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The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. However, this model performs well under strict assumptions such as the number of observations, the linearity of variables, multicollinearity, homoskedasticity, reliability of measurement, and normality. Besides, there is no consideration to date for handling and cleansing inconsistent samples in the data sets. These samples may significantly influence the performance of multiple linear regression in terms of these assumptions and several aspects, such as adjusted R-square, intercept-slopes, exogenous variables, and the accuracy of prediction. In this paper, the data reduction strategy of rough sets was employed to remove and clean these types of samples, boosting the performance of the linear regression models. This strategy was evaluated by examining three different effects; adjusted R-square, slopes-intercepts, and mean square error of the regression model. Simulated data and simple modeling problems were used to determine the effects of these three aspects. The secondary data sets were collected from various domains to examine the proposed rough-regression model. The simulation results showed that the data reduction strategy is exceedingly effective to boost the performance of the multiple linear regression in the three aspects above. In the implementation, these aspects also performed better than before data reduction. The results from both simulations and implementations demonstrate that the data reduction of rough sets is a viable strategy in cleansing of the inconsistent samples in the linear regression models. Thus, the proposed rough regression model is effectively proven to support the data analysis of surveys or cross-sectional studies, especially when the stated aspects are not well fulfilled. Therefore, the surveys are not needed to be repeated and reconsidered by researchers.

The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. However, this model performs well under strict assumptions such as the number of observations, the linearity of variables, multicollinearity, homoskedasticity, reliability of measurement, and normality. Besides, there is no consideration to date for handling and cleansing inconsistent samples in the data sets. These samples may significantly influence the performance of multiple linear regression in terms of these assumptions and several aspects, such as adjusted R-square, intercept-slopes, exogenous variables, and the accuracy of prediction. In this paper, the data reduction strategy of rough sets was employed to remove and clean these types of samples, boosting the performance of the linear regression models. This strategy was evaluated by examining three different effects; adjusted R-square, slopes-intercepts, and mean square error of the regression model. Simulated data and simple modeling problems were used to determine the effects of these three aspects. The secondary data sets were collected from various domains to examine the proposed rough-regression model. The simulation results showed that the data reduction strategy is exceedingly effective to boost the performance of the multiple linear regression in the three aspects above. In the implementation, these aspects also performed better than before data reduction. The results from both simulations and implementations demonstrate that the data reduction of rough sets is a viable strategy in cleansing of the inconsistent samples in the linear regression models. Thus, the proposed rough regression model is effectively proven to support the data analysis of surveys or cross-sectional studies, especially when the stated aspects are not well fulfilled. Therefore, the surveys are not needed to be repeated and reconsidered by researchers.

The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. However, this model performs well under strict assumptions such as the number of observations, the linearity of variables, multicollinearity, homoskedasticity, reliability of measurement, and normality. Besides, there is no consideration to date for handling and cleansing inconsistent samples in the data sets. These samples may significantly influence the performance of multiple linear regression in terms of these assumptions and several aspects, such as adjusted R-square, intercept-slopes, exogenous variables, and the accuracy of prediction. In this paper, the data reduction strategy of rough sets was employed to remove and clean these types of samples, boosting the performance of the linear regression models. This strategy was evaluated by examining three different effects; adjusted R-square, slopes-intercepts, and mean square error of the regression model. Simulated data and simple modeling problems were used to determine the effects of these three aspects. The secondary data sets were collected from various domains to examine the proposed rough-regression model. The simulation results showed that the data reduction strategy is exceedingly effective to boost the performance of the multiple linear regression in the three aspects above. In the implementation, these aspects also performed better than before data reduction. The results from both simulations and implementations demonstrate that the data reduction of rough sets is a viable strategy in cleansing of the inconsistent samples in the linear regression models. Thus, the proposed rough regression model is effectively proven to support the data analysis of surveys or cross-sectional studies, especially when the stated aspects are not well fulfilled. Therefore, the surveys are not needed to be repeated and reconsidered by researchers.

