When you were learning algebra back in high school, you might not have realized that one day you would need to create a scatter plot to demonstrate real-world results.

當您在高中學習代數時，您可能沒有意識到有一天需要創建一個散點圖來演示真實結果。

Back in school, the examples we had to plot always seemed silly. Hours spent studying for a test versus the test grade received. The height versus the weight of a group of people. Or the correlation between sales of hot coffee and the outside temperature.

回到學校后，我們不得不繪制的例子似乎總是很愚蠢。 學習測試所花的時間與收到的測試成績的小時數。 一群人的身高與體重。 或熱咖啡的銷量與外界溫度之間的相關性。

But as a working adult (or maybe just a curious one), there are many times you may actually need to use that grade school math.

但是，作為一個正在工作的成年人(或者可能只是一個好奇的成年人)，實際上您可能實際上需要使用很多次小學數學。

And creating a scatter plot is definitely one of those times. There are so many real-world applications that a scatter plot offers that can help you or your audience to visualize data and what it means.

創建散點圖絕對是那些時候之一。 散點圖提供了許多現實世界中的應用程序，可以幫助您或您的觀眾可視化數據及其含義。

Let’s take you back to high school math for a second, because you very well may have left any and all knowledge of what a scatter plot is back at your doodled-on desk.

讓我們回到高中數學上一秒鐘，因為您很可能已經把所有關于散點圖的知識都留在了涂鴉桌上。

什么是散點圖？ (What is a Scatter Plot?)

A scatter plot is a type of data visualization that shows the relationship between different variables. This data is shown by placing various data points between an x- and y-axis.

散點圖是一種數據可視化形式，可顯示不同變量之間的關系。 通過在x軸和y軸之間放置各種數據點來顯示此數據。

Essentially, each of these data points looks “scattered” around the graph, giving this type of data visualization its name.

本質上，這些數據點中的每一個在圖形周圍看起來都是“分散”的，從而使這種類型的數據可視化具有名稱。

Scatter plots can also be known as scatter diagrams or x-y graphs, and the point of using one of these is to determine if there are patterns or correlations between two variables.

散點圖也可以稱為散點圖或xy圖，使用其中之一是確定兩個變量之間是否存在模式或相關性。

Take a look at this example of a scatter plot pulled from one of Visme’s templates.

看一下從Visme的一個模板中提取的散點圖的示例。

Create your own scatter plot using this template. Edit and download here.

使用此模板創建自己的散點圖。 在此處編輯和下載。

The two variables are the square footage of a home versus its price. We pulled a sample data set of a couple handfuls of homes to see if we could determine a relationship between these two variables.

這兩個變量是房屋的平方英尺數與價格之間的差額。 我們提取了幾個房屋的樣本數據集，以查看是否可以確定這兩個變量之間的關系。

As the x-axis goes from the smallest size to the largest, we can see that there is a slight positive correlation showing that as square footage increases, so does the price.

當x軸從最小尺寸變為最大尺寸時，我們可以看到略有正相關，表明隨著平方英尺的增加，價格也隨之增加。

Of course there could be other factors contributing to this, like location or recent renovations, but we can see from this scatter diagram that there is a correlation between the square footage and home cost.

當然，可能還有其他因素，例如位置或最近的裝修，但從散點圖可以看出，平方英尺與房屋成本之間存在相關性。

The patterns or correlations found within a scatter plot will have a few different features.

在散點圖中找到的模式或相關性將具有一些不同的特征。

• Linear or Nonlinear: A linear correlation forms a straight line in its data points while a nonlinear correlation might have a curve or other form within the data points.

  線性或非線性：線性相關在其數據點中形成一條直線，而非線性相關在數據點中可能具有曲線或其他形式。

• Strong or Weak: A strong correlation will have data points close together while a weak correlation will have data points that are further apart.

  強相關或弱相關：強相關將使數據點靠在一起，而弱相關將使數據點靠得更遠。

• Positive or Negative: A positive correlation will point up (i.e., the x- and y-values are both increasing) while a negative correlation will point down (i.e., the x-values are increasing while the corresponding y-values are decreasing).

