This study focuses on the analysis and forecasting of monthly liquor sales in the United States over the period 1970-1980. The data represents sales in millions of dollars, forming a time series of 132 monthly observations. This study employs a comprehensive time series analysis approach, implementing two main families of forecasting models: SARIMA (Seasonal Autoregressive Integrated Moving Average) models Exponential Smoothing methods The analysis follows a structured process including exploratory data analysis, model identification, parameter estimation, diagnostic checking, and forecasting.

Context of the study: Ordinal logistic regression is an extension of binary logistic regression that allows the analysis of ordered categorical dependent variables with more than two levels. In this study, we apply it to analyze the environmental perception of electric vehicles, a crucial topic in the context of ecological transition.