BigLucyT0T1 {samplesize4surveys}

Some Business Population Database for two periods of time

Description

This data set corresponds to a random sample of BigLucy. It contains some financial variables of 85296 industrial companies of a city in a particular fiscal year.

Usage

BigLucyT0T1

Format

ID

The identifier of the company. It correspond to an alphanumeric sequence (two letters and three digits)

Ubication

The address of the principal office of the company in the city

Level

The industrial companies are discrimitnated according to the Taxes declared. There are small, medium and big companies

Zone

The city is divided by geoghrafical zones. A company is classified in a particular zone according to its address

Income

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

Employees

The total number of persons working for the company in the previuos fiscal year

Taxes

The total ammount of a company's income Tax

SPAM

Indicates if the company uses the Internet and WEBmail options in order to make self-propaganda.

Segments

The cartographic divisions.

Outgoing

Expenses per year.

Years

Age of the company.

ISO

Indicates whether the company is quality-certified.

ISOYears

Indicates the time company has been certified.

CountyP

Indicates wheter the county is participating in the intervention. That is if the county contains companies that have been certified by ISO

Time

Refers to the time of observation.

Author(s)

Hugo Andres Gutierrez Rojas [e-mail]

References

Gutierrez, H. A. (2009), Estrategias de muestreo: Diseno de encuestas y estimacion de parametros. Editorial Universidad Santo Tomas.

Examples

data(Lucy)
attach(Lucy)
# 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))