Previsão de demanda de veículos automotores através do modelo de regressão linear múltipla

This study aimed to model a equation for the demand of automobiles and light commercial vehicles, based on the data from February 2007 to July 2014, through a multiple regression analysis. The literature review consists of an information collection of the history of automotive industry, and it has contributed to the understanding of the current crisis that affects this market, which consequence was a large reduction in sales. The model developed was evaluated by a residual analysis and also was used an adhesion test - F test - with a significance level of 5%. In addition, a coefficient of determination (R2) of 0.8159 was determined, indicating that 81.59% of the demand for automobiles and light commercial vehicles can be explained by the regression variables: interest rate, unemployment rate, broad consumer price index (CPI), gross domestic product (GDP) and tax on industrialized products (IPI). Finally, other ten samples, from August 2014 to May 2015, were tested in the model in order to validate its forecasting quality. Finally, a Monte Carlo Simulation was run in order to obtain a distribution of probabilities of future demands. It was observed that the actual demand in the period after the sample was in the range that was most likely to occur, and that the GDP and the CPI are the variable that have the greatest influence on the developed model

Modelagem auto-regressiva na previsão de demanda dos produtos de uma empresa do setor químico

Considering the high competitiveness in the industrial chemical sector, demand forecast is a relevant factor for decision-making. There is a need for tools capable of assisting in the analysis and definition of the forecast. In that sense, the objective is to generate the chemical industry forecast using an advanced forecasting model and thus verify the accuracy of the method. Because it is time series with seasonality, the model of seasonal autoregressive integrated moving average - SARIMA generated reliable forecasts and acceding to the problem analyzed, thus enabling, through validation with real data improvements in the management and decision making of supply chain

Previsão de demanda de veículos automotores através do modelo de regressão linear múltipla

This study aimed to model a equation for the demand of automobiles and light commercial vehicles, based on the data from February 2007 to July 2014, through a multiple regression analysis. The literature review consists of an information collection of the history of automotive industry, and it has contributed to the understanding of the current crisis that affects this market, which consequence was a large reduction in sales. The model developed was evaluated by a residual analysis and also was used an adhesion test - F test - with a significance level of 5%. In addition, a coefficient of determination (R2) of 0.8159 was determined, indicating that 81.59% of the demand for automobiles and light commercial vehicles can be explained by the regression variables: interest rate, unemployment rate, broad consumer price index (CPI), gross domestic product (GDP) and tax on industrialized products (IPI). Finally, other ten samples, from August 2014 to May 2015, were tested in the model in order to validate its forecasting quality. Finally, a Monte Carlo Simulation was run in order to obtain a distribution of probabilities of future demands. It was observed that the actual demand in the period after the sample was in the range that was most likely to occur, and that the GDP and the CPI are the variable that have the greatest influence on the developed model

Modelagem auto-regressiva na previsão de demanda dos produtos de uma empresa do setor químico

Considering the high competitiveness in the industrial chemical sector, demand forecast is a relevant factor for decision-making. There is a need for tools capable of assisting in the analysis and definition of the forecast. In that sense, the objective is to generate the chemical industry forecast using an advanced forecasting model and thus verify the accuracy of the method. Because it is time series with seasonality, the model of seasonal autoregressive integrated moving average - SARIMA generated reliable forecasts and acceding to the problem analyzed, thus enabling, through validation with real data improvements in the management and decision making of supply chain

Perfil da demanda por microcomputadores no Brasil: o que os microdados da POF-IBGE indicam?

Neste estudo, analisou-se a relação entre a despesa domiciliar com a compra de computadores e as características demográficas e socioeconômicas dos domicílios brasileiros. Foram utilizados os microdados de duas Pesquisas de Orçamentos Familiares (POF), elaboradas pelo Instituto Brasileiro de Geografia e Estatística (IBGE): 2002-2003 e 2008-2009. Essas bases permitiram que se utilizasse a despesa total per capita como variável definidora do poder aquisitivo do domicílio. Foi adotada uma abordagem econométrica para a natureza desse tipo de análise, isto é, o modelo de seleção de Heckman, que envolve dois estágios. No primeiro, analisaram-se os fatores associados à probabilidade de ocorrência da despesa e, no segundo, foram avaliados os fatores associados aos valores da despesa efetuada. Os principais resultados indicaram que o perfil do chefe (gênero e idade) e a composição dos domicílios e escolaridade dos moradores são fatores relevantes tanto para a decisão de gastar quanto para a decisão sobre o valor a ser gasto. A redução da elasticidade que relaciona as despesas com computador ao poder aquisitivo do domicílio (em 2002-2003 foi 0,56763, enquanto em 2008-2009 caiu para 0,41546) pode ser explicada pela queda no preço dos computadores e pelo aumento do poder de compra das famílias.

Nursing Activities Score e demanda de trabalho de enfermagem em terapia intensiva

OBJETIVO: Medir e caracterizar a carga de trabalho de enfermagem em Unidade de Terapia Intensiva (UTI) por meio da aplicação do Nursing Activities Score (NAS). MÉTODOS: Estudo descritivo quantitativo, retrospectivo, realizado em uma das UTIs de um Hospital Filantrópico de Teresina- PI, de setembro a outubro de 2010, com amostra de 66 pacientes. Foram realizadas 285 medidas do escore NAS. RESULTADOS: Quanto à carga de trabalho de enfermagem, foi verificada uma média do escore total do NAS de 68,1% (51,5% e 108,3%), correspondendo à porcentagem de tempo gasto pelo profissional de enfermagem na assistência direta ao paciente nas 24 horas. Houve correlação estatística entre NAS e desfecho clínico (p= 0,001). Já entre NAS e tempo de internação (p= 0,073) e NAS e idade (p=0,952), não houve significância estatística. CONCLUSÃO: Os resultados mostraram que os pacientes apresentaram elevada necessidade de cuidados, refletida pela média elevada do NAS.

Análisis de los errores léxicos en la producción escrita de alemán L3 por alumnos de Escuela Oficial de Idiomas

Programa de Doctorado: Didáctica de la Lengua y la Literatura