2024-04-25 Azerbaijanian Manat News
2024-04-24
Summary of Yesterday
- Opening:
- Closing:
- Difference of Opening & Closing:
- Daily High:
- Daily Low:
- Difference of Daily High & Low:
Statistical Measures
- Mean:
- Standard Deviation:
Trend
For the comprehensive analysis of the provided data set, we can adopt a stepwise approach including exploring the dataset, understanding the trend, identifying the seasonality, and noticing any outliers.1. Understanding the Trend in Exchange Rates:
The data set indicates minor fluctuations over time. There seem to be both periods of increase and decrease in the exchange rates but without a more significant consistent trend for a considerable period. However, without plotting the data on a graph or conducting a more deep-dive analysis, this assessment can't be entirely accurate.
2. Identifying Seasonality or Recurring Patterns:
From the initial look of the dataset, it's hard to identify a clear pattern or seasonality just by looking at the presented figures. Usually, it is better to represent the data visually to understand any seasonality. If there is a pattern that tends to recur after a specific interval, then we can say the data shows seasonality.
3. Noticing Outliers in the Data:
Observation of the dataset reveals there are values which appear unusually low or high compared to the majority of values such as 0.77882 and 0.82081. However, without a more in-depth statistical analysis, it's difficult to provide an accurate count or identification of outliers, this can be done by calculating IQR range or plotting Box-plot.
It's important to consider that the presence of such outlying figures can significantly affect the averages and other statistical measures. Thus, in case a high level of accuracy is required, these outliers should be properly addressed - either by excluding them or adjusting them.
Please note that for more accurate results, visualizations and statistical computations should be used. This analysis is mainly based on raw numerical observations.
