Wednesday, March 10, 2010

Gapminder Turn-in Thread


Turn in your Gapminder assignment in this thread. There are 4 steps:

1. You need to do the assignment. Instructions:
2. Write up your answers to the task in a word document.
3. Copy and paste the text from your answer to the assignment into your comment.
4. Upload your image online somewhere. I used IMGUR for this example.
5. Include the URL to your image in your comment, like this:
6. Remember to bring a hard copy of your answer and your picture(s) to class on Friday.


42 comments:

  1. http://imgur.com/sWHvA.png
    http://imgur.com/sWHvA.png

    For my gap minder graph, I decided to illustrate a major problem in our world today, HIV. On the X-axis I have percentage of adults (age 15-49) that are infected by HIV. Life expectancy is on the Y-axis. This graph shows that there is a correlation between HIV infection and each country’s average life expectancy. I chose these variables because I thought I could easily show that HIV and AIDS is a huge problem that we have yet to solve. As the graph shows, African countries by far have the greatest number of people infected. The African country Swaziland has the highest rate with 26% of its population infected with the virus. Their countries average life expectancy is 40 years. The United States’ percentage is .64%. In the early 1980’s the African HIV infection skyrocketed to huge numbers. The rest of the world soon caught up and they all steadily started to rise. Life expectancy is way lower in African countries than anywhere else in the world. This could be partially due to the AIDS epidemic.
    I thought the patterns I saw were very interesting. I initially knew that HIV was an extremely hot topic in Africa. But I didn’t realize that African life expectancies were so low. I saw a lot of blue dots (African countries) fall down the life expectancy axis while also inclining on the HIV infection axis.

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  2. Helena Darah
    Ted Wesler
    Soc 101
    March 9 2010
    Gapminder Extra Credit

    For this project, I chose to relate divorce rate to the states people live in. I chose the variables Divorce rate total and U.S. citizens. I chose these two because I had a predication that environmental factors and norms of a state can determine how often people get divorced. I predicted that conservative states such as Utah, Idaho, Montana and North and South Dakota would have low divorce rates due to their states morals. I predicted that states that contained less traditional values, such as California, New York and New Jersey would have higher divorce rates do to the diversity of people and their morals and cultures. Also, men and women tend to be treated more equally and therefore, women may feel that they can file for a divorce and still be independent while some women from more conservative states tend to rely financially and completely on their husbands.
    After using the Gapminder graph, my theory proved correct. The states with more diverse culture and less traditional values tended to have a higher divorce rate. California had the highest; Texas and Florida were close at second and third. The lowest divorce rate was from Wyoming. According to the regions, the South varied tremendously. They either had very high divorce rates or extremely low divorce rates. Florida and Texas had been extremely high while Washington D.C. and West Virginia were some of the lowest.
    The North East had similar rates as well. New York was the 4th highest and Rhode Island was one of the lowest. I think New York had a high rate because women are much more independent there and do not need to rely on their husbands for financial support. Plus, New York has one of the highest rates for promiscuity which could cause divorce rates and anti-marriage beliefs. Rhode Island on the other hand is a much smaller state with very traditional beliefs. These factors play a role in the divorce rates.
    The Midwest was ranked in the middle of the states. I think this because they are more of the median of beliefs between conservative and liberal. In Ohio for example, the majority of families present a “happy medium” when it comes to divorce. Some families have more traditional values than others, but for the majority, everyone is pretty much in between beliefs on divorce.
    Finally, I was correct about the West. It contained the lowest rates throughout the spectrum. The West is a very traditional and conservative region where it is frowned upon to have a divorce. The husbands tend to support the family while the women cook, clean and care for the children. Therefore, the women that are complety dependent, emotionally and financially on their husbands and would not know what to do without them.
    In this Gapminder assignment, I have learned that my predictions were pretty accurate. States with more traditional views tend to have lower divorce rates while states with less traditional values tend to have higher ones.
    The link is listed below:
    http://graphs.gapminder.org/world/usa.php#$majorMode=chart$is;shi=t;ly=2003;lb=f;il=t;fs=11;al=30;stl=t;st=f;nsl=t;se=t$wst;tts=C$ts;sp=2.9741935483871;ti=2006$zpv;v=1$inc_x;mmid=XCOORDS;iid=pp59adS3CHWfPGhth8HHrBw;by=ind$inc_y;mmid=YCOORDS;iid=pp59adS3CHWdzxSyMtIkLXA;by=ind$inc_s;uniValue=20;iid=pp59adS3CHWedi8p5UR-KMw;by=ind$inc_c;uniValue=255;gid=CATID1;iid=pp59adS3CHWeR0Ufcou95MQ;by=grp$map_x;scale=lin;dataMin=-2091.0912;dataMax=148289$map_y;scale=lin;dataMin=-8992846.7231;dataMax=36197093$map_s;sma=93;smi=2.8$cd;bd=0$inds=

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  3. Kellie Asmus

    What do the variables measure?
    The variables I chose measure how the age of a woman’s first marriage affects the amount of children per woman, i.e. the average family size.

    Why did you choose those variables?
    I wanted to discover how women in the world are affected by different variables. I was especially interested by the indicator of the age of a woman’s first marriage. I chose the other indicator after playing around with several variables to see which ones made sense. This one in particular captured my attention because of the correlation I found in the graph.

    What does the graph show?
    This graph shows that overall women who marry younger tend to have more children, and over time, women tend to marry at older ages and have fewer children.

    What is interesting about your graph? What sociological concepts are relevant?
    One of the most interesting things I found from my graph was that women tend to marry at older ages and have fewer children than they did in the past. This shows sociological change and a change in worldwide norms. In past years, it used to be common for women to marry young and bear many children, but now there is a noticeable difference. This also points to how women’s place in society has changed. It is now acceptable for women to go to college and establish careers before they start families, which is one explanation for the change seen in the graph, especially in more developed countries, such as those in Europe and the Americas. In many African countries today, women still tend to have many children. Perhaps since their societies aren’t as developed, women are still expected to tend to families rather than seek out careers. Another interesting fact is that in China the number of children per women decreased rapidly in the 1970s while the age of marriage only showed a slight change. This is explained by the Chinese government’s attempt at population control and their “one-child policy” which encourages families to only have one child, sometimes forcing parents to pay the government if they want more children. This is an example of the sociological concepts of politics and the state, and perhaps of deviance, as some people may see this policy as inhumane and cruel. I also noticed, in looking at this graph, that over time, though families tend to have fewer children, the total population of the world increases. This interesting fact could possibly be explained by the fact that since families had more children in the past (such as the “baby boom” in the 1950s), there are now more people able to have children in general.

    http://www.flickr.com/photos/kelliea103/4423789906/in/photostream/

    http://www.flickr.com/photos/kelliea103/4423024931/

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  4. Rickelle Huggins
    Soc 101
    Gapminder Project Extra Credit


    The variables I decided to use are on the x-axis the amount of colon and rectum cancer deaths in male and on the y-axis I have cervical cancer deaths in females. The colors on the graph represent different parts of the world. Even though most of the countries are included its separated by the different continents.
    I chose to use these variables because I wanted to see which one of these health problems was bigger even though I am comparing two different issues and two different genders they both are major health problems all around the world.
    This graph shows that India has the highest amount of deaths in colon and cervical cancer (74,118). It also shows different countries that are affected by these problems. Sub-Saharan African population is affected the most. South Asia is affected the least. The point I was trying to make with the graph is to see if cervical and colon cancer only affect people in certain parts of the world, but I see that it don’t I never really did research to see how serious these conditions were but now I know that it is affecting many around the world not just in the United States. The interesting I learned about this graph is I thought the United States would have the highest amount of deaths because I am always seeing commercials and reading about how women should protect themselves from cervical cancer but it turned out that India is affected the most. Actually the U.S. only has 5,214 which is way less than India’s. I learned a lot from doing this mini project I guess I can’t judge people health issues by where they live because I thought the U.S. would have the most deaths.

    http://graphs.gapminder.org/world/#$majorMode=chart$is;shi=t;ly=2003;lb=f;il=t;fs=11;al=30;stl=t;st=t;nsl=t;se=t$wst;tts=C$ts;sp=6;ti=2002$zpv;v=1$inc_x;mmid=XCOORDS;iid=phAwcNAVuyj0%2DsqkfnD4rGA;by=ind$inc_y;mmid=YCOORDS;iid=phAwcNAVuyj2KBU%5FveE9AQg;by=ind$inc_s;uniValue=8.21;iid=phAwcNAVuyj2KBU%5FveE9AQg;by=ind$inc_c;uniValue=13369446;gid=CATID0;iid=phAwcNAVuyj0%2DsqkfnD4rGA;by=ind$map_x;scale=log;dataMin=2;dataMax=50200$map_y;scale=log;dataMin=3;dataMax=74118$map_s;sma=60;smi=12$map_c;scale=lin$cd;bd=0$inds=i238_t002002,,,,;i239_p002002agag;i117_t002002,,,,;i34_t002002,,,,

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  5. i dont know how to get the link to my picture that you want us to use. All i found was the url to the picture itself.

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  6. I have the pleasure to brief on our Data Visualization software "Trend Compass".

