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Background: Fitting a multiple regression model is always challenging and the level of difficulty varies according to the purpose for which it is fitted. Two major difficulties that arise while fitting a multiple regression model for forecasting are selecting 'potential predictors' from numerous possible variables to influence on the forecast variable and investigating the most appropriate model with a subset of the potential predictors.Objective: Purpose of this paper is to demonstrate a procedure adopted while fitting multiple regression model (with an attempt to optimize) for rice production forecasting in Nepal.Materials and Methods: This study has used fifty years (1961-2010) of time series data. A list of twenty-one predictors thought to impact on rice production was scanned based upon past literature, expert's hunches, availability of the data and the researcher's insight which left eleven possible predictors. Further, these possible predictors were subjected to family of automated stepwise methods which left five ‘potential predictors’ namely harvested area, rural population, farm harvest price, male agricultural labor force and, female agricultural labor force. Afterwards, best subset regression was performed in Minitab Version 16 which finally left three 'appropriate predictors' that best fit the model namely harvested area, rural population and farm harvest price.Results: The model fit was significant with p < .001. Also, all the three predictors were found highly significant with p < 0.001. The model was parsimonious which explained 93% variation in rice production with 54% overlapping predictive work done. Forecast error was less than 5%.Conclusion: Multiple regression model can be used in rice production forecasting in the country for the enhanced ease and efficiency.Nepalese Journal of Statistics, Vol. 2, 89-98

Multiple Regression Model Fitted for Rice Production Forecasting in Nepal: A Case of Time Series Data