histogram

简明释义

英[ˈhɪstəɡræm]美[ˈhɪstəɡræm]

n. [统计] 直方图；柱状图

英英释义

单词用法

同义词

反义词

例句

1.In this case, I'm going to plot a histogram.

在这个例子中我会画一个柱状图。

2.400 step histogram of living room temperatures

起居室温度的 400-级直方图

3.Notice that although the longest delays haven't changed much, the histogram is much shorter than it was initially.

注意，尽管最长的延迟没有多大变化，但直方图比最初小多了。

4.If you notice that histogram skews to the left, the image is probably too dark, or underexposed.

如果你发现直方图集中在左侧，说明图像太暗了，或者说曝光不足。

5.In this histogram, we have mid and dark tones but no light tones.

在这个分布图中，我们有中和色调以及暗色调但是没有浅色调。

6.And then I'm going to plot a histogram, Pylab dot hist instead of plot, and we'll get something quite different.

然后我将要绘制一个柱状图，我们要用Pylab,hist而不是plot了，我们能获取不一样的东西。

7.Here is the histogram in our example.

下图是我们这个例子的直方图。

8.There is no right or wrong histogram because different shots require different levels of brightness.

直方图没有好坏之分，因为不同的拍摄需要不同的亮度。

9.The data analyst created a histogram to visualize the distribution of test scores among students.

数据分析师创建了一个直方图来可视化学生测试分数的分布。

10.By examining the histogram, we can see that most customers prefer product A over product B.

通过检查这个直方图，我们可以看到大多数客户更喜欢产品A而不是产品B。

11.The histogram showed a normal distribution, indicating that the data is well-behaved.

这个直方图显示出正态分布，表明数据表现良好。

12.In our presentation, we included a histogram to illustrate the frequency of different age groups in our survey.

在我们的演示中，我们包含了一个直方图来说明我们调查中不同年龄组的频率。

13.To analyze the performance of the model, we plotted a histogram of the residuals.

为了分析模型的性能，我们绘制了残差的直方图。

作文

In the field of statistics and data analysis, visual representation is crucial for understanding complex data sets. One of the most effective tools for this purpose is the histogram. A histogram is a type of bar graph that represents the distribution of numerical data. It is particularly useful for illustrating the frequency of data points within specified ranges, known as bins. By organizing data into these bins, a histogram provides a clear visual summary, allowing analysts and researchers to quickly grasp the underlying patterns and trends in their data. To create a histogram, one must first collect a set of quantitative data. This data can come from various sources, including experiments, surveys, or observational studies. Once the data is gathered, it is essential to determine the range of values and the number of bins to use. The choice of bin size can significantly affect the appearance and interpretability of the histogram. If the bins are too wide, important details may be lost; conversely, if they are too narrow, the histogram may appear cluttered and difficult to read. Once the bins are established, the next step is to count how many data points fall into each bin. This count forms the height of the bars in the histogram. Each bar represents a bin, and its height indicates the frequency of data points within that range. For example, if we were to analyze the heights of a group of individuals, we could create a histogram with bins representing different height ranges (e.g., 150-160 cm, 160-170 cm, etc.). The resulting histogram would allow us to see how many individuals fall into each height category, providing valuable insights into the overall distribution of heights in the group. One of the significant advantages of using a histogram is that it can reveal the shape of the data distribution. Common shapes include normal distributions, where data points cluster around a central value, and skewed distributions, where data points are unevenly distributed. Identifying these shapes is essential in statistics, as they can inform further analysis and decision-making. For instance, a normal distribution might suggest that standard statistical methods can be applied, while a skewed distribution may require alternative approaches. Moreover, histograms can highlight outliers—data points that fall far outside the expected range. Recognizing outliers is crucial in data analysis, as they can significantly impact the results and interpretations of statistical tests. By visually inspecting a histogram, analysts can quickly identify these unusual data points and decide how to handle them in their analyses. In conclusion, the histogram is an invaluable tool in the realm of data analysis. Its ability to visually represent the distribution of numerical data makes it easier for researchers and analysts to understand complex datasets. By effectively utilizing histograms, one can uncover patterns, identify outliers, and make informed decisions based on data. As the importance of data-driven decision-making continues to grow across various fields, mastering the use of histograms will undoubtedly enhance one's analytical skills and contribute to more effective data interpretation.

在统计和数据分析领域，视觉表现对于理解复杂的数据集至关重要。其中一个最有效的工具就是直方图。直方图是一种条形图，用于表示数值数据的分布。它特别适合用来说明数据点在特定范围内的频率，这些范围被称为箱（bins）。通过将数据组织到这些箱中，直方图提供了清晰的视觉摘要，使分析师和研究人员能够快速掌握数据中的潜在模式和趋势。 要创建一个直方图，首先必须收集一组定量数据。这些数据可以来自各种来源，包括实验、调查或观察研究。一旦数据收集完成，就需要确定值的范围和使用的箱的数量。箱的大小选择会显著影响直方图的外观和可读性。如果箱太宽，重要细节可能会丢失；相反，如果箱太窄，直方图可能会显得杂乱且难以阅读。 一旦确定了箱，下一步就是计算每个箱中有多少数据点。这一计数形成了直方图中条形的高度。每个条形代表一个箱，其高度表示该范围内数据点的频率。例如，如果我们要分析一组个体的身高，我们可以创建一个直方图，其中箱代表不同的身高范围（例如，150-160厘米，160-170厘米等）。生成的直方图将使我们能够看到有多少个体落入每个身高类别，从而提供有关该组身高整体分布的宝贵见解。 使用直方图的一个显著优势是它可以揭示数据分布的形状。常见的形状包括正态分布，其中数据点集中在中心值附近，以及偏态分布，其中数据点分布不均。识别这些形状在统计学中至关重要，因为它们可以为进一步分析和决策提供信息。例如，正态分布可能表明可以应用标准统计方法，而偏态分布可能需要替代方法。 此外，直方图可以突出异常值——远离预期范围的数据点。识别异常值在数据分析中至关重要，因为它们可能显著影响结果和统计检验的解释。通过视觉检查直方图，分析师可以快速识别这些不寻常的数据点，并决定如何在分析中处理它们。 总之，直方图在数据分析领域是一个无价的工具。它能够直观地表示数值数据的分布，使研究人员和分析师更容易理解复杂的数据集。通过有效利用直方图，可以发现模式、识别异常值，并根据数据做出明智的决策。随着数据驱动决策在各个领域的重要性不断增长，掌握直方图的使用无疑将增强个人的分析技能，并有助于更有效的数据解释。