What is the translation of " SCATTER PLOT " in Chinese?

• 散点图

Examples of using Scatter plot in English and their translations into Chinese

The second shows the scatter plot that we see.
第二个显示我们看到的散点图。

Each of these new samples will give us a scatter plot.
这些新样本中的每一个都会给我们一个散点图。

The first panel shows how the scatter plot is generated from the true line.
第一个面板显示如何从真实直线生成散点图。

Scatter plot can be used to find the relationship between two continuous variables.
散点图可以用来发现两个连续变量之间的关系。

We know how to find the best straight line to draw through a scatter plot..
我们知道如何找到穿过散点图的最佳直线来绘制。

The scatter plot is remarkably close to linear, and the correlation is more than 0.92.
散点图明显接近线性，相关性大于0.92。

You can create maps, bar charts, scatter plots and more without the need for programming.
你可以创建图形、条形图、散点图及更多视图，不需要编程。

The scatter plot below shows all 544 movies which managed to fall below my flop standard.
下面的散点图显示所有544电影都设法低于我的翻牌标准。

The regression model seems reasonable, based on the scatter plot, but the correlation is not high.
回归模型似乎是合理的，基于散点图，但相关性不高。

A scatter plot is a great way to visualize the relationship between two variables.
散点图是用来可视化两个变量之间的关系的很好的方式。

We can take the first 2 principal components and then visualize the data using scatter plot.
例如我们可以提取前2个主要组成部分，然后在二维平面上使用散点图可视化数据。

The graph below shows the scatter plot and line that we developed in the previous section.
下图显示了我们在上一节中开发的散点图和直线。

The mathematics of this minimization is complicated and not easy to see just by examining the scatter plot.
这种最小化的数学是复杂的，不容易仅仅通过检查散点图来发现。

If the scatter plot does not look like that, then perhaps the model does not hold for the data.
如果散点图看起来不像那样，那么模型可能不适用于数据。

Call r_scatter a few times, with different values of$r$ as the argument, and see how the scatter plot changes.
调用r_scatter几次，以r的不同值作为参数，并查看散点图如何变化。

With Glue, users can create scatter plots, histograms and images(2D and 3D) of their data.
使用Glue，用户可以创建数据的散点图，直方图和图像（2D和3D）。

Scatter plots are used to show if one variable is a good predictor of another, or if they tend to change independently.
散点图用于显示一个变量是否是另一个变量的良好预测指标，或者它们是否倾向于独立变化。

With its help, you can build diverse charts, from histograms and scatter plots to non-Cartesian coordinate's graphs.
借助它的帮助，您可以构建各种图表，从直方图和散点图到非笛卡尔坐标图。

Python drew two scatter plots: one each for the relation between this variable and the other two.
Python绘制了两个散点图：这个变量和另外两个之间的关系，每个关系一个。

We saw this in the example about Little Women, but let's confirm it in an example where the scatter plot clearly isn't football shaped.
我们在《小女人》的例子中看到了它，但是让我们以散点图显然不是橄榄形的例子来证实它。

The scatter plot of the two variables shows a positive association, as we would expect for these variables.
两个变量的散点图显示了正相关，正如我们对这些变量的预期。

In this section we will go over some common plots, namely the line chart, scatter plot, and histogram.
在本节中，我们将讨论一些常见的图表，即折线图(linechart)，散点图(scatterplot)和直方图(histogram)。

Drawing the scatter plot of the data usually gives an indication of whether the relation between the two variables is non-linear.
绘制数据的散点图，通常表明了两个变量之间的关系是否是非线性的。

What is it: A Scatter Diagram or also called scatter plot or a X- Y graph is used to identify relationships between two process variables.
它是什么：散点图或也称为散点图或1的X-Y图是用来确定两个过程变量之间的关系。

Scatter plots are great for showing the relationship between two variables since you can directly see the raw distribution of the data.
散点图非常适合显示两个变量之间的关系，因为你可以直接看到数据的原始分布。

We will call that a bootstrapped scatter plot, and for short, we will call the entire process bootstrapping the scatter plot.
我们将这个称为自举散点图，简而言之，我们将调用整个过程来自举散点图。

Scatter plot: VisIt's Scatter plot allows visualizing multivariate data of up to four dimensions.
散点图（Scatterplot）：VisIt的散点图可用于高达四维的多变量数据（multivariatedata）的可视化。

For example, a scatter plot of birth weight versus gestational days shows us the precise relation between the two variables in our sample;
例如，出生体重与孕期的散点图，显示了我们样本中两个变量之间的精确关系；.

Here, the scatter plot of PI stained cells show two distinct clusters, one with almost twice the amount of PI than the other.
在这里，散点图的PI染色细胞显示两个独特的群体，几乎两倍的PI比另一个。

Those were the scatter plot and the line plot, both of which display two numerical variables- the variables on both axes are numerical.
它们是散点图和线图，两者都显示两个数值变量-两个轴上的变量都是数值型的。