This data set corresponds to some financial variables of 85396 industrial companies of a city in a particular fiscal year.
The identifier of the company. It correspond to an alphanumeric sequence (two letters and three digits)
The address of the principal office of the company in the city
The industrial companies are discrimitnated according to the Taxes declared. There are small, medium and big companies
The country is divided by counties. A company belongs to a particular zone according to its cartographic location.
The total ammount of a company's earnings (or profit) in the previuos fiscal year. It is calculated by taking revenues and adjusting for the cost of doing business
The total number of persons working for the company in the previuos fiscal year
The total ammount of a company's income Tax
Indicates if the company uses the Internet and WEBmail options in order to make self-propaganda.
Indicates if the company is certified by the International Organization for Standardization.
The age of the company.
Cartographic segments by county. A segment comprises in average 10 companies located close to each other.
Hugo Andres Gutierrez Rojas email@example.com
Gutierrez, H. A. (2009), Estrategias de muestreo: Diseno de encuestas y estimacion de parametros. Editorial Universidad Santo Tomas.
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data(BigLucy) attach(BigLucy) # The variables of interest are: Income, Employees and Taxes # This information is stored in a data frame called estima estima <- data.frame(Income, Employees, Taxes) # The population totals colSums(estima) # Some parameters of interest table(SPAM,Level) xtabs(Income ~ Level+SPAM) # Correlations among characteristics of interest cor(estima) # Some useful histograms hist(Income) hist(Taxes) hist(Employees) # Some useful plots boxplot(Income ~ Level) barplot(table(Level)) pie(table(SPAM))
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