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Errion Holness

Statistics

Research Paper

Ms. I

Does the life expectancy of a Chilean man depend on how many children his wife may have?

As tragedy ends, men live longer. Tragic events in a country will lower the amount of children. When people begin to  discriminate less, fight less, and unify together, they live longer. I looked at and researched 1 country using the internet and a Gapminder graph. But, does this all tie in together. I’m going to explain some vital points in each data point.

I’m focusing on the South American country of Chile. The color green represents the South American continent. The graph is on a linear scale with the y axis as the life expectancy of a male and the x axis is the amount of babies per woman. The x decreases as the y increases. There is a negative correlation while the graph is on a linear scale. The Gapminder data is from the United Nations and the World Population Prospects. The bubbles are based on the population size. As the graph moves, it begins with a sharp left turn after 10 years and proceed up the Y - axis. At this time, the amount of children women are having is decreasing over time.

Chile was trying to transition into a democracy. In the late 80s through early 90s, they were trying desperately to become a united country. According to Freedomhouse.org, the people peacefully held protests, created unions and political parties. This led to a steady increase in Chilean male lives. Although, women were having less children due to many factors such as them not being respected as much as men or a growing obesity rate.

An article by the World Heart Federation (WHF) claimed that, “In Chile, a survey showed that obesity rose from 6 % to 11% in men and from 14% to 24% in women in just 4 years.” It also went on to connect obesity in Latin American women leading to dangerous cardiovascular diseases along with other risk factors of smoking and hypertension. The WHF also pointed out cardiovascular diseases as the leading cause of death in Latin America. Therefore, women die after being subject to these dangers. Although, the article also pointed out and said, “As we have mentioned above, this increase in obesity levels affect women more than men. Indeed, they tend to sacrifice their health for that of their family, surviving on unhealthy foods while trying to feed their families with better ones.”

According to some 2012-13 Gallup polls, 32% of Chilean women are likely to say they’re treated with respect. 60% of Chilean women answered no when asked if “they believe that women in their country were treated with respect and dignity” 61% of latin americans overall claimed that women aren’t treated with respect and dignity.  This may have been a contributing factor to Chilean female obesity because an article by Drexel University hinted at signs of a Compulsive Eating disorder. It said that “They frequently suffer from low self-esteem and depression, hoping food will fill the void, or at least help them escape from feelings of stress, anxiety and anger. Compulsive Eating involves addictive behavior with food.”

In conclusion, I don’t think the life expectancy of a man relates to the amount of children his wife may have. I Although, countries will have their major events, it affects the population as a whole and nothing partially. It’s very difficult to separate the country's event problems from the whole population into gender specific categories. My question was sort of broad because of the extensive data I’d need in regards to fertility rates, male careers, and more.

Video Link: https://www.wevideo.com/view/843714453

https://freedomhouse.org/sites//files/inline_images/Chile.pdf

http://drexel.edu/counselingandhealth/resources/students/body-image/

http://www.imf.org/external/pubs/ft/fandd/2000/03/aninat.htm

www.world-heart-federation.org/fileadmin/user_upload/documents/Advocacy/Resources/Region_and_country-resources/Obesity%20and%20children%20obesity%20in%20Latin%20America.doc

http://www.gallup.com/poll/178427/respect-dignity-women-lacking-latin-america.aspx

Maliya Edwards

1/16/17

Ms.I

Benchmark

You ever wonder what could affect the amount of money you make in a year? You ever wonder why you make the amount of money you do and what's stopping you from making more? There are many answers and variables that affect your annual money a year. Today we are focusing on the amount of children and the amount of people in a family effect has on the amount of money made each year. All which support the idea of the less amount of family members and children can lead you to make more money.

In the early 1800s, Ghana were having nearly 7 kids per woman. The family size was an average of 9. In the 1800s Ghana average 696 a year. Now back in the 1800s there weren't a lot of jobs and there wasn't a lot of good paying jobs. Seen below As we get further into the future, in the 1900s Ghana average family size was 8 and the average amount of children were 6. At this time the average amount of money made a year was 725. During the 1800s Ghana were going through diseases. It lowered the amount of the population. It may not show in the average size family but a lot of people died due to diseases. As we get into the 2000s, Ghana average family size went down to 6 per family and roughly about 4 to 5 children a family. At this time people in Ghana were making up to 2,270 a year. Seen belowAs better jobs and vaccines came out for these diseases. As we go into the year 2015, the average family size went down to 5 and the average children per woman went down to 3 roughly 4 kids and average an amount of 4,100 a year. From the 1800s Ghana family size was 9 and average children in the family were 7, now in 2015 the average family size is 5 and children within the family is 3. Seen below