    TC is a new concept in viewing statistics and trends in an animated way by displaying 5 axis (X, Y, Time, Bubble size & Bubble color) instead of just the traditional X and Y axis. It could be used in analysis, research, presentation etc. In the banking sector, we have Deutsche Bank New York as our client.

    Link on Chile's Earthquake (27/02/2010):

    http://www.epicsyst.com/test/v2/EarthQuakeinChile/

    This a link on weather data :

    http://www.epicsyst.com/test/v2/aims/

    This is a bank link to compare Deposits, Withdrawals and numbers of Customers for different branches over time ( all in 1 Chart) :

    http://www.epicsyst.com/test/v2/bank-trx/

    Misc Examples :

    http://www.epicsyst.com/test/v2/airline/
    http://www.epicsyst.com/test/v2/stockmarket1/
    http://www.epicsyst.com/test/v2/tax/
    http://www.epicsyst.com/test/v2/football/
    http://www.epicsyst.com/test/v2/swinefludaily/
    http://www.epicsyst.com/test/v2/flu/
    http://www.epicsyst.com/test/v2/babyboomers/
    http://www.epicsyst.com/test/v2/bank-trx/
    http://www.epicsyst.com/test/v2/advertising/

    This is a project we did with Princeton University on US unemployment :
    http://www.epicsyst.com/main3.swf

    A 3 minutes video presentation of above by Professor Alan Krueger Bendheim Professor of Economics and Public Affairs at Princeton University and currently Chief Economist at the US Treasury using Trend Compass :
    http://epicsyst.com/trendcompass/princeton.aspx?home=1

    Latest financial links on the Central Bank of Egypt:

    http://www.epicsyst.com/trendcompass/samples/Aggregate-balance-sheet/
    http://www.epicsyst.com/trendcompass/samples/balance-sheet
    http://www.epicsyst.com/trendcompass/samples/banks-deposits-by-maturity/
    http://www.epicsyst.com/trendcompass/samples/egyptian-banks/
    http://www.epicsyst.com/trendcompass/samples/currency-by-denomination/

    I hope you could evaluate it and give me your comments. So many ideas are there.

    You can download a trial version. It has a feature to export EXE,PPS,HTML and AVI files. The most impressive is the AVI since you can record Audio/Video for the charts you create.

    http://epicsyst.com/trendcompass/FreeVersion/TrendCompassv1.2_DotNet.zip

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  7. In the Gapminder graph I created, I decided to focus on female youths around the world. My X axis measures literacy rates of youth females ages 15 to 24. This variable shows the percent of females in that range of ages that are literate. The Y axis measures the adolescent fertility rate by showing the number of births out of 1000 to women that are between the ages of 15 and 19.
    I chose to measure these variables because I wanted to see how the opportunity of education affected others aspects of life for young women. As can be seen in the graph, there is an inverse relationship between literacy rates and fertility rates of young women. This is what I expected to find because I thought that women who had the opportunity for education would be less likely to have children at such a young age. One country on the low end of literacy spectrum is Mali. 31% of females ages 15 to 24 are literate and 183 out of every 100 births is of a woman between the ages of 15 and 19. On the opposite end of the spectrum is China. China has a much high literacy rate of female youths at 99% and their adolescent fertility is much lower at 6.9 of 1000 births by women ages 15 to 19. Both of these countries support my hypothesis that women who are literate have a much lower probability of having children as a teenager. There are certainly other factors that would play into these results like wealth of the country and norms and values of the society in which they live but the relationship between literacy on young women and the number of births by young women in undeniably there.
    The graph provides data from 1997 to 2006. Over time, it appears that adolescent fertility is dropping and literacy is rising for the majority of the countries shown. For example, in 1997, India had a literacy rate of 60% and 99/1000 births were by young women. By 2006, India’s statistics changed to a literacy rate of 76% and the number of births by adolescents dropped to 63/1000.
    I found this graph to be very interesting because I think that this graph illustrates one example of life opportunity or chance playing into ones opportunities. I think that the results show that young women who are given the chance to receive and education receive opportunities that prevent them from having women at a young age. If women are educated, they have the opportunity to do more in their lives and may have other things to do than to have children. Women who are literate have a better chance to have a job that may prevent them from having a child because their time is spent working. Women who are illiterate do not have as many life chances and may think that they have little else they can do with their lives. This of course is speculation and there are certainly other factors that play into the variables that I chose but I do think that there is a strong relationship between adolescent birth rates and adolescent female literacy rates.


    http://www.gapminder.org/world/#$majorMode=chart$is;shi=t;ly=2003;lb=f;il=t;fs=11;al=30;stl=t;st=t;nsl=t;se=t$wst;tts=C$ts;sp=5.59290322580644;ti=2006$zpv;v=0$inc_x;mmid=XCOORDS;iid=pyj6tScZqmEf96wv_abR0OA;by=ind$inc_y;mmid=YCOORDS;iid=pyj6tScZqmEdIphYUHxcdLg;by=ind$inc_s;uniValue=8.21;iid=phAwcNAVuyj0XOoBL_n5tAQ;by=ind$inc_c;uniValue=255;gid=CATID0;by=grp$map_x;scale=lin;dataMin=6.66;dataMax=100$map_y;scale=lin;dataMin=1.453;dataMax=230$map_s;sma=49;smi=2.65$cd;bd=0$inds=

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  8. http://imgur.com/WIp1P.png
    http://imgur.com/JGQ6m.png

    After looking through my notes I came across our class discussion on gender inequality. I searched for many ways to look at gender inequality over time, and I finally settled with looking at the literacy rate of youths. My x-axis is the literacy rate of female youths (% of females ages 15-24), and my y-axis is the literacy rate of male youths (% of males ages 15-24). I chose these variables because the best way to show a comparison of gender inequality over the years is to compare males and females in the same category and I wanted to see the difference between the literacy rates of males and females. Although it is not very noticeable through the above two images, my graph, which starts in 1984 and continues to 2007, shows how over the years the literacy rate of women has begun to equal the literacy rate of men. It was interesting to see the effects of gender inequality, which is the disparity in rights and opportunities based on gender, shown through this graph. One of the opportunities that men had over women in the past was education, and it is interesting to see that even now, in many countries, women are still trying to catch up with men in their education. One country which demonstrates this change is India. In 1984, about 70% of men ages 15-24 were literate, while only about 43% of females ages 15-24 were literate. Twenty-three years later, about 85% of youth males were literate in India, while about 78% of female youths were literate. Although women were still not caught up to men in 2007, they had decreased the literacy rate gap from a 27% difference to a 7% difference.

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  9. http://imgur.com/eCfTt.png
    http://imgur.com/TvFi2.png

    1) What do the variables measure?
    The variables I chose are Income per person (GDP/capita, inflation-adjusted $) vs. Bad teeth per child (12 yr)
    And
    Income per person (GDP/capita, inflation-adjusted $) vs. Sugar per person (g per day)

    2) Why did you chose those variables?
    I chose the first set of variables because in my Geography class we were discussing how indigenous peoples, and those without outside foods have almost perfect teeth, while those who have been introduced to processed foods have terrible teeth.
    The second set of variables was chosen because I wanted to see how much sugar affected the teeth of certain countries. I kept income per person the same so that I had a control variable.
    I also thought it would be interesting to compare something more obscure than just HIV or energy usage.

    3) What does the graph show?
    The graph shows that countries who are more isolated and do not have imported processed foods have a higher chance of having bad teeth than those who consume less sugar and fewer processed foods. It also shows how the United States and other core countries have the money to fix teeth and therefore are outliers in this data set. Or rather that is inferred.

    4) What is interesting about what you found in your graph?
    I just find it really interesting how, especially in Africa, the data fits almost exactly what I assumed it would look like. That countries in Africa, because they are more isolated would of course also be poorer and therefore not be able to afford processed sugary foods. Thus they would have less sugar in their diet and better teeth.

    5) What sociological concepts are relevant?
    The sociological concepts of globalization and external influence are particularly relevant. Not until foreign products are introduced to the richer nations are their teeth poor.

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  10. Emily Brockmeier
    Gapminder Project
    Due: 3/12/10
    Gmail: eb321908@ohio.edu, username: Emily
    Explanation of Image
    1. The (x) axis variable measures U.S citizens, and the (y) axis variable measures percent of people with bachelor degrees.
    2. I chose these variables because I wanted to see the difference how many people are pursuing a degree in higher education in the U.S compared to how many have in the past.
    3. The graph shows that more people today are pursuing a degree in higher education than in the past. It also shows that the southern region of the U.S has the least percent of people with bachelor degrees compared to other regions in the country.
    4. I think it is interesting that the South has the fewest percent of people with bachelor degrees.
    5. One sociological concept that is relevant is that more people today are earning bachelor degrees than they have in the past. This is possible because there are more opportunities to go to college today than there have been in previous years. Financially, there are more scholarships available; also there are more women and minorities attending college than there ever has been before. Another sociological concept that is relevant is that there are less people in the southern region earning degrees. A lot of this is due to their geographical status. The south is known for being an agricultural area, that being said, many young people in this region have to work on their families’ farms to maintain their way of life, rather than attending college.

