2024-05-07 El Salvador Colon News
2024-05-06
Summary of Last Month
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
Unfortunately, your data shows the same exchange rate (10,000,000) at every timestamp, so it's impossible to draw any conclusions or perform any meaningful analysis based on it. Here is the example of the analysis if the data would be different:1. Overall Trend
The overall trend of the exchange rates would be determined by plotting the exchange rates against the timestamp. By applying a trend line, we could identify whether the general direction of the exchange rates is upwards, downwards, or sideways (signifying a more stable rate).
2. Seasonality or Recurring Patterns
Seasonality or recurring patterns could be detected via more complex statistical procedures, but a visiual analysis could show us how the exchange rate changes within a given day or certain days of the week. If the pattern recurs at regular intervals, it is suggestive of seasonality or cyclicity in the data. For instance, one common pattern in time-series financial data could be higher volatility (fluctuations in exchange rate) during certain hours of the day when trading activity is high.
3. Outliers
Outliers in this data can be points where the exchange rate is significantly different from the other values. These outliers may introduce bias or error into our analysis. They could occur due to a variety of reasons, including errors in data collection, or genuine instances of large fluctuations in the exchange rate. Typically, these outliers would be clearly visible when plotting the exchange rates over time.
Unfortunately, as mentioned earlier, due to the homogeneity of your data, none of these points can be observed or analyzed. You should ensure your data is correctly recorded and exhibits variability in exchange rates to perform a comprehensive analysis.