### Abstract

An equation modeling on Sembulan River, Sabah, Malaysia, has been undertaken using a backward stepwise multiple linear regression. A good performance has been obtained using a log transformation on water quality data designated as predictors and dependent variable. The regression model is in accordance with the ANOVA result. The temperature, biochemical oxygen demand (BOD), Echerichia Coli, Pb and nitrate were described as continuous predictors, while the river location (downstream, municipal and upstream) was designated as independent string grouping variable, and the chemical oxygen demand (COD) was set up as the dependent variable. The string grouping variable was converted to its dummy variable, which in turn led to the design of a three-equation model with respect to river location. The results show that BOD has a strong effect on COD, while Pb and nitrate show less effect on COD. The temperature gives little negative effect on COD, while other variables such as pH, salinity and Cd are excluded from the river modeling since they induce insignificant effects based on backward criterion probability of F-value = 0.100. Using the general linear model with LSD mode, it is revealed that predictor(s) show a remarkable discriminant effect between upstream and municipal/downstream on the 0.05 level. The most effect came from salinity indicated by the canonical discriminant function based on Wilks' lambda.

Original language | English |
---|---|

Pages (from-to) | 1-7 |

Number of pages | 7 |

Journal | Sains Malaysiana |

Volume | 35 |

Issue number | 2 |

Publication status | Published - Dec 2006 |

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### Keywords

- Backward stepwise multiple linear regression
- River modeling
- Water quality

### ASJC Scopus subject areas

- General

### Cite this

**Equation modeling of Sembulan River, Sabah, as a case study using backward stepwise multiple linear regression.** / Sundari, Rita; Ahmad, Musa; Lee, Yook Heng.

Research output: Contribution to journal › Article

*Sains Malaysiana*, vol. 35, no. 2, pp. 1-7.

}

TY - JOUR

T1 - Equation modeling of Sembulan River, Sabah, as a case study using backward stepwise multiple linear regression

AU - Sundari, Rita

AU - Ahmad, Musa

AU - Lee, Yook Heng

PY - 2006/12

Y1 - 2006/12

N2 - An equation modeling on Sembulan River, Sabah, Malaysia, has been undertaken using a backward stepwise multiple linear regression. A good performance has been obtained using a log transformation on water quality data designated as predictors and dependent variable. The regression model is in accordance with the ANOVA result. The temperature, biochemical oxygen demand (BOD), Echerichia Coli, Pb and nitrate were described as continuous predictors, while the river location (downstream, municipal and upstream) was designated as independent string grouping variable, and the chemical oxygen demand (COD) was set up as the dependent variable. The string grouping variable was converted to its dummy variable, which in turn led to the design of a three-equation model with respect to river location. The results show that BOD has a strong effect on COD, while Pb and nitrate show less effect on COD. The temperature gives little negative effect on COD, while other variables such as pH, salinity and Cd are excluded from the river modeling since they induce insignificant effects based on backward criterion probability of F-value = 0.100. Using the general linear model with LSD mode, it is revealed that predictor(s) show a remarkable discriminant effect between upstream and municipal/downstream on the 0.05 level. The most effect came from salinity indicated by the canonical discriminant function based on Wilks' lambda.

AB - An equation modeling on Sembulan River, Sabah, Malaysia, has been undertaken using a backward stepwise multiple linear regression. A good performance has been obtained using a log transformation on water quality data designated as predictors and dependent variable. The regression model is in accordance with the ANOVA result. The temperature, biochemical oxygen demand (BOD), Echerichia Coli, Pb and nitrate were described as continuous predictors, while the river location (downstream, municipal and upstream) was designated as independent string grouping variable, and the chemical oxygen demand (COD) was set up as the dependent variable. The string grouping variable was converted to its dummy variable, which in turn led to the design of a three-equation model with respect to river location. The results show that BOD has a strong effect on COD, while Pb and nitrate show less effect on COD. The temperature gives little negative effect on COD, while other variables such as pH, salinity and Cd are excluded from the river modeling since they induce insignificant effects based on backward criterion probability of F-value = 0.100. Using the general linear model with LSD mode, it is revealed that predictor(s) show a remarkable discriminant effect between upstream and municipal/downstream on the 0.05 level. The most effect came from salinity indicated by the canonical discriminant function based on Wilks' lambda.

KW - Backward stepwise multiple linear regression

KW - River modeling

KW - Water quality

UR - http://www.scopus.com/inward/record.url?scp=34548761311&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34548761311&partnerID=8YFLogxK

M3 - Article

VL - 35

SP - 1

EP - 7

JO - Sains Malaysiana

JF - Sains Malaysiana

SN - 0126-6039

IS - 2

ER -