    Here is the link to my gapminder:

    http://www.gapminder.org/labs/gapminder-usa/#$majorMode=chart$is;shi=t;ly=2003;lb=f;il=t;fs=11;al=30;stl=t;st=f;nsl=t;se=t$wst;tts=C$ts;sp=2.9741935483871;ti=2006$zpv;v=0$inc_x;mmid=XCOORDS;iid=pp59adS3CHWdzxSyMtIkLXA;by=ind$inc_y;mmid=YCOORDS;iid=pp59adS3CHWfoNDogaYwLKQ;by=ind$inc_s;uniValue=20;iid=pp59adS3CHWe46qwN4wmBAg;by=ind$inc_c;uniValue=255;gid=CATID1;iid=pp59adS3CHWe46qwN4wmBAg;by=grp$map_x;scale=lin;dataMin=102;dataMax=36249872$map_y;scale=log;dataMin=12;dataMax=48$map_s;sma=93;smi=2.8$cd;bd=0$inds=

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  11. Kristen Regan

    The image: http://imgur.com/UEENx.jpg

    The variables in the graph measure the average age of women in their first marriage and the average income per person. I chose these variables because there always seemed to be a trend that the more money a country had, the later women would be likely to get married for the first time. These variables can show how the more money people in a country have, the more likely women are going to be able to wait to get married by their own choice instead of being forced into it. This graph shows a general trend that this is the case as there is almost a line in relation to income and when a woman is first married.
    On this graph I tracked the United States, because I was interested to see the trends of our own country and also India, which has a significantly lower GDP to show contrast. The trend of the United States seems to be as the country grains more money, the age of first marriage also increases. There was an interesting trend around the late 1940s where the age of first marriage decreased, but this could be easily explained by World War II as many people got married before their boyfriends went off to war. India on the other hand started off with a significantly lower GDP and marriage age. India followed the trend, however, and as the countries income per person increased, so did the marriage age.
    One sociological concept that could be applied to this graph is gender inequality. Normally if a country has more money per person, the women will have more rights. When women have more rights they can make more choices for themselves instead of simply doing as they are told to do. Many countries are the graph show this trend as the more money people have the older the women are at their first marriage. Another concept is norms. Many norms of countries are to get married early because that has always been what happened in their country. These norms were set up and no one ever went about changing them. Normally, if the country has norms for very early marriage, it is more likely that it is an agrarian country where they need their women to be married and early and have children. If countries are more likely to get married later, it is because women have more options to go to school and get a job before settling down.

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  12. www.bit.ly/d1FVip

    Replacement Assignment #2
    Gapminder Graph

    In my gapminder assignment I was hoping not to find the results that I eventually did. I discovered a disturbing relationship between life expectancy and a person’s average income. I used the variables of life expectancy at birth (in years) and the country’s average income per person to try to determine if the two variables were related in any way. In my opinion I find it unethical that a person’s yearly income effects how long they live. I chose these two variables because I was curious to see if a person’s income in a different country affected the amount of years they were able to live. The graph shows a relationship between life expectancy and a person’s average income in different countries. South Africa is the area that has suffered the most according the gapminder graph. This is astonishing to me because of the fact that North Africa is high on the life expectancy and average income scale. I am compelled to do more research to determine why there is such a big difference from North Africa and South Africa. In terms of yearly income and average life expectancy South Africa is not even close to competing with the rest of the world. This disparity is something that should not happen in the world. Continents like North American and South American are at the upper notch of the world thriving with the best of the best health care. A person’s average income should not determine how long they are able to live. Health care should be a given to any person in any situation.

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  13. amanda.gilkey@gmail.com
    GapMinder Graph

    Here's the link to my graph:
    www.bit.ly/d4wujX

    The variables on my GapMinder graph represent the life expectancy at birth (years) for agricultural works (% of labour force). I chose these variables to use in my graph because I personally come from an agricultural lifestyle, and my family fits that same description.
    I was interested in finding out the life expectancy of the hard-working men and women across the world that do agriculture as their line of work. When I think of agriculture, I think of farming which in turn is extremely difficult. Pretty much my entire family and many of my friends are involved in this sort of thing everyday of their lives; it’s how they make a living. Whether it’s putting up hay, planting crops, or anything along those lines, it’s hard labor, and I know it can take a toll on the human body after enduring it for several years at a time.
    Once I selected the variables I wanted to use for my graph, I was actually surprised at the results that appeared. I think of the United States as filling the position as an “agricultural” country so to speak; as a result, I figured that the life expectancy for the population in the U.S. would be much lower than that of any other country. The average life expectancy for an agricultural worker in the United States was nearly 80; however, the average life expectancy for Africa was a mere 50...both of these results as of 2007.
    Japan and China contained the highest life expectancies, which seems normal to me because those countries seem to be more “industrialized.” They make the majority of the products sold here in the United States; this means that most employers in Japan and China work in factories.
    When it comes to sociological concepts, I would have to say that according to your geographical location is really what determines citizens roles in society. For example, those that live in Africa have extremely low employment rates to begin with; therefore, it’s not a surprise that they have the lowest life expectancy in this line of work. They are right along the equator in extremely hot conditions. The combination of heat and farming doesn’t go hand in hand health wise. As I mentioned above, Japan and China contain large cities where many employers manufacture vehicles, clothing, etc. in factories throughout the day. When we think of foreign countries here in the United States we think of industrialized workers, not “agricultural” related individuals. The U.S. might make 5-10% of the items sold in this country; the rest is shipped in from across the world because that’s the role that these other countries play, as we do when we ship crops and things of that nature to countries across the globe.

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  14. kh231609@ohio.edu
    URL:
    http://www.gapminder.org/world/#$majorMode=chart$is;shi=t;ly=2003;lb=f;il=t;fs=11;al=30;stl=t;st=t;nsl=t;se=t$wst;tts=C$ts;sp=5.59290322580644;ti=2007$zpv;v=0$inc_x;mmid=XCOORDS;iid=pyj6tScZqmEcKuNdFCUo6TQ;by=ind$inc_y;mmid=YCOORDS;iid=phAwcNAVuyj1jiMAkmq1iMg;by=ind$inc_s;uniValue=8.21;iid=phAwcNAVuyj0XOoBL_n5tAQ;by=ind$inc_c;uniValue=255;gid=CATID0;by=grp$map_x;scale=log;dataMin=0;dataMax=634000000$map_y;scale=log;dataMin=269;dataMax=119849$map_s;sma=49;smi=2.65$cd;bd=0$inds=

    The variables of my graph show the comparison between the income per person and the total amount of cell phones for every country between the years of 1960 and 2005. The size of each dot that represents each country are different sizes based on the population of the individual country, and they are color coordinated as to which continent they come from. I chose these variable because I thought it would be interesting to find out which countries usually have the least amount of cell phones, and if the wealthiest countries normally had the highest amount of cell phones. I also chose to compare these three sets of data (cell phones, income, and population), because I think they related to each other. The amount of income and population affects the amount of cell phones that are owned in a country. If I chose to compare, for example, the infant mortality rate with the amount of earth quakes, there wouldn’t be much for me to write about in terms of how these two things affect each other. My graph shows that China, India, and the United States have the highest number of cell phones and San Marino, Sao Tome and Principe, and Tonga have the fewest amount of cell phones. For income level, the Congo Dem. Rep, Zimbabwe, and Liberia have the lowest income level while Luxembourg, Qatar, and Macao China have the highest income levels. I found it interesting that some of the poorest countries, such as a lot of the African countries, don’t have the fewest number of cell phones by any means. In fact, one of the wealthiest countries, San Marino, has the fewest cell phones by far. It didn’t take me long to figure out though, that I wasn’t taking into account population. San Marino is also one of the tiniest countries, so it’s actually no surprise that they have the fewest number of cell phones. Another thing I found interesting was that Finland was the first country to own cell phones, starting in 1980. Directly after them, the populations of United States, Japan, and Norway all started getting cell phones. It wasn’t until 1987 that China started getting cell phones. In the 1990’s cell phones started becoming really popular. North Korea was the last country to get cell phones in 2006. The sociological imagination is the understanding that social outcomes are shaped by social context and social actions, it’s a way of thinking about things in society that have led to some sort of outcome, and understanding what causes led to that outcome. My Gapminder chart can be explained by the sociological imagination because the more populated, wealthy, and technology advanced countries is what caused the United States, China, and Japan to have the largest number of cell phones.

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  15. What do the variables measure?
    On my gapminder the variables measure time, from the 1800’s to present day and Children per women (total fertility)

    Why did you choose those variables?
    I choose these variables because I wanted to see the trend of how many average children were born between now and the 1800s and I also wanted to compare the U.S. with another big country.

    What does the graph show?
    This graph shows that in the 1800’s an American woman was having an average of 7 children in her life while a Japanese woman was having an average of only 4.5 children. In Japan, the number of children per women declined to 3.5 until about the 1820’s when it rose again back to 4.5 children. After that it began to decline again to just under 3.5 in the 1870’s. From the 1880’s to the 1940’s there was a big increase in Japan up to about 5.5 children per woman. After this boom the graph calms down, and continues to decline until present day where it is about 1.5 children per woman in Japan.
    In the U.S. the children per woman declined at a steady rate from 7 children to 2 children between 1800 and 1940. Between 1940 and 1960 there is a spike in the graph that changes the average from 2 to 4 children per woman. After 1960 this number declines to the 1.5 and doesn’t begin rising until recently where the average has returned to 2 children per woman.


