |
| Brunei | 1.994 | 78.7 |
| Bulgaria | 1.541 | 74.5 |
| Burkina Faso | 5.605 | 62 |
| Burundi | 6.033 | 59.8 |
| Cambodia | 2.861 | 67.8 |
| Cameroon | 4.78 | 58.7 |
| Canada | 1.67 | 81.5 |
| Cape Verde | 2.292 | 74.2 |
| Chad | 6.263 | 57.1 |
| Channel Islands | 1.459 | 80.324 |
| Chile | 1.82 | 79.1 |
| China | 1.668 | 76.5 |
| Colombia | 2.286 | 75.6 |
| Comoros | 4.714 | 63.7 |
| Congo, Dem. Rep. | 5.933 | 57.5 |
| Congo, Rep. | 4.969 | 61.5 |
| Costa Rica | 1.795 | 79.8 |
| Cote d'Ivoire | 4.866 | 58.9 |
| Croatia | 1.501 | 77.8 |
| Cuba | 1.449 | 78.3 |
| Cyprus | 1.461 | 82.2 |
| Czech Rep. | 1.566 | 78.2 |
| Denmark | 1.88 | 79.9 |
| Djibouti | 3.387 | 63.4 |
| Dominican Rep. | 2.484 | 73.6 |
| Ecuador | 2.559 | 74.8 |
| Egypt | 2.77 | 70.9 |
| El Salvador | 2.184 | 73.9 |
| Equatorial Guinea | 4.845 | 58.8 |
| Eritrea | 4.696 | 62.1 |
| Estonia | 1.604 | 76.6 |
| Ethiopia | 4.519 | 62.6 |
| Fiji | 2.588 | 66.1 |
| Finland | 1.853 | 80.6 |
| France | 1.98 | 81.7 |
| French Guiana | 3.058 | 77.121 |
| French Polynesia | 2.058 | 76.257 |
| Gabon | 4.087 | 59.1 |
| Gambia | 5.751 | 64.3 |
| Georgia | 1.817 | 72.9 |
| Germany | 1.419 | 80.7 |
| Ghana | 3.857 | 64.9 |
| Greece | 1.529 | 79.8 |
| Greenland | 2.077 | 71.5 |
| Grenada | 2.17 | 71.5 |
| Guadeloupe | 2.08 | 80.947 |
| Guam | 2.405 | 78.854 |
| Guatemala | 3.783 | 72.3 |
| Guinea | 4.915 | 60.2 |
| Guyana | 2.546 | 64 |
| Haiti | 3.148 | 64.3 |
| Honduras | 3.001 | 72 |
| Hong Kong, China | 1.135 | 83.378 |
| Hungary | 1.411 | 75.8 |
| Iceland | 2.083 | 82.8 |
| India | 2.479 | 66.2 |
| Indonesia | 2.338 | 70.5 |
| Iran | 1.92 | 78.3 |
| Iraq | 4.026 | 71.3 |
| Ireland | 1.997 | 80.4 |
| Israel | 2.898 | 82.2 |
| Italy | 1.487 | 82.1 |
| Jamaica | 2.26 | 75.5 |
| Japan | 1.419 | 83.3 |
| Jordan | 3.244 | 78.1 |
| Kazakhstan | 2.455 | 67.8 |
| Kenya | 4.382 | 65.2 |
| Kiribati | 2.952 | 62 |
| Korea, Dem. Rep. | 1.988 | 71.2 |
| Korea, Rep. | 1.321 | 80.5 |
| Kuwait | 2.6 | 80.3 |
| Kyrgyzstan | 3.075 | 68.6 |
| Laos | 3.02 | 65.8 |
| Latvia | 1.607 | 75.3 |
| Lebanon | 1.495 | 78.3 |
| Liberia | 4.792 | 63.1 |
| Libya | 2.356 | 75.6 |
| Lithuania | 1.519 | 75 |
| Luxembourg | 1.671 | 81.1 |
| Macao, China | 1.083 | 80.4 |
| Macedonia, FYR | 1.431 | 76.6 |
| Madagascar | 4.468 | 64.3 |
| Malawi | 5.389 | 57.3 |
| Malaysia | 1.964 | 74.7 |
| Maldives | 2.256 | 79.3 |
| Mali | 6.847 | 57.2 |
| Malta | 1.356 | 82.1 |
| Martinique | 1.827 | 81.41 |
| Mauritania | 4.67 | 65.1 |
| Mauritius | 1.501 | 73.3 |
