BigLucyT0T1: Some Business Population Database for two periods of time
In samplesize4surveys: Sample Size Calculations for Complex Surveys

Description Usage Format Author(s) References Examples

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.

1	BigLucyT0T1

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.

Hugo Andres Gutierrez Rojas hugogutierrez@usantotomas.edu.co

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

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))