The next country we are looking at is Keyna. In the 1800s Kenya family size was 9 and average children to a family was 7. Again around this time there wasn't a lot of jobs anywhere let alone jobs that paid a good amount of money. People in kenya in the 1800s made roughly 854 a year. Seen below Compared to ghana the family and children size were about the same, although Kenya made more money a year than Ghana. As we get closer to the 1900s Kenya was another country to deal with diseases and deaths throughout the years. In 1900s Kenya family size was still roughly around 9 and children per family was still hovering around 7. The money per year did go up to 1,170 a year. At this time people in Kenya were all at work. Kids started work a very early age to support their families. As deaths started to go up, more education and better jobs were being built. In the 2000s Kenya family size was down to 7 and babies per woman down to 5. The average salary a year has also gone up to 2,140 as seen below Kenya economy started to get better and were offering better paying jobs to the people. Seen below In 2015 Kenya family size went down to 6 per family and 4 children per woman. They were also making 2,900 a year as seen below As the year went on Kenya people had less children and were making more money.

As we move along we take a look at the United States. In the 1800s there was 7 children to a family and average family size was 9. United States was making 2,130 a year. Seen below As we get to the 1900s, we know United States was going through slavery and wars. This made the country lose a lot of people to death and there wasn't many jobs. Most jobs were war or being a slave. In the 1900s the United States the family size plumbed down to the size of 5 to a family and roughly 4 kids to a woman. United states also made 6,820 a year. That was a big jump from 1800 to 1900. As we get closer to the 2000s, United States have gotten their freedom, have rebuild and structure the country and got better jobs. The family size went down again to 4 a family and 2 children to a woman in 2000s. They also made roughly 46,000 a year. Seen below In 2015 the family size and children to a woman and still the same but with the country advancing in technology it made nearly 53,400 a year. Seen below United States has and still known for the richest country in the world.

The last country we are going to look at is China. To start off China made 985 a year with a family size of 7 and children size a 5 in 1800s. Seen below In the 1900s diseases also spread through China wiped out most of its country. China family and children size were still the same trying to refill its population again. They made 894 a year, less than what they made in the 1800s. In 2000s China made roughly 3,680 a year. The children to a woa went down to 2 and family size to 4. The family size dropped very big because China passed a law saying women couldn't have more than 2 babies. Seen below As we get to 2015 the average family size was 3 and the average kid to a woman was a 1 and they also made up to 13,400 a year seen below.

While doing this paper I realize the more kids people had the less time they spent working which mean the less money they made a year. With children you have to spend so much time taking care of them while they are young which meant you couldn't always go to work. In a lot of cases, people in these countries lost their jobs when they had a big family because they miss work too much. All in all, i do believe the less children you have the more money you will make.

The Sources I used was from the YouTube video from class https://youtu.be/N-x7eHuUhNM and  https://www.google.com/amp/nypost.com/2016/01/03/how-chinas-pregnancy-police-brutally-enforced-the-one-child-policy/amp/

Suicide is a big epidemic that our country and other developing countries around the world are experiencing. Suicide affects a lot of other factors that are looked at pertaining to the citizens of these countries and their health. Suicide of course is taking your own life but it is considered a disease. You would think this disease would affect life expectancy more. It happens a lot and there is medication to help but this decision is still in everyone’s own hands so you may not be able to prevents this that often. When more people commit suicide in a certain place, the life expectancy rate will go down in that place. Although you would expect suicide would affect life expectancy more drastically,  I have learned from further research and graphs that it doesn't affect it as much as expected.

From further research I have learned that the suicide rate doesn’t affect life expectancy as drastically as I expected. The graphs that I found based on this information show that suicide affects life expectancy but not as much as expected in most countries. Three countries that I focused on for this information is Russia, India, and the U.S. One of the graphs that I found that focused on this data was focused on these three countries. On these graphs it shows how the rate of suicide per 100,000 people and the life expectancy rate changes over time in these countries. For the U.S. the suicide rate and life expectancy moves back and forth but it doesn’t change much over time. The suicide rate is low and the life expectancy rate is high as expected. For India the life expectancy rate and suicide rate barely changes over the years. The suicide rate in India is high than the U.S. and the life expectancy is lower but compared to each other it still is as much as expected. For Russia, both the suicide rate and life expectancy rates fluctuates but not how you would expect it to. The life expectancy rate of Russia moves but not much compared to the suicide rate over time. On the graph the suicide rate rises and lowers between 20-40 suicides per 100,000 people but the life expectancy rate stays between 60-70. You would think that the life expectancy rate and suicide rate may move and affect each other together but the graph shows otherwise.

