average of adjacent values

简明释义

相邻值平均值;

英英释义

例句

1.The algorithm calculates the average of adjacent values 相邻值的平均数 to create a smoother curve.

该算法计算相邻值的平均数以创建更平滑的曲线。

2.In our weather model, we consider the average of adjacent values 相邻值的平均数 for temperature readings to improve accuracy.

在我们的天气模型中，我们考虑温度读数的相邻值的平均数以提高准确性。

3.To smooth out the data, we can calculate the average of adjacent values 相邻值的平均数 in the dataset.

为了平滑数据，我们可以计算数据集中相邻值的平均数。

4.When analyzing stock prices, traders might look at the average of adjacent values 相邻值的平均数 to identify trends.

在分析股票价格时，交易者可能会查看相邻值的平均数以识别趋势。

5.In image processing, applying a filter often involves finding the average of adjacent values 相邻值的平均数 to reduce noise.

在图像处理过程中，应用滤波器通常涉及找到相邻值的平均数以减少噪声。

作文

In the world of data analysis, understanding how to manipulate and interpret numerical information is crucial. One important concept that often arises in this field is the average of adjacent values. This term refers to the process of calculating the mean of two or more values that are next to each other in a dataset. By focusing on these neighboring values, analysts can uncover trends and patterns that might not be immediately apparent when looking at individual numbers alone.For example, consider a simple dataset representing daily temperatures over a week: 70°F, 72°F, 68°F, 75°F, and 74°F. If we want to analyze the temperature changes throughout the week, we might calculate the average of adjacent values for each pair of days. The first calculation would involve the temperatures of the first two days: (70 + 72) / 2 = 71°F. Next, we would calculate the average for the second and third days: (72 + 68) / 2 = 70°F. Continuing this process, we find the averages for the remaining pairs: (68 + 75) / 2 = 71.5°F and (75 + 74) / 2 = 74.5°F.These calculated averages provide a clearer picture of how the temperature fluctuated from day to day. Instead of merely observing the temperatures in isolation, we gain insights into the overall trend. The average of adjacent values allows us to smooth out the data, making it easier to visualize changes over time.This technique is not limited to temperature data; it can be applied to various fields such as finance, sports statistics, and even social sciences. For instance, in finance, an investor might look at the average of adjacent values of stock prices to assess market trends. By averaging the closing prices of a stock over consecutive days, investors can identify whether the stock is generally increasing or decreasing, helping them make informed decisions.Moreover, the average of adjacent values can also be useful in image processing. When editing images, algorithms often use this concept to reduce noise and enhance clarity. By averaging the pixel values of neighboring pixels, the algorithm can smooth out variations and produce a cleaner image.However, it is essential to recognize that while the average of adjacent values can provide valuable insights, it also has its limitations. For instance, if there are significant outliers in the data, the averages may not accurately reflect the overall trend. Therefore, it is crucial to consider the context and characteristics of the dataset before relying solely on this method.In conclusion, the average of adjacent values is a powerful tool in data analysis that helps to reveal trends and patterns by focusing on neighboring values. Whether analyzing temperature fluctuations, stock prices, or even pixel data in images, this technique offers a way to simplify complex information. However, one must exercise caution and consider the broader context of the data to ensure accurate interpretations. By mastering the concept of the average of adjacent values, analysts can enhance their ability to derive meaningful insights from numerical data, ultimately leading to better decision-making and understanding of various phenomena in our world.

在数据分析的世界中，理解如何操纵和解释数据信息至关重要。一个在这个领域中经常出现的重要概念是相邻值的平均值。这个术语指的是计算数据集中两个或多个彼此相邻的值的均值的过程。通过关注这些相邻的值，分析师可以发现趋势和模式，这些趋势和模式在单独查看数字时可能并不明显。例如，考虑一个简单的数据集，表示一周内的每日温度：70°F、72°F、68°F、75°F和74°F。如果我们想分析一周内的温度变化，我们可能会计算每对天数的相邻值的平均值。第一次计算将涉及前两天的温度：(70 + 72) / 2 = 71°F。接下来，我们将计算第二天和第三天的平均值：(72 + 68) / 2 = 70°F。继续这个过程，我们找到剩余对的平均值：(68 + 75) / 2 = 71.5°F和(75 + 74) / 2 = 74.5°F。这些计算出的平均值提供了更清晰的温度波动图景。与其孤立地观察温度，不如说我们获得了关于整体趋势的洞察。相邻值的平均值使我们能够平滑数据，从而更容易可视化随时间的变化。这种技术不仅限于温度数据；它可以应用于金融、体育统计甚至社会科学等各个领域。例如，在金融领域，投资者可能会查看股票价格的相邻值的平均值以评估市场趋势。通过对连续几天的收盘价进行平均，投资者可以识别出股票通常是上涨还是下跌，从而帮助他们做出明智的决策。此外，相邻值的平均值在图像处理方面也非常有用。在编辑图像时，算法经常使用这个概念来减少噪音并增强清晰度。通过对相邻像素的像素值进行平均，算法可以平滑变化并产生更清晰的图像。然而，必须认识到，虽然相邻值的平均值可以提供有价值的见解，但它也有其局限性。例如，如果数据中存在显著的异常值，平均值可能无法准确反映整体趋势。因此，在仅依赖这种方法之前，考虑数据集的上下文和特征至关重要。总之，相邻值的平均值是数据分析中的一种强大工具，通过关注相邻值，帮助揭示趋势和模式。无论是在分析温度波动、股票价格还是图像中的像素数据，这种技术都提供了一种简化复杂信息的方法。然而，必须谨慎行事，并考虑数据的广泛背景，以确保准确的解释。通过掌握相邻值的平均值的概念，分析师可以提高从数值数据中提取有意义见解的能力，最终导致更好的决策和对我们世界中各种现象的理解。