    What is interesting about your graph? What sociological concepts are relevant?
    What I think is interesting is the difference between the two graphs at the beginning in the 1800s, the U.S. has a higher average by 2.5 children. When thinking about why this may be, I cam to the conclusion that this was around when many settlers were coming to America and settling here to begin a family.
    It is also interesting to see that around the 1940’s there is a sharp decline in the number of children per woman. When you look at Japans history in 1945 two atomic bombs were dropped on two cities in Japan killing 250,000 people.
    The obvious baby boom for America is between 1940 and 1960 during the post WWII era, eliminating the economic and social pressures on Americans that kept them from starting a family. There is one norm prevalent in the graph, the present day average of 2 children per household. This is what many view as “the American dream,” starting a family in a big house with a white picket fence, a happy couple, two kids, and a gold retriever.

    link to image below:
    http://imgur.com/NC25d.png

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  16. www.bit.ly/aa6cbr

    The gapminder graph I created measures the adolescent fertility rate amongst women ages 15-19 and the crude birth rate in the different countries. On the x-axis I have the crude birth rate (births per 1,000 population and on the y-axis I have the adolescent fertility rate (births per 1,000 women ages 15-19). I chose these variables because I thought it would be interesting to see the how the younger people start having babies the more the crude death rates occur. This could simply be because their bodies are not ready to hold a child or the fact that they are not ready to have a child. The graph starts at 1997 and goes to 2006. When the graph is played it shows how all the countries decay but Africa has more increases than the rest of the countries. The third-world countries definitely have more crude birth rates per adolescent count than all the other countries.
    I found that it is very interesting that most of the counts decay as time goes on. This could be because we are creating better technology and medicines that help the babies grow and also there are ways to abort the child in high risk situations. In 2006, Africa is still in high risk if crude birth rates per adolescent births because they may not have the technology or medicines that other countries have in order to reduce crude birth rates.
    Ah165309@gmail.com

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  17. This comment has been removed by the author.

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  18. http://imgur.com/HSEdI.png

    Cell Phones and Total Income

    What do the variables measure?
    The two variables I chose were; total income and total cell phones
    This first variable, total income, measures the total amount of money the country has made in a specific year. For example in the year 2006 the United States has a total income of $114,100,956,353,536. In other words the United States made a LARGE amount of money. The second variable I chose
    was the number of cell phones a country has. This represents how many people have cells phones in a country. In china, they have a total of, 46105800 cell phones. Which averages 35 people having cell phones out of 100 in the year 2006.

    Why did you chose those variables?
    I chose these variables because I thought it would be an interesting comparison. I was wondering if other countries had as many call phones as the citizens of the United States. I chose total income to see if there was any correlation between the two. Interestingly enough, the countries who have a high total income were the countries who also had the higher number of cell phone totals.

    What does the graph show?
    The graph represents a correlation between total cell phones and total income. The higher the total income, the higher the total cell phones. Japan, China, and The United States all have tremendously high total incomes, and in that perspective they also have very high cell phone total. Countries who earn a higher income, have more money to spend giving them the option to buy cell phones.

    What is interesting about what you found in your graph?
    The most interesting comparison between the two variables is that one DOES affect the other. The higher the total income, the higher the cell phones totals. Which seems to be evident here in the United States. Many U.S. citizens have luxury items, such as TV’s, radio, and cell phones. The higher income the more luxury you can afford. That’s the reason we have so many cell phones compared to somewhere like Iceland, which only has 300,000 cell phones.

    What sociological concepts are relevant?
    We can look at the values of countries. Countries who have a higher total income will obviously have some different values compared to those with a lower income. They do not care so much about a cell phone as much as higher income country therefore there is a smaller cell phone totals. On the flip side, we see cultural relativism. Countries like the United States, Japan, China all are similar (not based on values) because of similar economies. Then we see smaller countries are similar with their lifestyles. The ways of lives differentiate tremendously depending on the status of their country.

    Gmail: uacole@yahoo.com

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  19. Danielle Meister Soc 101 dmeister dm400609@google.com
    Image explanation
    1. The graph represents on the vertical axis total unemployment and on the horizontal axis it represents high school graduates (%). It is the examining the data between people that are unemployed and people that do not end up graduating from high school
    2. I decided it would be interesting to choose this data because I believe the two pieces of information should relate to each other. If you graduate high school you should go on to get a job afterwards. However, this data only shows people that graduated high school. The people in the data did not get a college degree. Most people cannot find work if they do not go on to graduate from college. It Is interesting for me because I am a college student at the moment. Therefore, I am able to learn about the percent of people unemployed that did not end up attending college.
    3. The graph shows that people in the south graduated high school less than anywhere else. Ironically, they have the lowest rates of unemployment. California is the only state in the west that has a noticeable rate of unemployment. It is the state with the lowest high school graduates and highest unemployment
    Interesting concepts
    4. All the states in the west have a good percentage of high school graduates except California. An explanation for this could be because California is a bigger state. The entire south has low unemployment rates. The Midwest has between 100,000-400,000 unemployed depending on the state that is being examined. From 1995-2005 the unemployment rate stayed about the same but the number of high school graduates went up very high. From 2000-2002, the unemployment rate jumped higher very fast and then went back to where it previously was. In 1990, the west had the highest rate of people that graduated high school. In 2005, the rest of the world caught up to the west. The data was even.
    5. Relevant sociological concepts- Unemployment leads to poverty which is a very important topic in sociology. Most people that do not have the initiative to graduate from high school also do not have the initiative to work. These people would rather not waste time working. The information correlates well. It also shows that people that do not get a degree do not get as many job offers. The unemployment data has distortions in it. Some data is skewed because it does not show the people that gave up or is only working one day a week. Once you give up looking for a job, you are no longer considered to be unemployed. It is considered unacceptable not to work in today’s society. Most parents both work in order to pay the bills.
    Photo: http://graphs.gapminder.org/world/usa.php#$majorMode=chart$is;shi=t;ly=2003;lb=f;il=t;fs=11;al=30;stl=t;st=f;nsl=t;se=t$wst;tts=C$ts;sp=2.9741935483871;ti=2006$zpv;v=0$inc_x;mmid=XCOORDS;iid=pp59adS3CHWd-wpn5I5CsYA;by=ind$inc_y;mmid=YCOORDS;iid=pp59adS3CHWcvojVZKtekLA;by=ind$inc_s;uniValue=20;iid=pp59adS3CHWedi8p5UR-KMw;by=ind$inc_c;uniValue=255;gid=CATID1;iid=pp59adS3CHWeR0Ufcou95MQ;by=grp$map_x;scale=log;dataMin=8653;dataMax=1190604$map_y;scale=log;dataMin=64;dataMax=91$map_s;sma=93;smi=2.8$cd;bd=0$inds=

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  20. Kevin Sullivan
    Sociology 101
    Gapminder extra-credit

    When I was doing the Gapminder I chose to make the y-axis Income per person (GDP/caitaita) and the x-axis population aged 20-39 years old. The reason I choose theses two to be my axis is because I find money to be interesting when it comes to a country. I like to know how much each person on average makes because it lets me know where that country stands compares to others. The variables measure on the y-axis the amount of money people make in that country. On the x-axis it measures the amount of people that live in the country. For example China has the highest population at 435,244,400 million and Luxembourg has the highest income at 76,585.
    The axis that I chose made a very interesting graph. I pretty much knew where the countries lined up according to population and income but there were some surprising ones, for example the Bahamas’. Surprising the Bahamas had more people living on their island and there income was not as low as I expected it to be.

    http://www.gapminder.org/world/#$majorMode=chart$is;shi=t;ly=2003;lb=f;il=t;fs=11;al=30;stl=t;st=t;nsl=t;se=t$wst;tts=C$ts;sp=5.59290322580644;ti=2007$zpv;v=0$inc_x;mmid=XCOORDS;iid=rHrin819tHgZudARnpsN0Mg;by=ind$inc_y;mmid=YCOORDS;iid=phAwcNAVuyj1jiMAkmq1iMg;by=ind$inc_s;uniValue=8.21;iid=phAwcNAVuyj0XOoBL_n5tAQ;by=ind$inc_c;uniValue=255;gid=CATID0;by=grp$map_x;scale=lin;dataMin=4000;dataMax=479290000$map_y;scale=log;dataMin=269;dataMax=119849$map_s;sma=49;smi=2.65$cd;bd=0$inds=

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  21. My Gapminder graph uses the variable of Income Per Person and shows how it affects the Infant Mortality Rate. I chose these variables because I was interested to see if there is a direct relationship between the two. In my graph I narrowed the number of countries down to three. I chose the United States because it is one of the most rich, healthy countries in the world. I chose the Democratic Republic of the Congo because it seemed to have the lowest income level. Finally, I chose El Salvador because it seemed to be in the middle of the previous two countries’ income levels. In my graph, I found that Income Per Person is directly related to the Infant Mortality rate, in that as income decreases, the infant mortality rate increases and vice versa. My graph also showed that in the US and El Salvador, income levels grew and infant mortality rates decreased as time passed.