The other graph that I found compared the life expectancy rate and suicide rate per 100,00 people but not over time. This graph shows all of the countries from 2005 that have data to be presented for these categories. You can’t see the change over the time but you do see that some countries have high suicide rates and still have high life expectancy rates. Also some countries have a low suicide rate and low life expectancy rate. This is kind of the opposite of how I expected the data would be found. Because there are countries whose data shows this pattern you would expect there to be something else in these other countries affecting the life expectancy more than the suicide rates.

One of the reasons that could be affecting the life expectancy in these countries is the age in which people are committing suicide. In some countries that have a high suicide rate and life expectancy the people who are committing suicide there are more than likely to be committing suicide at an older age. The age which people kill themselves affects the life expectancy rate more than expected. If people are living longer in a certain country but as they get older they start to commit suicide the life expectancy rate will still be high because they are living to an older age. In the countries that have a low suicide rate and low life expectancy rate there could be other things in the country that just isn't safe for the citizens. They could be dying from war, because there are other major diseases in that area, etc. The suicide rate of that country will still affect the life expectancy but there are other factors that affect it more.

As far as the suicide rates and life expectancy rates of countries I don’t think anything will change. I feel that they will still affect each other how they already have and there isn’t much we can do about that, Another thing we could take into considerations is that this data is almost 12 years old, so there may be some changes that could have happened but they have not been documented yet.

How does long term unemployment affect how long the life expectancy of me in that country? I will be making scatter plot graph to help support on my question. Long term  unemployment is the explanatory variable. This question is worth researching because it tell the percentage of men who live longer or shorter due to unemployment. Finding out whether or not long term unemployment of a man can affect their health and sometime cause them to live a shorter life.

Starting off my research I want to give the overall long term unemployment rate and the life expectancy rate of both women and men together and separately. The countries that I picked for my research were the United States, France and Japan. Not really having a particular reason for why I choose these countries to find data on but it will be interesting seeing other countries economy problems. Wanting to compare data from a overall view on long term unemployment to see if, unemployment affect the life expectancy both men and women together or just men. Below is three graphs that will show long term unemployment vs. life expectancy for men and women and both men and women together.             

When looking at these graphs, I notice that with women unemployment for them is low in the United States and Japan and for those who are unemployed still life longer than the average long term unemployed for a male.

When thinking about a topic to research, I thought about something that I think that mainly affected the United States. This topic was Male Life expectancy based on Male long term unemployment rate. So what is the average age a man lives too based on their long term unemployment. What I found when researching is that men with long term unemployment tend to live to about 70 to 75 years of age.  When the long term unemployment rate of goes up (which is the y axis), the life expectancy of a male (which is the x axis) will be shorter. These fact are based off the three countries I picked United States, Japan, and France. According to Business Insider.com, “it's a fact that unemployment can lead to a loss of a sense of purpose and disconnection from the world — and other research shows that social isolation, and loss of purpose, has real health implications.”

Graph of Male Long Term Unemployment and the Male Life Expectancy from not having a job for a long time.

The x axis is represented by male life expectancy and y axis represented with long term male unemployment.The dots on the graph stands for three different countries United States (Green), Japan (Red), France (Yellow). The most important observation on my graph is that most long term unemployed men in these countries live a life to about 70 to 75 years of age. Something else that I noticed is that countries like the United States and France every time the long term unemployment goes up, the life expectancy of a man life is lowered from the average 70- 78 just to 70 years of age. Another observation that I noticed is that the bigger the dot the larger the population of men on long term unemployment and the country life expectancy of a male. The smaller size dot are has less of the male population that are being affected by long term male unemployment and life expectancy.

