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The image shows three time series graphs that display measurements of water parameters over the month of July, with a specific time zone indicated as "HST: Hawaii Standard Time (UTC -10:00)". The three parameters plotted appear to be:

1. **Water Temperature (°F)** - This is shown on the left graph in blue, with a y-axis range from approximately 80°F to 87°F. The data suggests fluctuations, with a relatively stable pattern but with occasional peaks and troughs.

2. **Salinity (PSU)** - The middle graph, displayed in orange, shows salinity with units in PSU (Practical Salinity Units). Salinity levels seem fairly stable around 20 PSU, though there are minor fluctuations.

3. **Turbidity (NTU)** - On the right, the turbidity is plotted, also in orange, and is measured in NTU (Nephelometric Turbidity Units). This graph shows lower baseline values with occasional spikes.

The question below the graphs asks about ignoring an outlier data point and estimating maximum and minimum water temperatures for the month.

Would you like me to help with estimating these values, or do you have a specific question in mind?

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### Related Questions:
1. What factors might cause water temperature variations over a month?
2. How does salinity affect marine ecosystems?
3. What are typical causes of spikes in turbidity in water bodies?
4. How can outliers in data affect statistical analysis?
5. What methods can be used to smooth or clean time series data with noise?

**Tip:** When analyzing time series data, identifying and removing outliers can improve the accuracy of trend observations.

Ignoring the one data point that seems to be an outlier, estimate the maximum and minimum water temperatures for the month.

Learn how to analyze water temperature time series data for a given month by estimating maximum and minimum temperatures, excluding outliers. This guide covers concepts of outlier identification and basic statistical analysis, suitable for students in Grades 9-12.

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