    An interesting discovery in my graph, was that the Democratic Republic of the Congo seemed get worse in terms of income levels and stay relatively the same in infant mortality rates as time passed. This occurrence can most likely be explained by Conflict Theory. For the United States and El Salvador, once they got into the trend of improving, they kept on improving because they have the resources and power to do so. On the other hand, the Democratic Republic of the Congo does not have the resources that these two countries have, and therefore can only stay consistent or get worse.

    http://imgur.com/SpHCo.png

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  22. Kelli Shultz

    Explanation of my graph:

    1. What do your variables measure?
    The y-axis of my graph represents the percentage of woman ages 15-49 that use contraceptives. The x-axis represents percents of total births that were attended by skilled staff. The color correlation of this graph represents the mortality rate of children less than 5 years old per 1,000 births.

    2. I decided to choose these variables because I recently did a paper on the importance of teaching teenagers about the different uses of contraceptive. They correlate well with each other because obviously as the usage of contraceptive lowers, the pregnancy rate grows. Also, I inferred that in poorer countries where medical staff does not assist birth, these would be the same countries in which the use of contraceptives is not stressed or affordable. And also, with the color variable, I inferred that the presence of a medical staff would be a direct factor of the mortality rate for children under 5.

    3. The graph shows that the lower percent of women using contraceptive, the less likely it is for a medical staff to assist a birth. The lower both of these factors are, the higher the mortality rater for less than 5 years per thousand births is. Likewise, the more woman that use forms of contraceptive is positively linked to the likelihood of a medical staff to be present during a birth, and for that countries mortality rate for children under 5 to be lower.



    What is interesting about what you found on your graph?

    I find my graph very interesting because the variables are very distinctively related. The line of relativity is positive throughout the graph with few extreme outliers. I think that my graph could also be linked to many educational and economic factors as well because the more educated and wealthy a country is would affect all of the variables I chose. For example a wealthy, educated country such as the US teaches people about contraceptive use, has doctors present for most of their births, and the mortality rate for children under 5 is obviously very low. The graph has shed light on the fact that impoverished countries, such as Ethiopia, (where only 13% of women use contraceptives, and only 5.7% of births are attended by a doctor and the mortality rate is 127 per 1,000) face many issues other than poverty. Many children are being born unplanned, mothers may be dying giving birth, and children are often times dying young. The strand of problems for countries such as Ethiopia seems never ending.

    www.bit.ly/aOwo1S
    http://imgur.com/iBozl.png

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  23. In my graph I chose to look at percent of high school graduates corresponding with income per person. I chose these variables because i believed there was a correlation between a person graduating high school and how much money there family made. If a person's family did not have a whole lot of income, the oldest child may drop out to work full time and help provide for the other children in the family. i wanted to see if this seemed to be true. This graph does show that as the average income goes up high school graduation percentages increases. All of the dots seem to move up and to the right.

    I thought it was interesting that by clicking on individual graphs you saw that each one varied and that there could be other factors affecting the graduation rate. Ohio was one of many that showed a basic correlation between graduation rate and income. In 2006 the percent had gone up ten percent and the income about ten thousand dollars. This seemed to be the way most of the states dots worked. A few of them showed other interesting transitions. Alaska's income actually went the opposite direction. The income was becoming less but the percentage of high school graduates still continued to rise. In West Virginia, the income only went up about seven thousand but the percent jumped from sixty-seven to eighty-one. That is a percentage jump of fourteen points. The same thing was true for Mississippi. In Washington D.C. the opposite was the case. They went from an income of about 104,000 to about 138,500. There percentage of high school graduates also increased but only by eleven percentage points. That is still a lot but not a huge number you would except with that huge jump in income.

    This all led me to believe there were many more reasons the percentage of high school graduates was increasing. It might be that the higher percent of high school graduates is leading to a greater income per person. We could also say that because income is per person more populated areas might have more people in school that do not work causes the income per person to go down. In Washington D.C. it might be that high power people live there and they always had a good percent of high school graduates and we just started paying the people in government more money and that is why the percent of graduating steadily increased, instead of taking a dramatic jump like income did. There could be many other factors and are not shown on this graph. One might be a social norm.

    It had become more of a norm in society to graduate and go to college. It used to be a norm that if you were a boy and lived in farm country you might not have needed to graduate from high school because you were just going to work on the farm. In our society today we see this less and more people are graduating and going on in the world to get better jobs. A farmer does not make much money and the cost of living has gone up so fewer kids see that as a job they don't ant to do for the rest of their lives. There is more technology now days and it is easier to get a degree online as well. Our society is changing and moving forward and so are our jobs that are offered. This could be a reason more people are graduating from high school instead of dropping out to do other things.

    My URLs
    1990:http://imgur.com/2245r
    2006:http://imgur.com/MH3Ou

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  24. www.bit.ly/aVbZBc

    1. The two variables I used in this assignment are “Under-five mortality rate (per 1,000 births) which is on the y-axis, and on the x-axis I used “Income per person.
    2. I choose these variables because I was interested in how the mortality rate of those five and under directly related to the GDP of the average person in the country.
    3. My graph shows as time moves forward in all countries the mortality rate has decreased over the past 58 years. But putting the graph in motion you can see that those countries with a higher GDP, the lower the mortality rate is.
    4. I was looking at each country individually and for the most part I wasn’t too surprised about what I saw. But one thing that I did notice that kind of caught my eye was that China is actually right smack in the middle of everything. I would of figured with there growing economy and being able to spend more money on medications and better equipment that there mortality rate would be a lot similar to the United States or the UK. I also noticed the smaller the population the less chance of mortality occurring, except those countries in the Southern Africa. Which we all see on the television trying to help them out.

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  25. Matt Johnson
    Ted Welser
    Soc 101- Replacement Assignment 2
    3/ 7/2010


    1.) The variables that I chose to measure were in 2007 the “Literacy Rates, adult females (% of females ages 15 and above who can, with understand, read and write a short, simple statement on their everyday life)

    2.) I chose to use these variables because I wanted to see how many females in the world are literate. I was interested in how different countries compared with one another as far as these statistics are concerned. I wanted to see what type of effect a countries income had on the literacy of their citizens and exactly how important those factors were to the growth of education within a county.

    3.) The graph had somewhat mixed results, I found. For some countries the income of the citizens did not really have that great of an effect on the literacy rates of their women. In general, however, the results that I saw were that countries who had more money were much more likely to have more literate women. In America, Asia and Europe the amounts of money people made were are all very comparable to one another as well as the literacy rates. In poorer areas such as Africa and South Asia where the average income was much lower, people tended to have lower literacy rates.

    4.) The thing that interested me about the graph was that although most of Africa has lower incomes not all areas had low literacy rates. For example Liberia has the lowest income rate but they are far ahead of other African countries for literacy rates and that makes me wonder why this is. Perhaps this areas take education more seriously that other parts of Africa.

    5.) A sociological concept that this pertains to is that people with more resources can advance themselves further than areas with less resources. The countries with more money were more literate and countries with less money, in general, had much lower literacy rates.

    http://www.gapminder.org/world/#$majorMode=chart$is;shi=t;ly=2003;lb=f;il=t;fs=11;al=30;stl=t;st=t;nsl=t;se=t$wst;tts=C$ts;sp=5.59290322580644;ti=2007$zpv;v=0$inc_x;mmid=XCOORDS;iid=phAwcNAVuyj1jiMAkmq1iMg;by=ind$inc_y;mmid=YCOORDS;iid=pyj6tScZqmEc96gAEE60-Zg;by=ind$inc_s;uniValue=8.21;iid=phAwcNAVuyj0XOoBL_n5tAQ;by=ind$inc_c;uniValue=255;gid=CATID0;by=grp$map_x;scale=log;dataMin=194;dataMax=96846$map_y;scale=lin;dataMin=6.66;dataMax=100$map_s;sma=49;smi=2.65$cd;bd=0$inds=

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  26. Ashley Mierzwiak
    AshMierz (gmail login)
    Howard Welser
    SOC 101
    Gapminder Graph
    The variables of the graph that I made measure math achievement scores in the 8th grade in relation to income per person. I kept track of all of the geographic regions in my map. I chose these variables because I wanted to know if it was true that those who are better off in society are more likely to succeed. It is believed that those who have resources tend to succeed. I wanted to see if that was accurate in regards to education and income.
    The graph blatantly shows that those with higher incomes had higher achievement scores. The overall graph does not level off at any stage, and constantly increases. The only continent that showed some type of regression was the Middle East and North Africa. In contrast, East Asia and the Pacific countries have the highest scores per income per capita. America, Europe, and Central Asia are next behind East Asia and the Pacific countries within their scores. The overall graph has a positive, constant rate of change.
    I thought it was interesting that the Middle East and North Africa showed regression in the scores as the income increased. Their points on the map are scattered and do not have a constant, positive change. This is surprising because although Africa is not one of the wealthiest nations in the world, one would still figure that while their income increased, their scores would increase like everyone else’s. However surprising, that is not the case.
    Like mentioned earlier, the concepts that are sociologically relevant are those of social conflict. Specifically the Matthew Effect. The Matthew Effect describes that those who have the resources are more likely to succeed than those who do not have the privilege of having those resources. The families or individuals that have the money are able to send their children to better schools thus providing them with better education. The better the education the higher

    http://www.gapminder.org/world/#$majorMode=chart$is;shi=t;ly=2003;lb=f;il=t;fs=11;al=30;stl=t;st=t;nsl=t;se=t$wst;tts=C$ts;sp=5.59290322580644;ti=2007$zpv;v=0$inc_x;mmid=XCOORDS;iid=phAwcNAVuyj1jiMAkmq1iMg;by=ind$inc_y;mmid=YCOORDS;iid=phAwcNAVuyj3fwfA8XA25Eg;by=ind$inc_s;uniValue=8.21;iid=phAwcNAVuyj0XOoBL_n5tAQ;by=ind$inc_c;uniValue=255;gid=CATID0;by=grp$map_x;scale=log;dataMin=194;dataMax=96846$map_y;scale=lin;dataMin=276;dataMax=609$map_s;sma=49;smi=2.65$cd;bd=0$inds=