The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. However, this model performs well under strict assumptions such as the number of observations, the linearity of variables, multicollinearity, homoskedasticity, reliability of measurement, and normality. Besides, there is no consideration to date for handling and cleansing inconsistent samples in the data sets. These samples may significantly influence the performance of multiple linear regression in terms of these assumptions and several aspects, such as adjusted R-square, intercept-slopes, exogenous variables, and the accuracy of prediction. In this paper, the data reduction strategy of rough sets was employed to remove and clean these types of samples, boosting the performance of the linear regression models. This strategy was evaluated by examining three different effects; adjusted R-square, slopes-intercepts, and mean square error of the regression model. Simulated data and simple modeling problems were used to determine the effects of these three aspects. The secondary data sets were collected from various domains to examine the proposed rough-regression model. The simulation results showed that the data reduction strategy is exceedingly effective to boost the performance of the multiple linear regression in the three aspects above. In the implementation, these aspects also performed better than before data reduction. The results from both simulations and implementations demonstrate that the data reduction of rough sets is a viable strategy in cleansing of the inconsistent samples in the linear regression models. Thus, the proposed rough regression model is effectively proven to support the data analysis of surveys or cross-sectional studies, especially when the stated aspects are not well fulfilled. Therefore, the surveys are not needed to be repeated and reconsidered by researchers.

The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. However, this model performs well under strict assumptions such as the number of observations, the linearity of variables, multicollinearity, homoskedasticity, reliability of measurement, and normality. Besides, there is no consideration to date for handling and cleansing inconsistent samples in the data sets. These samples may significantly influence the performance of multiple linear regression in terms of these assumptions and several aspects, such as adjusted R-square, intercept-slopes, exogenous variables, and the accuracy of prediction. In this paper, the data reduction strategy of rough sets was employed to remove and clean these types of samples, boosting the performance of the linear regression models. This strategy was evaluated by examining three different effects; adjusted R-square, slopes-intercepts, and mean square error of the regression model. Simulated data and simple modeling problems were used to determine the effects of these three aspects. The secondary data sets were collected from various domains to examine the proposed rough-regression model. The simulation results showed that the data reduction strategy is exceedingly effective to boost the performance of the multiple linear regression in the three aspects above. In the implementation, these aspects also performed better than before data reduction. The results from both simulations and implementations demonstrate that the data reduction of rough sets is a viable strategy in cleansing of the inconsistent samples in the linear regression models. Thus, the proposed rough regression model is effectively proven to support the data analysis of surveys or cross-sectional studies, especially when the stated aspects are not well fulfilled. Therefore, the surveys are not needed to be repeated and reconsidered by researchers.

The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. However, this model performs well under strict assumptions such as the number of observations, the linearity of variables, multicollinearity, homoskedasticity, reliability of measurement, and normality. Besides, there is no consideration to date for handling and cleansing inconsistent samples in the data sets. These samples may significantly influence the performance of multiple linear regression in terms of these assumptions and several aspects, such as adjusted R-square, intercept-slopes, exogenous variables, and the accuracy of prediction. In this paper, the data reduction strategy of rough sets was employed to remove and clean these types of samples, boosting the performance of the linear regression models. This strategy was evaluated by examining three different effects; adjusted R-square, slopes-intercepts, and mean square error of the regression model. Simulated data and simple modeling problems were used to determine the effects of these three aspects. The secondary data sets were collected from various domains to examine the proposed rough-regression model. The simulation results showed that the data reduction strategy is exceedingly effective to boost the performance of the multiple linear regression in the three aspects above. In the implementation, these aspects also performed better than before data reduction. The results from both simulations and implementations demonstrate that the data reduction of rough sets is a viable strategy in cleansing of the inconsistent samples in the linear regression models. Thus, the proposed rough regression model is effectively proven to support the data analysis of surveys or cross-sectional studies, especially when the stated aspects are not well fulfilled. Therefore, the surveys are not needed to be repeated and reconsidered by researchers.