Thing that I noticed was that the dot are are going more in a positive correlation which in life expectancy is a good thing but not as good for the unemployment.  Also that in the years of 1983 in the United States, France 1998 and, Japan 2003 in these years male unemployment has risen up which cause the life expectancy of a male to be lower than average. This observation leaves me as questions like, “What was the cause for the unemployment to rise in United States in the year 1980?” According to InvestmentWatch.com, it shows and give the accuracy of male unemployment rate and the reasons for the drop in unemployment in the 1980’s. According to this website

The early 1980s recession was a severe recession in the United States which began in July 1981 and ended in November 1982
Unemployment had risen from 5.1% in January 1974 to a high of 9.0% in May 1975.  Although it had gradually declined to 5.6% by May 1979, unemployment began rising again thereafter. It jumped sharply to 6.9% in April 1980 and to 7.5% in May 1980. Twelve million people were unemployed, , an increase of 4.2 million people since July 1981.  Unemployment rates for every major group reached post-war highs, with men age 20 and over particularly hard hit.

“What was the cause for the unemployment to rise in France in the year 1998?” is another questions that I asked based on the data on the graph. According to tradingeconomics.com, it confirms that the unemployment was drastically high in france 1997 and 1998. Following a chart that show the high rate and unemployment.

It confirms that the unemployment was drastically high in france 1997 and 1998. Unemployment rate in France averaged 9.26 percent from 1996 until 2016
reaching an all time high of 10.70 percent in the first quarter of 1997 and a record low of 7.20 percent in the first quarter of 2008.”

In Japan we would also like to know “What was the causes for the unemployment to rise in Japan in the year 2003?”. Based on keidanren.or.jp, it gives information on what going on in Japan in 2003.

According to Tokyo Big Sight,

For many years after the end of the war, Japan's industrial structure was geared toward the task of catching up with the advanced countries of the West. According to nber.org

“In the second half of the 1990s, the job insecurities of middle-aged and older workers have received much attention. The Japanese government, media, labor unions, and even employers often admit that corporate restructuring measures have resulted in massive job loss among older, especially white-collar, workers.” Also that “middle aged
and older workers, who mainly lost their jobs through bankruptcy,
dismissal, or mandatory retirement.”

Another statistical evidence graphs that I will you reference in this paragraph along with some in text citation from whitehouse.gov.

“As Figure 1 shows, participation among prime-age men peaked in 1954, declined only slightly until the mid-1960s, but then began to decline in earnest in the decade between 1965 and 1975, when the share in the labor force fell from 96.7 percent to 94.2 percent. Since then, participation has fallen persistently, with sharper declines in recessionary periods, such as the early 1990s, that were not fully reversed in the subsequent expansion periods.”

When doing some research I discovered whitehouse.gov, which talked about the United States “during the Great Recession, the rate of labor force participation among prime-age men fell steeply, falling from 91.5 percent in January 2007 to 87.9 percent at its trough in October 2013”
“Not only has labor force participation among prime-age men declined over the past six decades, since 1990, the United States has had the second-largest decrease in prime-age male participation among member countries of the Organisation for Economic Co-operation and Development (OECD). Of the OECD, the United States now ranks 3rd lowest out of 34, as shown in Figure 3—above only Italy and Israel—in terms of prime-age male labor force participation, compared to 10th lowest out of 24 in 1990.”

This supports my thesis statement because it prove that men that have not been employed in a long time, or that been look for a job for a long time, can have an affect on a man or persons health. Unemployment can sometimes leave to serious health conditions or even death.

• "U.S double digits unemployment rate of 1980-1981 vs. 2008-2009 single digit." InvestmentWatch. IWB, 06 Sept. 2012. Web. 31 Dec. 2016.

• "France Unemployment Rate | 1996-2017 | Data | Chart | Calendar | Forecast."

• ""Challenges and Perspectives of the Japanese Economy and Industry"." "Challenges and Perspectives of the Japanese Economy and Industry" (2003-06-02). N.p., n.d. Web. 20 Dec. 2016.

• Genda, Yuji. ": Labor Markets and Firm Benefit Policies in Japan and the United States." N.p., Jan. 2003. Web. 20 Dec. 2016.

We are Peachy Designs and we work with many people all over to help them be more green in some shape or form. Our most recent client Ms. Fanning located on the second floor next to Mr.Johnson’s office is our wonderful Guidance Counselor and fabulous creative writing teacher. Sh told us that she wanted her room to be more green so we decided to create a garden box and place it in her room.Ms. Fanning will give the plants what it needs to thrive such as water and general care and since her room has a lot of sunlight that will come naturally. Our green design relates to almost all of our previous assignment which teaches us the inner workings of the plant therefore teaching us how to take care of the plant better and create a stable place for the plant to survive. Overall I would say the as a group we did good on the project however and think that since we were so  exciting in creating we did not plan ahead for possible problems of the garden box such as it being knocked over. We do believe that we satisfied our customer and look forward to working with her sometime soon.