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  27. Eric Ludlow
    SOC 101
    Professor Howard
    Extra Credit: Gapminder graph

    The variables measure the total number of Bankruptcy Cases of each state in the US (x-axis). The (y-axis) measures the Road Accident of Deaths of each state in the US. I chose these two variables, because I thought it would be interesting to look at the economic side of the spectrum. As well as the number of as the traffic accidents that occur, during every year. For me, I thought more people who really don’t care much about their finance, really wouldn’t care about themselves on the road. I also wanted to look at a graph, which had sort of an upward trend on the graph. I do realize that the bigger states will be higher number of population on the graph, since they have more people in their state. I think if every state had the same population the graph would be more equal. This being said, I think that all of the United States really does care about their people and not seeing them get in trouble financially. Overall, the upward trend is mainly the cause of the population on the graph.
    With all of the information, this brings me to my first point. The graph shows people care about themselves, even if they are in financial trouble. When I first took a look at the graph I thought that many people who are in bankruptcy would not give a care in the world, what happens to them on the road. This right here shows that America is all about trying to get people out of financial trouble. As I look at the graph again I see the last date was 2006. The United States was still not in a recession as we are in right now. If the graph was current I think more people would be in bankruptcy and the graph could even have a straight line forming. That could lead to the cause of more deaths in smaller states with less population. The final point I would like to make about the graph is people with more financial trouble could ultimately lead to death, whether it’s a car crash or committing suicide.
    These concepts that are relevant with sociology are social structure. The meaning is a persistent pattern in the distribution of relationships, attributes, holdings in a population. With this occurring it could lead to cause and effect. When one thing leads to another and things start become a downward effect. These relationships both occur in one way by having money problems that can lead to deaths. Also society looks at bankruptcy as a bad situation and people can even go as far as killing themselves over money. Patterns and roles are a big part of leading to deaths, by not making the right choice in life. I think most people have money problems typically don’t have a care about themselves or others around them, which can also lead to death. I think that sociology plays a big role when it comes to the outcomes of death and money.


    http://www.gapminder.org/labs/gapminder-usa/#$majorMode=chart$is;shi=t;ly=2003;lb=f;il=t;fs=11;al=30;stl=t;st=f;nsl=t;se=t$wst;tts=C$ts;sp=2.9741935483871;ti=2006$zpv;v=0$inc_x;mmid=XCOORDS;iid=pp59adS3CHWeelFlxBre8KQ;by=ind$inc_y;mmid=YCOORDS;iid=pp59adS3CHWfvLw6mehmTag;by=ind$inc_s;uniValue=20;iid=pp59adS3CHWedi8p5UR%2DKMw;by=ind$inc_c;uniValue=255;gid=CATID1;iid=pp59adS3CHWeR0Ufcou95MQ;by=grp$map_x;scale=log;dataMin=229;dataMax=211335$map_y;scale=log;dataMin=37;dataMax=5192$map_s;sma=93;smi=2.8$cd;bd=0$inds=

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  28. The y-axis variable is life expectancy at birth measured in years. The x-axis variable is total income. The reason that I chose these variables was because I knew that United States had the most income out of any other countries, and I wanted to see if any other countries had a higher life expectancy. It would make sense if the citizens of the United States had the highest life expectancy because they have more money for the best types of health care. The graph shows that from 1960 until 2005, the Unites States has the income but not the highest life expectancy. It turns out that Japan has the highest life expectancy but less than half of the income of the United States. The lowest country on the chart is Swaziland.
    It is interesting that Japan and other countries like Canada and Germany have higher life expectancies than the United States. I also thought that South Korea and Mexico would have been lower on the life expectancy part of the chart. Another thing that shocked me about Germany was how they were ranked in 4th for total income. A sociological concept that pertains to this is that people with higher incomes tend to have higher life expectancies because of the amount of resources that are available to them.

    http://www.gapminder.org/world/#$majorMode=chart$is;shi=t;ly=2003;lb=f;il=t;fs=11;al=30;stl=t;st=t;nsl=t;se=t$wst;tts=C$ts;sp=5.59290322580644;ti=2006$zpv;v=0$inc_x;mmid=XCOORDS;iid=pyj6tScZqmEfI4sLVvEQtHw;by=ind$inc_y;mmid=YCOORDS;iid=phAwcNAVuyj2tPLxKvvnNPA;by=ind$inc_s;uniValue=8.21;iid=phAwcNAVuyj0XOoBL_n5tAQ;by=ind$inc_c;uniValue=255;gid=CATID0;by=grp$map_x;scale=lin;dataMin=25531524;dataMax=11410956353536$map_y;scale=lin;dataMin=23;dataMax=86$map_s;sma=69;smi=2.65$cd;bd=0$inds=

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  29. For my Gapminder graph, I chose to relate infant mortality rate (per 1,000 births) to income per person. This will compare the wealth of each nation to the rate of child survival. I chose these variables because I feel that it relates to the issue of global inequality within the health care systems. I think that this is a very prevalent issue in today’s society, from diseases like the AIDs epidemic, to infant mortality, and life expectancies.
    I chose infant mortality as one variable because I thought it would show the clearest representation of the wealth vs. health situation. People in poorer countries do not have the same chances at survival that people born in wealthier countries, simply because the same health care is not available to them. I think this is a terrible misfortune as even though there is technology and healthcare developments that would keep these children alive, they cannot pay for it, and therefore the lower income countries must suffer.
    As expected, the graph shows that the countries with higher income per person have a lower infant mortality rate. This shows that the availability of proper and modern health care is only available to those countries that are wealthy enough to afford it. This shows the inequality as simply because people in the lower income countries cannot afford the same health care, they have a much harder time keeping the infants alive.
    My graph shows that the country with the highest infant mortality rate is Afghanistan, with 165 deaths per 1,000 children born. This surprised me because based on media and other sources I expected the highest infant mortality rate to be a country on the African continent. Afghanistan is closely followed by most of the African countries, which are known to have health care problems, especially with infant mortality.
    Another surprise to me was that Singapore is tied for the lowest infant mortality rate with only 2 deaths per 1,000. The East Asia and Pacific countries have the greatest range of both income and infant mortality, and are spread throughout the graph, with Myanmar having the highest infant mortality rate of the region. Singapore surprised me because most of the countries around it are much higher, and even China has always seemed to me to be a more developed country has a much higher mortality rate.
    Another surprise was that the 3rd highest income per person country, Qatar, held a much higher infant mortality than the other countries with similar incomes. Qatar has an infant mortality rate of 18 per 1,000 infants, while Liechtenstein, the highest income country, has an infant mortality rate of only 3 per 1,000. I expected the higher income countries to be more similar but Qatar seems to be an anomaly to my predictions.



    http://imgur.com/RUbFb.png

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  30. Julia Bunce
    Sociology 101
    March 11, 2010

    1. The variables of my Gap-Minder graph are percent of adults from 15-49 years of age infected with HIV on the vertical axis, versus the income per person on the horizontal axis.
    2. I chose these variables because there is the stereotype that only the poor countries in places like Sub Saharan Africa get infected with HIV, and I was curious to see if this stereotype was indeed true.
    3. My graph shows that in 1979, the income of a country had nothing to do with HIV, and very few people anywhere in the world was infected with HIV. However, in 2007, the data shows that these stereotypes are actually true. The countries highest on the vertical axis, or people infected with the HIV virus, are from the Sub Saharan regions. However, some of these highly infected areas are not the lowest on the horizontal axis, or income per person. This graph both proves the theory and still challenges some regions, showing there are definitely more factors than simply income that affect the percentage of people ages 15-49 infected with HIV. Some of these factors may be feminine rights, education, and others.
    4. Some aspects of sociology that could be related to this graph are sociological imagination, in that we as Americans must have these stereotypes of what it must be like to live in Sub Saharan regions, and figure that they are poor and infected with HIV. Another aspect of sociology associated with this graph is inequality. If the countries of Sub Saharan Africa had equal opportunities, education, and even income as the United States or Luxembourg, perhaps there would be a much lower percentage of people infected with HIV.

    http://i18.photobucket.com/albums/b143/ecnub1013/Picture1.png
    http://i18.photobucket.com/albums/b143/ecnub1013/Picture2.png