| Mayotte | 3.802 | 79.19 |
| Mexico | 2.185 | 75.5 |
| Micronesia, Fed. Sts. | 3.294 | 66.8 |
| Moldova | 1.456 | 71.9 |
| Mongolia | 2.436 | 64.7 |
| Montenegro | 1.666 | 75.6 |
| Morocco | 2.735 | 74.3 |
| Mozambique | 5.188 | 56.2 |
| Myanmar | 1.938 | 67.1 |
| Namibia | 3.051 | 60.6 |
| Nepal | 2.3 | 70.6 |
| Netherlands | 1.774 | 80.6 |
| Netherlands Antilles | 1.89 | 76.894 |
| New Caledonia | 2.127 | 76.306 |
| New Zealand | 2.052 | 80.6 |
| Nicaragua | 2.498 | 76.4 |
| Niger | 7.561 | 61.6 |
| Nigeria | 5.976 | 60.1 |
| Norway | 1.931 | 81.4 |
| Oman | 2.853 | 75.5 |
| Pakistan | 3.185 | 65.7 |
| Panama | 2.466 | 77.8 |
| Papua New Guinea | 3.781 | 59.8 |
| Paraguay | 2.864 | 73.7 |
| Peru | 2.417 | 77.1 |
| Philippines | 3.043 | 70 |
| Poland | 1.417 | 76.9 |
| Portugal | 1.315 | 79.8 |
| Puerto Rico | 1.636 | 78.864 |
| Qatar | 2.019 | 81.8 |
| Reunion | 2.232 | 79.646 |
| Romania | 1.417 | 76 |
| Russia | 1.595 | 71.3 |
| Rwanda | 4.508 | 65.3 |
| Saint Lucia | 1.912 | 74.5 |
| Saint Vincent and the Grenadines | 1.997 | 72.7 |
| Samoa | 4.147 | 71.8 |
| Sao Tome and Principe | 4.075 | 68.4 |
| Saudi Arabia | 2.644 | 77.9 |
| Senegal | 4.934 | 65.7 |
| Serbia | 1.365 | 77.7 |
| Seychelles | 2.18 | 73.3 |
| Sierra Leone | 4.705 | 57.7 |
| Singapore | 1.282 | 81.9 |
| Slovak Republic | 1.396 | 76.2 |
| Slovenia | 1.509 | 80 |
| Solomon Islands | 4.031 | 63.7 |
| Somalia | 6.563 | 57.7 |
| South Africa | 2.387 | 60.4 |
| South Sudan | 4.92 | 57.2 |
| Spain | 1.505 | 81.7 |
| Sri Lanka | 2.339 | 76.1 |
| Sudan | 4.42 | 68.9 |
| Suriname | 2.268 | 70.1 |
| Sweden | 1.928 | 81.8 |
| Switzerland | 1.533 | 82.7 |
| Syria | 2.964 | 72.4 |
| Taiwan | 1.065 | 79.3 |
| Tajikistan | 3.815 | 70.6 |
| Tanzania | 5.214 | 62.2 |
| Thailand | 1.399 | 74.9 |
| Timor-Leste | 5.855 | 71.4 |
| Togo | 4.639 | 63 |
| Tonga | 3.767 | 70.3 |
| Trinidad and Tobago | 1.797 | 71.2 |
| Tunisia | 2.008 | 77.1 |
| Turkey | 2.041 | 76.3 |
| Turkmenistan | 2.326 | 67.5 |
| Uganda | 5.867 | 59.8 |
| Ukraine | 1.47 | 71.7 |
| United Arab Emirates | 1.801 | 76.4 |
| United Kingdom | 1.892 | 81 |
| United States | 1.976 | 78.9 |
| Uruguay | 2.046 | 76.9 |
| Uzbekistan | 2.309 | 69.7 |
| Vanuatu | 3.382 | 64.6 |
| Venezuela | 2.39 | 75.4 |
| Vietnam | 1.743 | 76.3 |
| Virgin Islands (U.S.) | 2.487 | 80.152 |
| West Bank and Gaza | 4.01 | 74.6 |
| Western Sahara | 2.363 | 67.764 |
| Yemen, Rep. | 4.075 | 67 |
| Zambia | 5.687 | 56.7 |
| Zimbabwe | 3.486 | 56 |
In: Statistics and Probability