  正或負：正相關將指向上(即x和y值都在增加)，而負相關將指向下(即x值在增加，而相應的y值在減小)。

However, if you don’t see any of these features present within your graph, that means there’s no correlation between your data.

但是，如果您在圖形中看不到任何這些功能，則意味著數據之間沒有關聯。

何時使用散點圖 (When To Use a Scatter Plot)

Each type of chart or graph has its own rules for when it’s going to be the best data visualization to showcase your information.

每種圖表或圖形都有自己的規則，以決定何時將其作為最佳數據可視化來展示您的信息。

Let’s dive into the best times to use a scatter plot to visualize your data set.

讓我們深入研究最佳時間，使用散點圖可視化您的數據集。

使用散點圖來確定兩個變量是否具有關系或相關性。 (Use a scatter plot to determine whether or not two variables have a relationship or correlation.)

Are you trying to see if your two variables might mean something when put together? Plotting a scattergram with your data points can help you to determine whether there’s a potential relationship between them.

您是否要查看兩個變量放在一起時是否有意義？ 用數據點繪制散點圖可以幫助您確定它們之間是否存在潛在的關系。

Let’s say you’re running an ice cream business, and you’re curious to see if there’s a pattern in why your sales have been low recently.

假設您經營一家冰淇淋店，并且很好奇最近是否有低迷的銷售模式。

You might create a scatter plot to measure different factors, including outside temperature.

您可能會創建一個散點圖以測量不同的因素，包括外部溫度。

Make this scatter plot template your own. Customize it here.

將此散點圖模板設為您自己的。 在這里自定義。

You always want to plot your scatter diagram with both the x-axis and the y-axis increasing as they go out so that you can determine correlation.

您始終希望繪制散點圖，使x軸和y軸隨著散布而增加，以便確定相關性。

As we can see in the above example, people tend to buy ice cream — a cold dessert — less often when the temperature is cold outside.

正如我們在上面的示例中看到的那樣，當外界溫度較低時，人們傾向于購買冰淇淋(一種冷甜點)。

當自變量具有多個因變量值時，請使用散點圖。 (Use a scatter plot when your independent variable has multiple values for your dependent variable.)

Okay, let’s take it back to math class for a minute and go over what independent and dependent variables mean.

好吧，讓我們回到數學課上一分鐘，然后再討論自變量和因變量的含義。

First of all, a variable is the thing you’re trying to track or measure. Every graph has two variables — an independent variable that is typically graphed on the x-axis and a dependent variable that is typically graphed on the y-axis.

首先，變量是您要跟蹤或測量的事物。 每個圖都有兩個變量-一個通常在x軸上繪制的自變量和一個通常在y軸上繪制的因變量。

An independent variable is the controlled variable. This is what changes naturally, or what the person manipulating the experiment or graph changes.

自變量是受控變量。 這就是自然改變的東西，或者是操作實驗或圖形的人改變的東西。

A dependent variable is the variable that is being studied or measured. In the case of a scatter plot, it’s the variable that we’re looking to determine whether or not has a correlation with the independent variable.

因變量是正在研究或測量的變量。 就散點圖而言，它是我們要確定與自變量是否相關的變量。

If you’re trying to determine if height and weight have a correlation, the height will be placed on the x-axis and weight will be placed on the y-axis, like in the example below.

如果要確定身高和體重是否相關，則將高度放在x軸上，將重量放在y軸上，如以下示例所示。

Create your own scatter plot with this template. Find the template here.

使用此模板創建自己的散點圖。 在此處找到模板。

Because weight fluctuates much more than height, it’s likely that you could have different weights for the same height in your data, giving you more than one dependent variable value for each independent variable.

由于權重的波動遠大于高度的波動，因此同一數據中的高度可能具有不同的權重，從而為每個自變量提供多個因變量值。

當兩個變量配對得很好時，請使用散點圖。 (Use a scatter plot when you have two variables that pair well together.)

If you have two variables that pair well together, plotting them on a scatter diagram is a great way to view their relationship and see if it’s a positive or negative correlation.

