Week 7 Discussion Response Support

Regression Analysis

Agarwal and Saxena (2011) conducted a study in Gagan River, India, that seek to determine the level of pollution contributed by different domestic and industrial activities. The researchers collected samples of water from Gagan River at two different sites and tested the samples for chemical oxygen demand, biological oxygen demand as well as alkalinity during rainy, winter and summer season. Regression analysis helped in predicting the level of contamination of this river. In this study, the researchers established a regressing equation between two parameters and utilized it to forecast the value of the unknown parameter using the value of the known parameter. Regression analysis acts as a tool for finding the value of physicochemical parameters as well as the extent of pollution theoretically. The case of careless disposal of industrial effluent into rivers has become rampant particularly in the urban areas with the development of the industries. The consequence of the poor quality of river water is a threat to animals and humans who may drink untreated water.

Agarwal and Saxena (2011) found out a correlation between physicochemical parameters and the extent of water pollution in river Gagan. The resulting regression equation can be used to predict the level of contamination of water in Ghana River by different parameters. An analysis using the regression analysis is time-saving and cost efficient. Statistical equations employed by the researchers to calculate the value of physicochemical parameters in addition to measuring the extent of pollution in Gagan River is significant to raise the alarm concerning water pollution. Policymakers and relevant authorities can use this information to set up preventive measures and perform detailed investigations that will lead to proper control of contamination at river Gagan.

Reference

Agarwal, A., & Saxena, M. (2011). Assessment of Pollution by Physicochemical-Water- Parameters; Using Regression-Analysis: A Case-Study of Gagan-River at Moradabad-India.  Advances in Applied Science Research, 2(2), 185-189.

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Very interesting example! Do you have some numeric results that you can share with us? For example, R-square, p-value for the correlation (p-value located on ANOVA table is the same one), coefficients estimates, standard error, or ANOVA table for regression.

The example that you mentioned is very interesting, but it will be very convenient to take a look also at the goodness of fit statistics.