Sincerely,

Peachy Designs Cash’e Chapman, Tyrone Grant, & Ali Omer  

Jonae Johnson

Ms. I

Statistics

15 January 2017

How Does the Age of First Marriages Affect the Total Number of New Births ?

As of today the population size for Japan is 127.3 million people, and Sri Lanka’s population is 20.48 million. Sri Lanka is a lot smaller than Japan is, but they both are pretty small compared to other countries. A lot of the families in Japan live in a middle class life style. Most families in Sri Lanka were lower class. Throughout the years as the age of first marriage increased the numbers of new birth stayed the same in Japan and in Sri Lanka.

Over the years in the population for Japan has increased at a steady rate. There life expectancy rate in the year of 2012 is 83 years old. In japan women first marriage was at the age 30. The average family would have up to at least two kids. This all is important because without this information we would have reason for why the new birth rate is where it is. The new birth rate at the age of first marriage has been steady over the past century.

Sri Lanka is a pretty small country compared to a lot of other countries. There life expectancy rate is around 75 years old. Sri Lanka average age for first marriage is between the age of 18 and 25. The average family has about three kids per household. This important to know because it will help understand why Sri Lanka has a steady rate for new birth rate.

These two countries are similar in some ways, both countries are about 4,000 miles from each other. Both countries use to have assigned marriages, where they would set their children up to be married.

The two countries have more difference than similarities. Sri Lanka is smaller than Japan. They always married their kids at young age. Most of the families in Sri Lanka are lower class compared to Japan where a lot of the families are middle class.

The x axis is represented by the age of 1st marriage, women. The y axis is represented by the new births in that country. In the second graph I focused more on Japan and Sri Lanka. This is a no correlation. The size of the bubbles represent the population size. The color represent the different countries. Looking at the graph the countries over time are staying constant in the numbers of new births. The countries mostly steady throughout the years. Sri Lanka had a jump in 1800 when the birth rates increased. Japan age at first marriage jumped back a couple of years around the year 1800. The age of first marriage began to get older and older. The birth rate however continue to stay relatively normal. It slight decreased in Japan. In Sri Lanka in the year of 1883 the age of first marriage increased to 17 years old, the birth rate increased also. I found that when people got older and got married they began to have more kids. In Japan the older they got the less kids they decided to have.

This is the population of Japan over the past 10 years. The population has not been at a steady rate. Around 2009 the population decreased a little. From 2012 until now the population continued to decrease, and shows not effort to increase.

Income Per Person and Primary School Completion among Countries around the World

In America, education is a right that everyone is given. This is not what it is like around the world. Children around the world are not getting their birthright of a proper primary education. It has become depressing that some children are not being able to complete their primary education because of financial need. All around the world, there is a positive correlation that shows when the income per person is higher in a country the country has a higher percent of children completing primary school. Education is a human right that everybody should receive easily. Countries with lower average income per person have lower primary school completion rates because the countries are not funding the schools and parents cannot afford the fees.

The first thing that we must look at is what causes students to drop out. Students around the world drop out for many reasons, including having to work and help out with their family, they might be very ill, or most commonly their family cannot afford schooling. Taken from the United Nations Educational, Scientific, and Cultural Organization report of Education For All Global Monitoring Report, there is a paper titled School Drop out: Patterns, Causes, Changes, and Policies, “There are many factors associated with dropout, some of which belong to the individual, such as poor health or malnutrition and motivation. Others emerge from children’s household situations such as child labour and poverty.” This quote means that sometimes children drop out of school because they have to work to afford to live and get their family out of poverty or it could also be because they are too sick to continue going to school. In this graph from gapminder who collects data from various sources, we can see that there is a problem particularly in Africa, which are the blue dots. This data is collected from the World Bank and other various sources.      

This graph shows income per person, GDP per country and Primary School Completion for the same country. Income Per Person is the explanatory variable in this instance and primary school education is the response variable. This is because primary school completion is dependent on income per person. The size of the bubble represents the population of the country. Green represents the Americas, Red represents Asia and Australia, Blue is Africa, Yellow is Europe. This graph shows a small correlation between the income per person and the primary school completion, but it is evident what is happening all around the world. You can see the trend, where countries that have less income per person means there is less primary school education. Overall, there is a pattern but if you look at the trend within specific countries there is not a trend. This can be backed up with my reasoning that students drop out of school to work and make money for their family.

