2024-05-08 Pound Sterling News
2024-05-07
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
1. Understanding the overall trend of the exchange rates
Without plotting the data and computing a regression line, it's difficult to discern an exact trend with the naked eye. However, at a casual glance, the dataset seems to show several cycles of rises and drops, suggesting that the exchange rate between GBP is highly volatile and changes frequently over a short period of time. There doesn't seem to be a clear upward or downward trend; instead, the rate seems to fluctuate around a certain level.
2. Identifying any seasonality or recurring patterns in the changes of exchange rates
It's unclear based on this data whether there is any seasonality or recurring patterns in the exchange rates. The data set would need to be visualized in order to determine if any seasonality exists. For time series data such as exchange rates, visualization often involves plotting the rate against time, with time being represented along the X-axis and the exchange rate being represented along the Y-axis. This technique could reveal patterns in the data such as daily or hourly patterns.
3. Noting any outliers, or instances where the exchange rate differs significantly from what would be expected based on the trend or seasonality
Without calculating the mean and standard deviation of the exchange rate and comparing individual rates to these statistics, it's nearly impossible to accurately identify outliers within the dataset. Outliers are typically defined as values that are more than 1.5 times the interquartile range (IQR) above the third quartile or below the first quartile. IQR measures statistical dispersion, or how far apart the values in a data set are. However, there appears to be some instances in the data such as the value '1.68913' which stands out from the majority of the data points, indicating it may be an outlier.
In general, it seems that the dataset is highly volatile and changes drastically over a short period of time. To gain more conclusive insights, the data would need to be explored more rigorously using data visualization and statistical testing.