TABLE 1.6 ANNUAL CARBON DIOXIDE EMISSIONS IN 2010 (METRIC TON S PER PERSON) to C
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TABLE 1.6 ANNUAL CARBON DIOXIDE EMISSIONS IN 2010 (METRIC TON S PER PERSON) to COUNTRY Indonesia c0 1.8032 7.6765 3.7034 COUNTRY Poland Russia South Africa COUNTRY 3.3315 4.4710 0.3716 2.1503 14.6261 6·1949 1.6295 LO 8.3086 12.2255 9.2041 5.8535 0.3109 0.1522 4.4469 4.1310 Algeria Argentina desh Brazil Canada China Colombia Congo Egypt Et rance Germany India Sudan Tanzania Thailand Korea, South Mexico Morocco Myanmar Nigeria Pakistan 9.1857 0.3038 11.4869 3.7636 1.5994 0.1732 0.4941 2.6228 0.0746 5.5554 9.1148 1.6662 Uganda Ukraine United Kingdom United States Vietnam 6.6449 7.9251 17.5642 1.7281 0.8731 tions continued on St. Paul until 1984, but despite a reduction in harvest, the population of fur seals has continued to decline. Possible reasons include climate shifts in the North Pacific, changes in the availability of prey, and new or increased interaction with com mercial fisheries that increase mortality. Here are data on the estimated number of fur seal pups born on St Paul Island (in thousands) from 1979 to 2012, where a dash indicates a year in which no data were collected. .36 Carbon dioxide emissions. Burning fuels in pow er plants and motor vehicles emits carbon dioxide (CO,), which contributes to global warming. Table 6 displays the 2010 CO, emissions per person from countries with populations of at least 30 million in that year.26 lCO2EMISS (a) Why do you think we choose to measure emis- sions per person rather than total CO, emissions for each country? (b) Make a stemplot to display the data of Table 1.6 The data will first need to be rounded (see page 30) What units are you going to use for the stems? The leaves? You should round the data to the units you are planning to use for the leaves before drawing the PUPS BORN PUPS BORN (THOUSANDS) 170.12 YEAR (THOUSANDS) mplot. Describe the shape, center, and variability of the distribution. Which countries are outliers? 245.93 203.82 YEAR 1996 1997 1980Explanation / Answer
A stem plot is a way of representing data in a table such that each number is divided into a stem and a leaf. All numbers with the same stem are grouped together.
Here, there are four significant digits after the decimal point. It would be better if the numbers are rounded for a better visualization of the data. The emission will have to be rounded to one digit after the decimal point. The number before the decimal point will be the stem while the rounded digit after the decimal point will be the leaf.
The stems of the plot will be in metric tons per person, while the leaves will be in 0.1 metric tons per person. For example, if stem reads 2 and the leaf reads 3, then it should be read as 2.3 metric tons per person.
Here is the same data as above rounded to one decimal place and sorted for the stem plot:
The stem and leaf plot will look like this:
The shape of the distribution will be right-tailed.
The median of the data is 3.7 metric tons per person. This is a measure of the center of the data.
The variability is given by the inter quartile range. For calculating that, we need the Q1 the first quartile and Q3 the third quartile. From the data, we get Q1 = 0.9 and Q3 = 7.2 metric tons per person. So, IQR = Q3 - Q1 = 7.2 - 0.9 = 6.3. So, this is the measure of the variability of the data.
It is obvious from the plot that there are no outliers at the low end.
To find outliers at the high end, we can calculate Q3 + 1.5 * IQR = 7.2 + 1.5 * 6.3 = 16.55. So, any data point greater than 16.55 will be treated as an outlier. Here, United States with the value of 17.6 is an outlier.
Country CO2 emission CO2 emission (rounded) Ethiopia 0.0746 0.1 Uganda 0.1113 0.1 Tanzania 0.1522 0.2 Myanmar 0.1732 0.2 Kenya 0.3038 0.3 Sudan 0.3109 0.3 Bangladesh 0.3716 0.4 Congo 0.4932 0.5 Nigeria 0.4941 0.5 Philippines 0.8731 0.9 Pakistan 0.9321 0.9 Morocco 1.5994 1.6 Columbia 1.6295 1.6 India 1.6662 1.7 Vietnam 1.7281 1.7 Indonesia 1.8032 1.8 Brazil 2.1503 2.2 Egypt 2.6228 2.6 Algeria 3.3315 3.3 Iraq 3.7034 3.7 Mexico 3.7636 3.8 Turkey 4.131 4.1 Thailand 4.4469 4.4 Argentina 4.471 4.5 France 5.5554 5.6 Spain 5.8535 5.9 China 6.1949 6.2 Ukraine 6.6449 6.6 Italy 6.7177 6.7 Iran 7.6765 7.7 United Kingdom 7.9251 7.9 Poland 8.3086 8.3 Germany 9.1148 9.1 Japan 9.1857 9.2 South Africa 9.2041 9.2 South Korea 11.4869 11.5 Russia 12.2255 12.2 Canada 14.6261 14.6 United States 17.5642 17.6Related Questions
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