All around the world, there are barriers to education. The biggest barriers would be a lack of funding of education, not having a classroom and/or lack of learning materials, hunger, and the expense of education (formal and informal fees). From the Global Citizen, an article titled 10 barriers to education around the world there is a quote, “In many countries in Africa, while education is theoretically free, in practice ‘informal fees’ see parents forced to pay for ‘compulsory items’ like uniforms, books, pens, extra lessons, exam fees or funds to support the school buildings.” This quote is specifically mentioning Africa and the fact that education is free but there are hidden costs that hit the family hard. These fees are necessary and commonly send families into a cycle among generations of poverty because of them. This happens because if a family is spending an overwhelming amount of money in hidden fees for school they will not be able to crawl out of poverty. This means that their children are going to have to live in poverty and then have children and having to pay their hidden school fees. There is just an overall lack of money going towards a proper education for the children in these countries.

As we follow few countries on the graph we begin to see how each individual country follows the standard of more money more school completion.

For example, Bermuda, the green trail (which means that it is in the Americas) to the right of the graph. You can see the as the income per person rises so does the primary school completion, as well as Columbia. While there are some countries that do not follow this trend and this is because of other things happening within the country, like war. In Qatar, if you follow the trend you see that as income per person goes down primary school completion goes down as well.

Financing education is very different to for some families around the world. While education is free in many countries around the world, families still cannot afford to send their children because they live in extreme poverty. From an article published by Our World In Data titled Financing Education, there is a quote “The second half of the 20th century marked the beginning of education expansion as a global phenomenon. Available data shows that by 1990 government spending on education as a share of national income in many developing countries was already close to the average observed in developed countries.” This quote means that during the 20th-century education became expanded to underdeveloped countries, like those in Africa. These underdeveloped countries education systems are now similar to developed countries. Although, there are still children unable to complete their education.

To answer my research question, How does income per person affect primary school completion in countries around the world? I graphed income per person as the explanatory variable, and primary completion as the response variable. The explanatory variable is on the x-axis which is on a logarithmic scale and the response variable on the y-axis which is on a linear scale. The data shown in my graph was collected by the World Bank and put together by Gapminder. The size of the bubbles represents the population of each country. Looking at each country separately you could say that some have positive correlations while others have no correlations, there are sharp increases and decreases in each of the countries. When you look at my graph you can see that there really is no pattern as to where they start, they all start at the amount that a person is making per year. You can see that Albania and Hong Kong, China follow the same kind of pattern which is no correlation between the two variables. You can also see that Columbia and Bermuda follow the same kind of pattern which is a positive correlation between the two variables. A positive correlation on this graph would mean that when the income per person rises, the primary school completion rises with it. You can conclude that countries that make more money have more students finish primary school.

In conclusion, It is more likely for a child who lives in poverty to not complete primary school compared to a child that does not. This is because of the overall lack of money that parents can afford for formal and informal fees, the fact that some children have to quit school and work to provide for their family, or that some schools do not have enough funding to create a good enough learning environment. This is important to learn about and do more research because there are a lot of things we can do to help the children in need of their human right to a proper education. We can donate money, or even travel to these places to help the children learn and grow. We can see that governments in countries all around the world are starting to give more money to education but it is still not enough for some people.

Works Cited

"10 barriers to education around the world." Global Citizen. Write To Learn, 2 June 2014. Web. 10

Jan. 2017.

Roser, Max, and Esteban Ortiz-Ospina. "Financing Education." Our World In Data. N.p., n.d. Web.

10 Jan. 2017.

School Drop out: Patterns, Causes, Changes and Policies . Tech. N.p.: United Nations Educational,

Scientific, and Cultural Organization, 2011. Print.

Shah Follow, Jamal. "Causes and effects of dropouts at primary level." Share and Discover

Knowledge on LinkedIn SlideShare. N.p., 13 Feb. 2014. Web. 10 Jan. 2017.

The food supply affects life expectancy is proper nutrition. Eating healthy is the most effective and sometimes expensive way to decrease the fear of diseases ending your life or causing major complications. According to Audre Biciunaite, “.The most obvious explanation behind the connection between life expectancy and income is the effect of food supply on mortality. Historically, there have been statistically convincing parallels between prices of food and mortality.”

