community Trends in china Type II- Mathematical theoretical account Sam Moghadam Introduction The opera hat graph to ingestion when discussing the campaign of nation increase or decrease is a diffuse plot because it helps to show the correlation coefficiental statistics between the changes of being over a period of time. The deuce variables used in this scatter plot are the cosmos in millions and years. With a scatter plot, it is helpful in draft copy a line of vanquish fit to determine a one-dimensional correlation and to further predict future trends. A scatter plot move also show us the trend in change of Chinese population versus the population changes in the rest of the world and/or other countries. done and through looking at the scatter plot, we can determine the Chinese population increase, relative to the world population increase end-to-end the 1950s till 1995 and onward to the present day and in the decades to come. Studying the data on the s catter plot, we can wile a consistent increase of Chinese population as percentage of the world population in the last two decades. The linear regression model is y=16.26x-31190. Implicating this line in the scatter plot, we can see it has a direct correlation with the actual data shown on the graph resulting in a line of best fit. people of China from 1950-1995 (#1) Year| existence in Millions| 1950| 554.8| 1955| 609| 1960| 657.
5| 1965| 729.2| 1970| 830.7| 1975| 927.8| 1980| 998.9| 1985| 1070| 1990| 1155.3| 1995| 1290.5| The apparent trend that is shown from this graph is a linea r regression. The graph shows that as the ye! ars pass, the Chinese population is exploitation as well. This shows an exponential growth occurring in China from 1950-1995. The best graph to use is an scatter plot because we can intelligibly see the exponential growth, efficiently and quickly. Year| Population in Millions| 1950| 554.8| 1955| 609| 1960| 657.5| 1965| 729.2| 1970| 830.7| 1975| 927.8| 1980| 998.9|...If you want to get a full essay, point it on our website: OrderCustomPaper.com
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