predetermined;
- the regression analysis can be successfully applied to determine the changes of response variable when a predictor variable
changes;
- the variables population aged 15 to 59 years and total municipal solid waste are significant factors for the analysis and strongly influences the waste generation, while number of population and
urban life expectancy are less significant;
- time series analysis is a technique which can be successfully applied for waste prognostic and in our case the S-Curve trend model is the most suitable for municipal solid waste prediction for total waste and for each waste fraction evaluated.
Since in this study we have conducted the RA considering social variables, in the following studies we will also consider economic indicators as variables which will replace the urban life expectancy variable that proved to have less influence on waste generation with gross domestic product per capita or other economic indicator. Also, models like ANN or ARIMA will be applied for municipal solid waste prediction in the next studies, in order to make a comparison of these tools and recommend the best one for decision makers.