The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. However, this model performs well under strict assumptions such as the number of observations, the linearity of variables, multicollinearity, homoskedasticity, reliability of measurement, and normality. Besides, there is no consideration to date for handling and cleansing inconsistent samples in the data sets. These samples may significantly influence the performance of multiple linear regression in terms of these assumptions and several aspects, such as adjusted R-square, intercept-slopes, exogenous variables, and the accuracy of prediction. In this paper, the data reduction strategy of rough sets was employed to remove and clean these types of samples, boosting the performance of the linear regression models. This strategy was evaluated by examining three different effects; adjusted R-square, slopes-intercepts, and mean square error of the regression model. Simulated data and simple modeling problems were used to determine the effects of these three aspects. The secondary data sets were collected from various domains to examine the proposed rough-regression model. The simulation results showed that the data reduction strategy is exceedingly effective to boost the performance of the multiple linear regression in the three aspects above. In the implementation, these aspects also performed better than before data reduction. The results from both simulations and implementations demonstrate that the data reduction of rough sets is a viable strategy in cleansing of the inconsistent samples in the linear regression models. Thus, the proposed rough regression model is effectively proven to support the data analysis of surveys or cross-sectional studies, especially when the stated aspects are not well fulfilled. Therefore, the surveys are not needed to be repeated and reconsidered by researchers.

The linear regression model is one of the most common and easiest algorithms used in machine learning for predictive analysis purposes. However, this model performs well under strict assumptions such as the number of observations, the linearity of variables, multicollinearity, homoskedasticity, reliability of measurement, and normality. Besides, there is no consideration to date for handling and cleansing inconsistent samples in the data sets. These samples may significantly influence the performance of multiple linear regression in terms of these assumptions and several aspects, such as adjusted R-square, intercept-slopes, exogenous variables, and the accuracy of prediction. In this paper, the data reduction strategy of rough sets was employed to remove and clean these types of samples, boosting the performance of the linear regression models. This strategy was evaluated by examining three different effects; adjusted R-square, slopes-intercepts, and mean square error of the regression model. Simulated data and simple modeling problems were used to determine the effects of these three aspects. The secondary data sets were collected from various domains to examine the proposed rough-regression model. The simulation results showed that the data reduction strategy is exceedingly effective to boost the performance of the multiple linear regression in the three aspects above. In the implementation, these aspects also performed better than before data reduction. The results from both simulations and implementations demonstrate that the data reduction of rough sets is a viable strategy in cleansing of the inconsistent samples in the linear regression models. Thus, the proposed rough regression model is effectively proven to support the data analysis of surveys or cross-sectional studies, especially when the stated aspects are not well fulfilled. Therefore, the surveys are not needed to be repeated and reconsidered by researchers.

Various models used in stock market forecasting presented have been classified according to the data preparation, forecasting methodology, performance evaluation, and performance measure. However, these models have not sufficiently discussed in data preparation to overcome randomness, as well as uncertainty and volatility of stock prices issues in achieving high forecasting accuracy. Therefore, the focus of this paper is the data preparation procedure of triangular fuzzy number to build an improved fuzzy random autoregression model using non-stationary stock market data for forecasting purposes. The improved forecasting model considers two types of input, which are data with low-high and single point values of stock market prices. Even though, low-high data present variability and volatility in nature, the single data has to be form in symmetry left-right spread to present variability and standard error. Then, expectations and variances, confidenceintervals of fuzzy random data are constructed for fuzzy input-output data. By using the input-output data and simplex approach, parameters of the model can be estimated. In this study, some real data sets were used to represent both types of inputs, which are the Kuala Lumpur stock exchange and Alabama University enrollment. The study found that variability and spread adjustment are important factors in data preparation to improve accuracy of the fuzzy random auto-regression model.