APPLICATIONS OF STATISTICAL ANALYSES ON WATER QUALITY DATA AND ITS RECENT RESEARCH TRENDS

An Academic presentation by

Dr. Nancy Agnes, Head, Technical Operations, Statswork Group www.statswork.comEmail: info@statswork.com

Outline

TODAY'S DISCUSSION

IntroductionStatistical Techniques Future Scope

Analysing water quality data entails reviewing and assessing the data to see if any errors were made during the sampling or analysis of the water quality sample or data entry.

To detect any issues regarding data, a series of data checks should be performed.

It includes data checking in the first stage, i.e. data entry, whether the data is within the range of parameters, data is within the detection limits, etc.

INTRODUCTION

However, there are different aspects of water quality, and the techniques suitable for each field are tabulated in the following subsection.

One way to analyse the water quality data is using graphical techniques.

The benefits of graphical representation of data include finding the datatrend, finding outliers in the data, etc.

STATISTICAL TECHNIQUES

The common statistical analysis or techniquesto handle water quality data is as follows:

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It acts as an important factor for water quality analysis since it helps the researcher understand the data's variability.

Wang et al. (2020) proposed an innovative trend analysis to detect or identifythe annual and seasonal rainfall pattern.

Data collection from different meteorological stations and compare theproposed method with Theil-Sen trend method Mann-Kendall test.

The result revealed a strong trend associated with flood and drought during extreme rainfall.

1.TREND ANALYSIS

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These methods' validity showed that the proposed method detects theseasonal trend accurately than using the other two test methods.

Our Data collection service help in collect clean data and maximize your impact.

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Correlation is basically to identify the relationship between two or morevariables. It helps to identify the variables which control the variability in water quality data.

For example, consider a study on water flowing quality on land, i.e. water quality in the rivers.

One can take different research problem based on the river data.

However, suppose our interest is to find the seasonality of water quality inselected areas and the land usage.

The correlation matrix will look like the following table 1. Contd...

2. CORRELATION

Table 1: Sample Correlation Matrix

3. REGRESSION ANALYSIS

Regression analysis helps find the average relationship between the variables and is useful to predict future outcomes.

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4. AUTOCORRELATION ANALYSIS

Ifwewanttounderstandtherelationshipbetweentwoormoresimilarattributes measured at different time points, then autocorrelation analysis can be used.

5. TESTING STATISTICAL HYPOTHESIS

6. STATISTICAL MODELLING

It is used to identify the behavior and predict future outcomes through a mathematical formulation.

Contd...

It is used to monitor the process and detect the data variability using control limits.

The most popular is the mean chart and range chart.

If any data points are scattered away from the limits, they can be considered defective and treated as an outlier(s).

However, selecting a suitable statistical analysis or water quality analysis technique depends on the data and the research question.

The choice of suitable analytical technique is based on detection limits, i.e.Contd...

7. CONTROL CHARTS

range of concentration of the chemical component in the water, how much accuracy and precision are needed for the research problem.

The most important is the sampling strategy.

Statswork provide suitable online statistical analysis service to get high quality data.

FUTURE SCOPE

There are numerous statistical procedures or techniques to analyse the water quality data.

Since water scarcity is increasing due to lack of rainfall in many regions, finding the water quality for the recycled water, research related to turning the hard water to soft water, etc. are considered future research scope.

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