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  31. Andrea Searcy
    Sociology 101
    Ted Welser
    12 March 2010
    searcy1139@gmail.com
    Gapminder Replacement Assignment #2
    For this assignment, the two variables I decided to use were the life expectancy at birth (y-axis) and the percentage of adults between ages 15-49 that were HIV infected (x-axis). The variables begin in 1979 and end in 2007. I chose to use three countries: the United States, South Africa, and Rwanda. I chose these three countries after playing around with the graph to figure out which ones I could use that would help prove my point, which is that there is a direct correlation between the percent of those infected with HIV and life expectancy. The graph helps show that the higher the percentage of HIV infected adults, the lower the life expectancy, especially in impoverished countries, which lack the necessary medications to help prevent and stabilize the disease.
    In 1979, the life expectancy of Rwanda was 45 years of age and the percent of those living with HIV/AIDS was .01%, while the life expectancy of the United States was 74 and the percent of those living with HIV /AIDS was .03%. In 1981, the life expectancy of South Africa was 54 years of age and the percent of those living with HIV/AIDS was .01%. Although it was later found out that the origin of HIV/AIDS was in Africa, the United States had a large break out in the 1980s. During this time, although the life expectancy stayed pretty stagnant, the percent of those living with the disease continued to increase. I believe that because those living in the United States were more advanced in the medical world, the treatment was more readily available, although the disease was new at the time. As time moved on, the percent of those living with HIV/AIDS has increased in both South Africa and Rwanda. Within the past twenty years, the percent in Rwanda peaked to 8.4%, while the life expectancy dropped to an extreme low of 24 years of age. Also during this time South Africa’s percent of HIV/AIDS infected people was 18%, while the life expectancy of 49. Lastly, in the United States, during these twenty years, the percent of HIV/AIDS infected people was .68% and the life expectancy was 74 years of age.
    As previously stated, it showed that one of the countries, being Rwanda, had a serious low in its life expectancy, in reference to the percent of those living with HIV/AIDS. I believe that one of the most interesting things about this graph is that because of the impoverished states of the two countries in Africa, the medical needs of the people who were infected with the HIV/AIDS virus was not properly cared for, which continued to lead to more cases and lower life expectancies. In reference to sociological concepts, I think that the fact that the United States and the two African countries are two very separate cultures, with very different economic states has a lot to do with the improvement of medical advancements.

    Link Is Below
    http://www.gapminder.org/world/#$majorMode=chart$is;shi=t;ly=2003;lb=f;il=t;fs=11;al=30;stl=t;st=t;nsl=t;se=t$wst;tts=C$ts;sp=5.59290322580644;ti=2007$zpv;v=0$inc_x;mmid=XCOORDS;iid=pyj6tScZqmEfbZyl0qjbiRQ;by=ind$inc_y;mmid=YCOORDS;iid=phAwcNAVuyj2tPLxKvvnNPA;by=ind$inc_s;uniValue=8.21;iid=phAwcNAVuyj0XOoBL_n5tAQ;by=ind$inc_c;uniValue=255;gid=CATID0;by=grp$map_x;scale=log;dataMin=0.0095;dataMax=29$map_y;scale=log;dataMin=23;dataMax=86$map_s;sma=49;smi=2.65$cd;bd=0$inds=i209_d001981c0aV;i239_t001979,,,,;i185_d001979bZbd

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  32. Christina Neary
    Gapminder XC

    The variable (Y) measures math achievement scores for students in the 8th grade, and (X) measures the total number of students tested, with the age span being 15 years through 19 years of age.

    I was interested in seeing and comparing the United States’ students’ math scores with those of other countries, particularly Asian countries because of the stereotypes I hear about Asian countries being far superior in math compared to other countries.

    The graph shows that Asian students do indeed test better in math, with Japan, South Korea, China, and Singapore rounding out the top high scores. However, the U.S. does not fall too far after.

    It is interesting that Asian countries hold the top scoring math students, implying that other countries perhaps need to place more of an emphasis on education in math for their students. It is interesting that location of these students, meaning where they live, seems to have an effect upon their math skills and scores.


    Image: http://www.gapminder.org/world/#$majorMode=chart$is;shi=t;ly=2003;lb=f;il=t;fs=11;al=30;stl=t;st=t;nsl=t;se=t$wst;tts=C$ts;sp=5.59290322580644;ti=2007$zpv;v=0$inc_x;mmid=XCOORDS;iid=rFmJvuotJYE30q4nWEvpGJA;by=ind$inc_y;mmid=YCOORDS;iid=phAwcNAVuyj3fwfA8XA25Eg;by=ind$inc_s;uniValue=8.21;iid=phAwcNAVuyj0XOoBL_n5tAQ;by=ind$inc_c;uniValue=255;gid=CATID0;by=grp$map_x;scale=lin;dataMin=1000;dataMax=127281000$map_y;scale=lin;dataMin=276;dataMax=609$map_s;sma=50;smi=2$cd;bd=0$inds=

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  33. Brad Corrao

    Soc Extra Credit

    bmc5708@gmail.com

    For my Gapminder graph I chose to work with two important variables. On the x- axis of the graph I chose population and on the y- axis I though Earthquake- affected annual number would work well. These two variables show me the exact number of people that have been affected during major earthquakes in three major countries. China was number one in most affected due to the living conditions along with the gigantic population issue. The graph shows that as many as 47, 174,203 people were affected in China in 2008. India, the second largest country, also has a great number of people who are bothered by the quake. In 1988, over 20,000,000 were affected in India. The third country of course would be the US. Although not as devastating, a whopping 27,285 people were affected in 1994 (highest ever in US history).

    In my opinion, the reason for such high numbers in China and India are not just because they are located on or near faults. I truly believe it has to do with the population. We all know that China and India are the two most populated countries in the world. Also I think that being affected is also determined by living conditions. Does the US have a lower number of affected people because we have better living conditions? Perhaps it’s that we have the smallest population of the bunch. Or is it because we are nowhere near faults? The answer really has to do with all three.

    I also found that the number of people affected in all three countries varied greatly on different years. The reason for this is that some years had many more quakes. Also a there are powerful ones and not so powerful ones. This graph has a lot to do with sociological outcomes and how people will have to deal with earthquakes. Sociology is examined by the population and the large number of people that were affected.

    I enjoyed this extra credit assignment because on the Gapminder graphs. Not only are they addicting to watch and look at, they fun to make too. Also this assignment showed me what I needed to do to make a respectable graph. This was a little time consuming, but in the end it should be worth it.


    http://www.gapminder.org/world/#$majorMode=chart$is;shi=t;ly=2003;lb=f;il=t;fs=11;al=30;stl=t;st=t;nsl=t;se=t$wst;tts=C$ts;sp=5.59290322580644;ti=2008$zpv;v=0$inc_x;mmid=XCOORDS;iid=phAwcNAVuyj0XOoBL%5Fn5tAQ;by=ind$inc_y;mmid=YCOORDS;iid=rG%5FBjsDwyS2n7DANNH3i5vQ;by=ind$inc_s;uniValue=8.21;iid=phAwcNAVuyj0XOoBL%5Fn5tAQ;by=ind$inc_c;uniValue=255;gid=CATID0;by=grp$map_x;scale=lin;dataMin=0;dataMax=1458024460$map_y;scale=log;dataMin=0;dataMax=47174203$map_s;sma=37;smi=2.65$cd;bd=0$inds=i44_t001970,,,,;i101_t001970,,,,;i239_t001970,,,,

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  34. here is my link: www.bit.ly/cWXttx (wasn't too sure on how to use the imagure?

    1) In my Gapminder assignment I decided to test employment rate of women fifteen years and above and the employment rate of men fifteen years and above in the United States and China.
    2) I chose to compare employment rates between men and women in the United States and China because I wanted to see, as times are changing and women are being more accepted in the work force, if there was a significant change in the amount of women employed now compared to men, compared to in the early nineties. I also wanted to see if the United States has made as much progress as they like to say they have.
    3) The graph shows that there is a rise in the percent of women in the work force compared to men, but women are still not equal to that of men. In 1992 women were employed 53% compared to the 69% of men in the United States. And in China women were employed 71% to that of 81% men, almost equal. In 2007, the percentage of women in the United States in the work force has only gone up three percent while the percent of men stayed the same. In China both dropped women 69% and men 77%. Although China’s percentages have decreased they are still at a more equal level than the United States.
    4) It is interesting that China is more equal when it comes to men and women in the work fore and yet the United States is supposed to be the Country of equal rights to all, and yet women in the work force are still not even close to the amount of men working.
    5) The sociological concept that is relevant are the theory of the glass ceiling. Although women are considered to be equal there is always a barrier in certain jobs and circumstances where they will not be hired or be able to advance due to the fat that they are women.