如果您有兩個變量配對得很好，則將它們繪制在散點圖上是查看它們之間的關系并查看其是正相關還是負相關的好方法。

For example, think about birth weight versus gestational age (how long the baby has been in utero). It would make sense that a baby who was able to grow inside its mother for longer would be larger, and therefore weigh more, correct?

例如，考慮出生體重與胎齡(嬰兒進入子宮的時間)。 能夠在母親體內長一些的嬰兒會更大，因此體重會增加，對嗎？

Let’s take a look at this data on a scatter plot.

讓我們在散點圖上查看這些數據。

Make your scatter plot using this template. Find it here.

使用此模板制作散點圖。 在這里找到它。

As we would expect, the longer a baby is able to “cook,” the more it tends to weigh at birth.

正如我們所期望的，嬰兒能夠“做飯”的時間越長，出生時體重就越重。

Other examples of variables that appear to go hand in hand would be hours worked versus money made, time studied versus test grade or price versus diamond size.

似乎還有其他變量的例子還有：工作時間與賺錢，研究時間與測試等級或價格與鉆石尺寸。

何時不使用散點圖 (When Not to Use a Scatter Plot)

Just as there are certain times that it makes sense to use a scatter plot to visualize your data, there are a couple of examples when you want to stay away from this type of chart.

正如在某些情況下使用散點圖可視化數據是有意義的，當您想遠離此類圖表時，有幾個示例。

當您的數據根本不相關時，請避免使用散點圖。 (Avoid a scatter plot when your data is not at all related.)

There are certain variables that make it obvious that there’s no correlation, therefore a scatter plot would be a useless way to visualize your information.

有某些變量使它們之間毫無關聯，這很明顯，因此散點圖將是一種可視化您的信息的無用方法。

For example, if you’re gathering a random survey on a classroom full of students, putting together the students’ varying heights and the number of pets they have at home would make no sense on a scatter plot.

例如，如果您要在教室里滿是學生的情況下進行隨機調查，那么將散布在不同地點的學生的身高和寵物的數量匯總在一起就毫無意義。

These two variables obviously have no relationship whatsoever, and while they can still be fun to graph, a bar chart (one for each data value) might be the better choice here.

這兩個變量顯然沒有任何關系，盡管它們仍然很有趣，但是條形圖(每個數據值一個)可能是更好的選擇。

當數據集過多時，請避免使用散點圖。 (Avoid a scatter plot when you have too large a set of data.)

When you have so much data in your scatter plot that it clogs up the entire graph, this is the result of overplotting.

當散點圖中的數據太多而阻塞整個圖形時，這是過度繪圖的結果 。

Statistician Nathan Yau sums up this phenomenon pretty well in the below graphic:

統計員Nathan Yau在下圖中很好地總結了這種現象：

As another example, take a look at the scatter plot below. It’s so dense that it essentially becomes one large blob, and it’s hard to read much from this kind of diagram.

作為另一個示例，請看下面的散點圖。 它是如此的密集，以至于它實際上變成了一個大斑點，并且很難從這種圖表中讀到很多東西。

There are a few ways to counteract an overplotted scatter plot, though. First, consider using a heatmap that shows where the most point-heavy sections of your data are.

不過，有幾種方法可以抵消過度繪制的散點圖。 首先， 考慮使用熱圖來顯示數據中最繁瑣的部分所在的位置。

You could also color code various data sets, use translucent data points to create a heatmap-like effect and more.

您還可以對各種數據集進行顏色編碼，使用半透明的數據點來創建類似熱圖的效果等等。

However, your best bet is to avoid using a scatter plot when you have so much data that it becomes a large blob.

但是，最好的選擇是避免在數據量太大而成為大斑點時使用散點圖。

散點圖要注意的事項 (Things to Keep in Mind With a Scatter Plot)

As you take a look at your data, there are a few things to keep in mind when you decide to use a scatter plot to determine relationships or correlation.

當您查看數據時，在決定使用散點圖確定關系或相關性時要牢記一些注意事項。

相關并不總是因果關系。 (Correlation is not always causation.)