Overall, the life expectancy of my countries are in a positive correlation. Haiti's correlation is all over the place because climate change and earthquakes. Japan and Germany goes in a positive correlation because they both have equal amounts of food supply and decrease and increasing life expectancy. This is because in places like Haiti, food is scarce because they have a lot of natural disasters that makes the food supply decrease and as a result, life expectancy drops. In the other countries, Natural disasters are less likely to occur so, food supply is leveled and so their life expectancy.

In the graph above, there are two images of graphs from gapminder. As you can see Haiti’s data is in a S shape. In the data, you can see that Haiti has had some decrease in food supply and life expectancy. The cause of that is because of the climate changes and natural disasters occurring. When looking at Japan’s data, overtime life expectancy and the food supply is increasing. Then life expectancy starts to increase more than food supply starts to decrease. The cause of this is because people traveled to the countryside to get their food and in the early stages war was reasonable. When looking Germany’s data, the life expectancy and food supply is increasing. These data points show the increasing and decreasing of food supply and life expectancy throughout the years.

Living in a country like Haiti is difficult due to the common natural disasters including severe storms, flooding, landslides, drought and the devastating earthquake that rocked the country on 12 January 2010 that have been occurring for the past two decades. A lot of people who live in Haiti, such as some relatives of mine, do not have access to water, electricity, health benefits or somewhere that is clean/sanitized. The cause of food supply decreasing is because of the storms. A lot of Haitians grow everything they eat and go fishing. When huge storms hit them, the food supply decreases drastically. “According to the latest WHO data published in 2015 life expectancy in Haiti is: Male 61.5, female 65.5 and total life expectancy is 63.5 which gives Haiti a World Life Expectancy ranking of 144. You can see the top 20 causes of death data and rankings for Haiti by clicking on the links below or select the full country health profile at the bottom of the page.”

In a country like Japan, food is their culture. They have a wide variety of food. Being as though their food plays a major role in their country, “uniquely reflects the natural environment, regional diversity and underlying value system of this resilient country, says Brendan FD Barrett”. Their food supply are always increasing in numbers because everything they eat are grown from the ground by them. Then the food supply started to decrease a bit because people stopped farming and they started to import goods from other countries which increased in Japan. The largest amount of imported food comes from the United States followed by China, of course. According to nbc news by Geoffrey Cain, a globalpost correspondent, “The Japanese have the highest life expectancy of any major country. Women on average live to 87 and men to 80 (compared to 81 years for American women and 76 for American men). The Japanese can live 75 of those years disability free and fully healthy, according to the World Health Organization.”

In germany, the living is fair… not too bad. According to Better Life Index, “Germany performs well in many measures of well-being relative to most other countries in the Better Life Index. Germany ranks above the average in education and skills, work-life balance, jobs and earnings, environmental quality, social connections, housing, personal security and subjective well-being.” When looking at the graph, germany has had a steady increase of food supply. “According to the latest WHO data published in 2015 life expectancy in Germany is: Male 78.7, female 83.4 and total life expectancy is 81.0 which gives Germany a World Life Expectancy ranking of 24. You can see the top 20 causes of death data and rankings for Germany by clicking on the links below or select the full country health profile at the bottom of the page”.

These countries have different data. Some of the life expectancy depends on the food supply whereas the food supply depend on the life expectancy. In haiti, life expectancy has been decreasing over the past two decades due to natural disasters that made a negative impact on the food supply. The citizens of Haiti depend on their crops to feed their families and survive. In Japan, food supply is increasing and so is Life expectancy. In japan, they have a strict diet. Mostly growing their food, but then they started to import specific foods such as soy and wheat. One thing that I found interesting is that Japan has the highest life expectancy in the world because the diet they stick to is healthy. As they get older, they start to eat more vegetables. A combination of small portions, lower-calorie foods like fish and vegetables, and beautiful eye-appealing dishes all contribute to a longer and healthier lifespan, argues Naomi Moriyama in her co-authored book "Japanese Women Don't Get Old or Fat: Secrets of My Mother's Tokyo Kitchen." In Germany, the life expectancy and food supply are increasing in a steady rate. As far as making a living, Germany has been living well in debt free. They rank the highest in education and skills which is a plus to making a living.

