APPLICATION OF STEPWISE MULTIPLE REGRESSION TO SUPERSATURATED DESIGNS DATA OF WATER POLLUTION IN SAUDI ARABIA

Background. In data analysis issues, stepwise multiple linear regression is seen to be a very helpful computational method. Multiple linear regression (MLR), logistic regression, ordinal regression and multinominal regression are all examples of regressions. Multiple linear regressions are a type of linear regression analysis that is used to examine the relationship between a single response variable (the dependent variable) and two or more controlled variables (the independent variables). Objective. This research aims to comprehensively investigate and address the multifaceted issue of water pollution. Methods and materials. Data were obtained using an online questionnaire form, where the questionnaire was sent to the target group. There were four general steps to build questionnaire model in MLR. The general steps were supersaturated designs, checking assumptions, choosing appropriate multiple variables analysis, and interpreting the output. Each model was analyzed separately using the chosen analysis methods using a program Statistical Package for the Social Sciences 20. Results. The stepwise regression method for all applications reveals that five controlled variables were chosen using SPSS 20: , , , and . Conclusion. (increase in population), (inorganic materials such as (copper, mercury, etc.), (rainwater), (waste chemicals) and (waste of living organisms) are the actual causes of water pollution.