Just because you might see a strong positive or negative correlation in your data does not necessarily mean that your independent variable is the reason your dependent variable is measuring the way it is.

僅僅因為您可能會看到數據中存在強的正相關或負相關性，并不一定意味著您的自變量就是您的因變量正在測量其方式的原因。

These are correlations, meaning that it appears that your independent variable does have some sort of effect on your dependent variable.

這些是相關性，這意味著您的自變量似乎確實會對您的因變量產生某種影響。

Let’s jump back into our ice cream sales example.

讓我們回到我們的冰淇淋銷售示例中。

While it may seem that the weather is the direct cause of a decrease in sales, there could be so many other factors that are leading to slower business.

盡管天氣似乎是銷量下降的直接原因，但可能還有許多其他因素導致業務放緩。

Perhaps there was a natural disaster like a hurricane that led to a mandatory evacuation and therefore less business. A new ice cream shop could have opened down the street creating competition that wasn’t there before.

也許發生了像颶風這樣的自然災害，導致強制撤離，因此生意減少了。 一家新的冰淇淋店本可以在大街上開張，從而創造出前所未有的競爭優勢。

Some days people just don’t want to buy ice cream. And while, sure, the colder weather might be a factor, just because you see a correlation on a scatter plot does not mean you should take it as law.

有時候人們只是不想買冰淇淋。 當然，雖然寒冷的天氣可能是一個因素，但僅僅是因為您看到散點圖上的相關性并不意味著您應該將其視為定律。

您可以有多個因變量。 (You can have more than one dependent variable.)

Your data set might include more than one dependent variable, and you can still track this on a scatter plot.

您的數據集可能包含多個因變量，您仍然可以在散點圖上對其進行跟蹤。

The only thing you’ll want to change is the color of each dependent variable so that you can measure them against each other on the scatter plot.

您唯一要更改的是每個因變量的顏色，以便您可以在散點圖上相對于它們進行度量。

Let’s take a look back at our height versus weight example.

讓我們回顧一下身高與體重的示例。

In that scatter plot, we added two different dependent variables — male and female — to see if there was also a difference between those factors. We colored female points orange and male points brown so that we could differentiate between the two.

在該散點圖中，我們添加了兩個不同的因變量(男性和女性)，以查看這些因素之間是否也存在差異。 我們將雌性點涂成橙色，將雄性點涂成棕色，以便我們可以區分兩者。

This is another great way to avoid overplotting. Ensuring you’re color coding your data helps to set it apart so that you can see more of your points.

這是避免過度繪圖的另一種好方法。 確保對數據進行顏色編碼有助于將數據區分開，以便可以看到更多點。

如何使用Visme創建散點圖 (How to Create a Scatter Plot With Visme)

Now that you know all about what a scatter plot is and when you do and don’t want to use one, let’s get started with our tutorial on how you can actually create one.

既然您已經了解了什么是散點圖以及何時使用和不希望使用散點圖，那么讓我們開始學習如何實際創建散點圖的教程。

There are 16 different chart types you can create right in Visme, and a scatter plot is only one of them! Learn more about how to get started below.

您可以在Visme中直接創建16種不同的圖表類型 ，而散點圖只是其中之一！ 在下面詳細了解如何入門。

1.從模板開始。 (1. Start with a template.)

There are several different starter scatter plot templates available right in Visme for you to jump into and start adding your data.

Visme中有幾種可用的入門散點圖模板 ，您可以跳入并開始添加數據。

Follow this link to discover more templates in the Visme library.

單擊此鏈接可在Visme庫中發現更多模板。

2.輸入您的數據。 (2. Input your data.)

Click on the scatter plot, and the graph settings will appear along the left side of your editor. Click Chart Data to input your data.

單擊散點圖，圖形設置將出現在編輯器的左側。 單擊圖表數據以輸入數據。

The x-axis information will go in the top row and the corresponding y-axis data will go in the bottom row. Be sure that all of your numbers on the x-axis are in numerical order from lowest to highest.

x軸信息將顯示在頂部，相應的y軸數據將顯示在底部。 確保在x軸上的所有數字均按從低到高的數字順序排列。

If you have more than one dependent variable, simply add that information to the next row for a second variable, the fourth row for a third and so on.