Life expectancy of my countries are in a positive correlation. Haiti's correlation is all over the place because climate change and earthquakes. overtime life expectancy and the food supply is increasing. Then life expectancy starts to increase more than food supply starts to decrease. The cause of this is because people traveled to the countryside to get their food and in the early stages war was reasonable. When looking Germany’s data, the life expectancy and food supply is increasing. These data points show the increasing and decreasing of food supply and life expectancy throughout the years.

                                   Reference Page :

"Life Expectancy in Germany." World Life Expectancy. N.p., n.d. Web. 24 Jan. 2017.

"Germany." OECD Better Life Index. N.p., n.d. Web. 24 Jan. 2017.

OurWorld20. "Future of Food in Japan." Our World. N.p., n.d. Web. 24 Jan. 2017.

Correspondent, Geoffrey Cain GlobalPost. NBCNews.com. NBCUniversal News Group,    17 June 2014. Web. 24 Jan. 2017.

Lucia Idriss

01/10/17

Mrs. I

Statistics

There is no correlation between HIV and life expectancy in females within the countries of Sierra Leone and Italy.HUMAN IMMUNODEFICIENCY VIRUS ( HIV) "Unlike some other viruses, the human body can’t get rid of HIV completely. So once you have HIV, you have it for life. HIV attacks the body’s immune system, specifically the CD4 cells (T cells), which help the immune system fight off infections. If left untreated, HIV reduces the number of CD4 cells (T cells) in the body, making the person more likely to get infections or infection-related cancers. Over time, HIV can destroy so many of these cells that the body can’t fight off infections and disease. HIV is an easily contracted disease that can be transmitted through breast milk, sexual activities, and also blood.Countries such as Sierra Leone are underdeveloped and do not have access to all the resources needed to sustain incurable diseases such as HIV, whereas Italy has that kind of access because they are a more developed country.  I wanted to learn more about those whom have been infected and why they’ve been infected. To truly determine what the data points on the graph represent, from the least to the greatest. So now I will test whether your location and income affect the life expectancy in women with HIV.

When I first began I asked myself questions such as : How many of these women were sexually active before marriage and where these women sexually abused to contract HIV? So I took those questions to the web to find out that, yes sexual abuse was used as a form for some of these woman to contract HIV, especially in Sierra Leone during the war time. As I searched around even further I came an article ( lifeinitaly.com ) says “ In the past sex was viewed as a taboo subject. In Italy, like so many other European countries, this was perhaps mostly due to the powerful influence of the Catholic Church on Italian society. Sex was not a topic discussed by people in public, nor did it have a presence in the media.” and “ The average age for the first sexual experience is currently 17 in Italy. This is a difference of up to four years compared to the past, in particular the 30's-40's, when women generally had sex for the first time when they were, on average, 21-22 years old. Not only has the age for first experience changed but so has the context.” So could this also be a reason as to why HIV has such a high testing rate in Italy? Yes. And that evidence can be found on the gapminder graph that I have created.

The graph on gapminder tells a story of the women in Italy and Sierra Leone living with the HIV disease. In the graph you can clearly see that there is no positive nor negative correlation between the two countries. But you can also see that Italy has a larger bubble and also a longer life expectancy than women in Sierra Leone. The bubble represents the amount of women infected by the HIV disease, but how is it that they are living longer than the women who actually have  a lower HIV infection? Poverty, many women in Sierra Leone live in poverty, they are not expected to live as long because treatment in sometimes non affordable so they wind up passing because they can no longer take care of themselves. But as for the women in Italy they are more financially equipped so they can then actually take care of themselves to live much longer. The graph’s story has been told as to what the bubbles and correlations represent.

Now I also asked myself “ does living circumstances affect treatment availability? “ and my answer to that is yes. Some people who have been diagnosed with HIV cannot afford treatment because they are too poor, but based off of the graph that seems to have not affected the life expectancy for those women whom have been diagnosed. But in recent affairs I have noticed that the graph in 1990 Italy’s life expectancy increased, and there must have been a more affordable access to the treatment. This same increase also happened in Sierra leone in 2000 and remained at that same rate in 2006 going forward. So I believe the same affect that happened in Italy also Happened in Sierra Leone.  

In conclusion, from my research I can say that HIV does affect life expectancy in Females, and I can conclude that your financial statement also plays a part in whether or not you will receive treatment for the HIV disease. But I can still remain to say that there is no correlation on the graph determining life expectancy and HIV in Sierra Leone and Italy.