In developing countries, child labor has become a significant problem with adverse effects in the present and future for society and individuals. There are many causes that obligate children to abandon school and start working. Economic, social, familiar, and personal problems can expel children from school, inhibiting them from living appropriately. Polls like the ENAHO in Peru tried to recollect relevant data as much as possible to explain this problem. With many variables, it is necessary to have a methodology to build an algorithm with enough explanatory power to explain the situation. Therefore, this research elaborated an algorithm through Lasso to proportionate a statistical explanation of child labor. Due to the type of data, the regression was logistic.

In this paper, an effort has been put to develop a model for estimating growth based on logit re-gression (logit) and implemented the model to Italian manufacturing companies. Our data set consists of 8232 SMEs of Italy. To estimate the growth of the firm an innovative approach that considers annual statements issued the year before the accelerated growth has been considered as the effective estimators of firm growth. The result of the logit showed that return on asset, log (cash flow) and log (Inventory) positively affect in estimating the growth of the high growth firm whereas working capital turnover times negatively affects in estimating the growth of the firm. The discriminant power of the model using Receiver Operating Characteristics curve shows 72.35%, which means the model is fair in terms of estimating the growth.

The Central Road is the most connected road between the capital and cities of the jungle and the highlands in Peru. Nowadays, this road suffers from intense traffic jams that excessively delay time travel. Moreover, it is vulnerable to closures due to natural and human factors. Also, there is a proliferation of reckless drivers who do not care about speeding and cause terrible accidents. Hence, the current research explores whether the speeding record affected the occurrence of accidents on this road. After applying the Logit regression, it was possible to find that only recording speeding beyond 30 kilometers per hour over the limit was relevant to prevent accidents. It was also suggested that the speedometer should track 24 hours a day along with measures to improve the infrastructure of this road.

Road accidents are an essential issue for every country in the world because their consequences are devastating for the people involved in all senses. Speeding has been considered one of the main reasons for these tragedies. Therefore, the current research analyzed the relationship between speeding and severe accidents on the Pan-American highway in Peru. Publicly available data kept in a Peruvian official agency was employed. Methodologically, the logit regression was harnessed since the data were categorical. After the analysis, the research found that it was riskier to have severe accidents than speeding in urban areas crossing the highway and driving or being inside a truck.