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  35. Replacement Assignment #2: Gapminder graph
    Christa Himmelein
    Soc 101

    Explanation of the Image:

    1)The percentage of unemployed 25-54 year olds is what is displayed on the y-axis. On the x-axis is the hours worked per week as well as time.
    2) I wanted to see if a decrease in the number of people employed would cause those that are working to work more hours.
    3) The graph does not display what I’d expected. The US unemployment rate varies by 6% over the course of 24 years but the hours per week worked remains between 34 and 36 hours per week. Japan and France however have a wider range of hours worked per week (France: 29-35 & Japan 35- 40). Japan’s number of hours worked has decreased significantly too while the number of workers has decreased. I’d expected these two variables to be inversely proportional

    Interesting about what is found in graph:

    1) As mentioned, I expected to see more of a variety of hours worked in the US and I did not expect Japan’s results to be inversely proportional. Maybe this is due to the increase in technology of the course of these 24 years. However, jobs like telemarketing have been replaced in the US too. My other guess would be urbanization. Maybe cities have grown in population but not at the same rate in available jobs.

    Image:http://imgur.com/OrtSv.jpg

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  36. Travis Hicks
    Soc 101
    Gapminder Graph


    In my graph, I choose to do cell phones (per 100 people) and Internet Users (per 100 people). I chose these too variables because they show direct correlation between one and other. If the country has a high level of cell phones, then they have a high number of people with access to the internet. On the other side of things the less developed countries do not have cell phones, so they don’t have access to the internet.
    Cultural lag “refers to the notion that culture takes time to catch up with technological innovations, and that social problems and conflicts are caused by this lag”(Wikipedia). In the countries that don’t have cell phones like India, they also have very little access to the internet. The countries that do have a lot of cell phones like Sweden, went ahead and upgraded to the internet. The United Arab Emirates are an exception to this by they have the highest number of cell phones per 100 people, but are in middle of the pack with internet connections.

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  37. My Gap minder graph shows the relationship between all the countries and the literacy rate of females ages 15 and older. What I found really interesting about the graph is how in 2000 there were about ten countries that were under 50 percent of the women from those countries were illiterate, also there were a few countries where only twenty percent of their females were literate. After only 7 years all of the countries recorded to be above at least 33 percent, and only 6 of the ten countries were below 50 percent illiterate rates. I choose these variables for the graph because I wanted to see the progress that females education and literacy rates had made in the past to now.
    Another aspect of the graph I found to be interesting was that all of the countries above 90 percent female literacy rates were all developed, industrial countries, many of the countries that the female population wasn’t as literate were less developed countries, proving that education and money go hand in hand.
    All of the America countries above ninety percent, close to 100 percent literacy rate with the exception of one country; Guatemala.
    I choose these variables for the graph because I wanted to see the progress that females education and literacy rates had made in the past to now.
    From a sociological standpoint I think the graph is a good example of stratification among the countries and how different cultures. I can’t imagine being a part of a country where over sixty percent of the females are illiterate.

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  38. The graph that I had chose compare the United States and India with the age that women would first get married and what year it was. I chose these variables because I thought it would be interesting to see how times have changed with marrying off women. The graph shows that as the year’s progress the age of when women get married increases in both the United States and India.
    This is relevant to sociology because it shows the inequality of women. It’s interesting to see how women marry later and later as the years pass. This is simply because women are moving up in the world and are putting their careers first before wanting to marry and have a family. This is especially relevant to the United States as seeing in the past presidential election how the first woman (Hillary Clinton) was seriously considered as a running candidate in the election. As the years pass gender and race don’t really matter because people are finally seeing other people for who they are not by what color or gender they are.
    http://www.gapminder.org/world/#$majorMode=chart$is;shi=t;ly=2003;lb=f;il=t;fs=11;al=30;stl=t;st=t;nsl=t;se=t$wst;tts=C$ts;sp=5.59290322580644;ti=2005$zpv;v=0$inc_x;mmid=XCOORDS;iid=ti;by=ind$inc_y;mmid=YCOORDS;iid=t4eF8H%5Fjq%5FxyKCUHAX6VT1g;by=ind$inc_s;uniValue=8.21;iid=phAwcNAVuyj0XOoBL%5Fn5tAQ;by=ind$inc_c;uniValue=255;gid=CATID0;by=grp$map_x;scale=lin;dataMin=1616;dataMax=2005$map_y;scale=lin;dataMin=13;dataMax=34$map_s;sma=50;smi=2$cd;bd=0$inds=i239_t001780,,,,;i101_t001815,,,,

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  39. Sara DuBois
    www.bit.ly/bNc1Q8

    a) My variables measure the amount of income per person and the relation between those members who are living with HIV.
    b) I chose these variables because I was interested to see what the outcomes would be between those who are financially stable and also with those who are struggling with HIV.
    c) The graph shows that the number of persons living with HIV increase with the more income one has.
    d.) What is interesting about my graph is the fact that you wouldn’t believe that the more income you have the more likely you are to have HIV. You would assume those wealthier are more fortunate and also have more opportunities to learn the different ways to can receive HIV. However this could be due to the fact that the more money you have the more likely you are to buy drugs or have higher status to receive sex from anonymous partners.
    e.) The sociological concepts that are relevant would be with the sociological imagination and how these people that are getting HIV are higher status people that are suffering from a disease. However these might have gotten this disease from such taboo things. This shows the way in which social behavior can change despite the route that you would assume it to go.

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  40. http://imgur.com/r3ZFQ.png


    John Brubach
    john.brubach21@gmail.com
    March. 10, 2010
    Sociology 101


    Social Imagination: Gapminder Assignment


    There are many different situations and concepts that can deal with global inequality and change. At first, I had no idea what I wanted to incorporate into my Gapminder graph, but after doing some research and reading a few things I decided what to show in this graph. I believe it includes both global inequality and a specific type of change that we hear about constantly.
    The variables that make up my graph are Cumulative CO2 Emissions on the x-axis and the Inequality Index on the y-axis. The amount of CO2 emissions is constantly being talked about throughout the news and other media as well as the measures everyone should be taking to make changes in their countries to help the situation. The inequality Index is used as a measure of inequality of income and wealth. The graph shows many different countries at different points.
    I chose these variables because of the relevance to our country today. Global Warming and Greenhouse gases are continuously being discussed and that we need to take measures to change that. After reading a small excerpt, written by Michael Redclift and Collin Sage, about how countries that do not produce as much CO2 emissions have trouble putting money into making changes in their countries, especially if they may not have as much money as other countries that produce more emissions. I found this interesting and decided to create my graph on this concept. The graph does go on to show that these countries that do not produce as much CO2 emissions generally fall higher on the Inequality Index. These countries include Paraguay, Swaziland, Bolivia, and many more countries that usually fall into South America or Africa.
    I found this interesting and completely understandable that these countries have trouble putting money towards something like this when they are not the source of the problem as well as may not even be able to spend money on such things. I, myself, can relate to situations like this on a personal level. Things that I have not caused or was not even a witness to something happening, are hard for me to put forth time and money to fix.

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  41. Donell Cooper
    1) It measures the math achievements from the fourth grade and the population by age of males 10 yrs-14 yrs of age
    2) I chose the variables because I always was curious on how the those type of things calculated and what kids were getting education.
    3) The graph showed different countries and the amount of young males receiving educations. The countries that I viewed were, United States, Netherlands, And Paris.
    4) What was interesting was the amount of kids at the age of 10-14 yrs of age how they respond to adversity and the different types of educations they receive.

    http://www.gapminder.org/world/#$majorMode=chart$is;shi=t;ly=2003;lb=f;il=t;fs=11;al=30;stl=t;st=t;nsl=t;se=t$wst;tts=C$ts;sp=5.59290322580644;ti=2005$zpv;v=0$inc_x;mmid=XCOORDS;iid=rmQZ%5FH88rIhF3315QBZpcIQ;by=ind$inc_y;mmid=YCOORDS;iid=phAwcNAVuyj3Iw3kqbjJTZQ;by=ind$inc_s;uniValue=8.21;iid=phAwcNAVuyj0XOoBL%5Fn5tAQ;by=ind$inc_c;uniValue=255;gid=CATID0;by=grp$map_x;scale=lin;dataMin=3.251;dataMax=18$map_y;scale=lin;dataMin=224;dataMax=607$map_s;sma=49;smi=2.65$cd;bd=0$inds=

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  42. 1. I am measuring population of females ages 20-39 and breast cancer death per 100,000 women.
    2. I chose those variables because I was interested to see what the graph would look like.
    3. The graph show the population of females ages 20-39 and the number of breast cancer death per 100,000.
    4. I was surprised how many women die from breast cancer in China.


    http://www.gapminder.org/world/#$majorMode=chart$is;shi=t;ly=2003;lb=f;il=t;fs=11;al=30;stl=t;st=t;nsl=t;se=t$wst;tts=C$ts;sp=5.59290322580644;ti=2002$zpv;v=0$inc_x;mmid=XCOORDS;iid=phAwcNAVuyj3wJUwXXDdiGg;by=ind$inc_y;mmid=YCOORDS;iid=rWpQHQIQdntj6BEK8OIuWYw;by=ind$inc_s;uniValue=8.21;iid=phAwcNAVuyj0XOoBL%5Fn5tAQ;by=ind$inc_c;uniValue=255;gid=CATID0;by=grp$map_x;scale=lin;dataMin=0.06;dataMax=43$map_y;scale=lin;dataMin=0;dataMax=46$map_s;sma=49;smi=2.65$cd;bd=0$inds=

    ReplyDelete