如果您有多個因變量，只需將該信息添加到第二行的下一行，第四行添加第三行，依此類推。

You can also head over to the Import Data tab to import data you’ve already gathered in a Google Sheet or Excel file.

您也可以轉到“ 導入數據”標簽，導入已在Google表格或Excel文件中收集的數據。

3.標記您的軸。 (3. Label your axes.)

Head over to Settings, the third tab in the chart settings. Click to open the Axis tab. This is where you can customize your x- and y-axis information and ensure your scatter plot data is appearing correctly.

轉到“設置” (圖表設置中的第三個標簽)。 單擊以打開“ 軸”選項卡。 在這里您可以自定義x軸和y軸信息，并確保散點圖數據正確顯示。

The first thing you need to do is ensure that Treat labels as text is switched to Off, otherwise your scatter plot will look a bit more like a with dots.

您需要做的第一件事是確保將“ 將標簽當文本”設置為Off ，否則散點圖看起來更像帶有點的a。

Give each axis a label dictating what the variable is and customize your fonts to match the rest of your design by clicking the gear icon next to each axis. Or, as you see here, you can use separate text areas to label your graph.

單擊每個軸旁邊的齒輪圖標，為每個軸指定一個指示變量的標簽，并自定義字體以匹配設計的其余部分。 或者，如您在此處看到的，可以使用單獨的文本區域來標記圖形。

Check out our article on font pairing to determine the best ones to use.

請查看我們有關字體配對的文章，以確定最適合使用的字體 。

4.顏色代碼。 (4. Color code.)

You can fully customize every single part of your scatter plot in Visme’s editor. Choose a color for each of your dependent variable’s points, choose a color for your values, your axes and your chart title.

您可以在Visme的編輯器中完全自定義散點圖的每個部分。 為每個因變量的點選擇一種顏色，為值，軸和圖表標題選擇一種顏色。

Click on the colored box next to each variable or each label in your settings to access the color picker. Choose from colors you’ve used in the past and preset palettes, or click on the + sign to access Visme’s color picker.

單擊設置中每個變量或每個標簽旁邊的彩色框以訪問顏色選擇器。 從過去使用的顏色和預設調色板中進行選擇，或單擊+號以訪問Visme的顏色選擇器。

Once you find colors that make sense for your content or your overall design, you can move onto the next step.

找到適合您的內容或整體設計的顏色后，即可繼續進行下一步。

5.動畫散點圖。 (5. Animate your scatter plot.)

Last but not least, animate your scatter diagram! Animation comes with any type of graph or chart within Visme’s editor.

最后但并非最不重要的一點是，為散點圖設置動畫！ 動畫附帶Visme編輯器中的任何類型的圖形或圖表。

There are five different animation types for you to choose from for your chart: Linear, Bounce, Elastic, Ease Out and Ease In.

圖表有五種不同的動畫類型供您選擇：“線性”，“彈跳”，“彈性”，“緩出”和“緩入”。

Once you’ve completed your changes, simply click outside of the chart on your canvas, then you can download your scatter plot or share it online however you like.

完成更改后，只需在畫布上圖表的外部單擊，即可下載散點圖或根據需要在線共享。

輪到你 (Your Turn)

Ready to start plotting your own scatter plot? Check out Visme’s graph maker for yourself and start creating one of 16 different charts to help visualize your information.

準備開始繪制自己的散點圖了嗎？ 親自檢查Visme的圖表制作器 ，并開始創建16種不同圖表之一，以幫助可視化您的信息。

Be sure to check out all of the scatter plot templates available to jumpstart your design, as well. We’re just here to make graph-making and graphic design easier for you.

一定要檢查所有可用于啟動設計的散點圖模板 。 我們只是在這里為您簡化圖形制作和圖形設計。

The original version of this post first appeared on Visme’s Visual Learning Center.

這篇文章 的 原始版本 首次出現在Visme的 Visual Learning Center中 。