| Type of data: Check all that apply. Use "Other" to specify other types so that we can include them in further updates. |
text
number
series
table
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| Variable labels of dataset (the names of the variables) |
AREA|CITY|GROWTH RATE|WATER ACCESS|FOOD SUPPLY|POPULATION IN 80 90 00|NUMBER OF VEHICLES OWNED|PHONE ACCESS|ELECTRICITY ACCESS|COUNTRY |
| Outline of data |
The dataset provide information about the number of population, growth rate, food supply, utilities access and etc. of the most largest cities in the world. |
| Simulation process |
Since the data is time series and involves many variables with the one we are interested is the number of population. Ordinary least square method can be applied to analyze the dataset. The process begins with selection of dependent and independent variables and put them into a regression form. Then, the commercial package like Excel can be used to find the value of each coefficient, completing the model. Some assumptions will be raised such as all dependent variables are unbiased variables and there are no missing significant variables in the equation. The derived equation can be tested using past dataset to estimate its robustness. |
| Expected outcome of the process (obtained knowledge, analysis results, output of tools) |
The derived equation can be used to predict the growth rate of a city. If we assume the value of dependent variables, we can observe the number of population, and can see how each variable influences the outcome. Moreover, we can apply further by switching the independent variable among other dependent variables, or even introduce a new variable to the model. |
| Anticipation for analyses/simulations other than the typical ones provided above |
We anticipate that the model can be adjusted to be more precise and eventually lead to some advisable information that may prove useful for those people who make the policy. |
Last Update：December,31,2013 Created：December,31,2013
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February,13,2022
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