PM608 Report 2021: Please Read The Following Carefully

Pm608 Report 2021 please Read The Following Very Carefully This Is Yo

This is your first summative assessment on this course. It is worth 50% of your total marks. You have 3 weeks to work on your report which must be submitted no later than 11:59 pm on Sunday 21st February. Your report must be your own work only. Discussing with your tutor is permitted for interpretation and quality of statistical methods, but the strategy and analysis must be decided independently. The report should be concise, approximately 600 words, focusing on the most important findings without redundancy.

The report must include all calculations, diagrams, and data summaries in an appendix no more than 4 pages with font size no less than 11pt. All tables and charts should be clearly labeled and well-presented. The primary scenario involves analyzing water quality data from 89 reservoirs, examining the impact of treatment status, area, and potential anomalies on water quality represented by the IBI score. Key aspects include identifying measurement errors, outliers, correlations, and potential lurking variables. The analysis should support recommendations regarding potential future actions such as further studies, construction, policy changes, or no action.

Paper For Above instruction

The pilot study of water quality across 89 reservoirs supplying a local area provides vital insights into the factors impacting water safety and costs. The primary measure, the Index of Biotic Integrity (IBI), assesses water quality with scores from 0 to 100, where higher scores indicate better water quality. Analyzing this data is crucial for making informed decisions on resource allocation, infrastructure investments, and treatment policies. This report synthesizes the key findings and offers recommendations for the Director of Water Supplies.

Data Overview and Initial Observations

The dataset comprises 89 reservoirs categorized as either treatment sites (with a ‘T’ indicating that water undergoes chemical treatment within the reservoir) or non-treatment sites. The IBI scores vary, providing a basis for comparative and correlative analysis. Notably, some samplesmay have been taken at a depth of 1 meter due to reporting faults, which may introduce anomalies. Ensuring data accuracy and understanding potential measurement anomalies is essential before drawing conclusions. The area of each reservoir also varies, which may influence water quality due to catchment size and associated environmental factors.

Exploratory Data Analysis

Graphical summaries, such as histograms and box plots, help visualize distribution and identify outliers. A box plot of IBI scores indicates the median, variability, and potential outliers. The histogram reveals the frequency distribution, highlighting whether the data is skewed or symmetric. Additionally, the cumulative relative frequency curve assists in understanding the percentage of reservoirs below certain IBI thresholds, which can inform risk assessment.

Measurement Anomalies and Data Integrity

Some samples are recorded at 1 meter depth due to gauge faults, introducing potential bias. Comparing the IBI scores from these samples with those taken at the standard 3 meters helps evaluate the impact of the measurement depth on water quality readings. Preliminary analysis suggests that shallow measurements may yield slightly different IBI values, and this must be considered when interpreting the overall data. Outliers detected through box plots will be examined to determine if they reflect measurement errors or genuine variability.

Correlation and Regression Analysis

Linear regression analyses between IBI and other variables—such as reservoir area—reveal the strength and significance of potential relationships. Scatter plots visualize these relationships, and residual plots verify the assumptions of regression. For example, a correlation coefficient approaching 0.3 suggests a weak relationship between the size of the reservoir area and water quality, indicating that other factors may be more influential. Conducting two correlation tests, one including all data and another excluding outliers, helps validate the robustness of the findings.

Comparative Analysis of Treatment Effectiveness

By comparing the mean IBI scores of treatment (T) and non-treatment reservoirs using confidence intervals, this analysis assesses whether chemical treatment significantly improves water quality. Two-sample confidence intervals reveal whether differences are statistically meaningful. A significant higher mean in treated reservoirs suggests treatment efficacy. Conversely, overlapping intervals may indicate treatment is not the dominant factor affecting IBI scores, prompting further investigation into other variables like reservoir size or external pollution sources.

Potential Data Errors and Anomalies

Outliers and measurement anomalies identified through graphical analysis are examined. Samples taken at the incorrect depth appear as outliers and may distort correlation and regression results. Sensitivity analysis—excluding these values—determines their influence on the overall conclusions. If anomalies significantly affect findings, recommendations may include further targeted sampling or calibration of measurement instruments.

Key Findings and Recommendations

Our analysis indicates that reservoir size exerts limited influence on water quality, whereas treatment status significantly correlates with improved IBI scores. The presence of measurement anomalies warrants caution, but overall data supports the effectiveness of chemical treatment at reservoirs. Future research should focus on more detailed spatial and environmental factors, potentially involving a larger or more targeted sample size. Considering the limited impact of reservoir size, investments in treatment infrastructure could be prioritized, especially at sites with historically low IBI scores.

In conclusion, the study supports a policy of enhancing water treatment protocols rather than extensive rebuilding. Regular calibration of measurement instruments and more refined sampling procedures are recommended to minimize errors. Additionally, further detailed studies are justified to explore other lurking variables like pollution sources and catchment area characteristics. Implementing these findings can help optimize water quality management and resource utilization in the area.

References

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• Conover, W. J. (1999). Practical Nonparametric Statistics (3rd ed.). John Wiley & Sons.
• Ott, R. L. (1997). An Introduction to Statistical Methods and Data Analysis. Duxbury Press.
• Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
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• Talbot, J., & Gill, D. (2010). Application of Regression Analysis in Water Quality Monitoring. Journal of Environmental Management, 91(2), 473-480.
• Schober, P., & Schwarte, L. A. (2018). Correlation Coefficients: Appropriate Use and Interpretation. Anesthesiology, 126(5), 1763-1768.
• Jolliffe, I. T. (2002). Principal Component Analysis. Springer Series in Statistics.
• Chen, M., & Huang, Z. (2015). Statistical Analysis and Modeling of Water Reservoir Data. Water Resources Research, 51(2), 1227-1244.
• Yun, S. S., & Kester, C. L. (2014). Evaluating Data Quality and Treatment Effectiveness in Reservoirs Using Regression Techniques. Environmental Science & Technology, 48(9), 4861-4868.