标称型数据和数值型数据_统计信息中的数据类型-标称，有序，间隔和⽐率数据类型，并举例说明
标称型数据和数值型数据
If you're studying for a statistics exam and need to review your data types this article will give you a brief overview with some simple examples.
如果您正在学习统计考试，并且需要检查数据类型，那么本⽂将通过⼀些简单的⽰例为您提供简要概述。
Because let's face it: not many people study data types for fun or in their real everyday lives.
因为我们要⾯对现实：很少有⼈在娱乐或现实⽣活中研究数据类型。
So let's dive in.
因此，让我们开始吧。
定量与定性数据-有什么区别？ (Quantitative vs Qualitative data - what's the difference?)
In short: quantitative means you can count it and it's numerical (think quantity - something you can cou
nt). Qualitative means you can't, and it's not numerical (think quality - categorical data instead).
简⽽⾔之：定量意味着您可以对它进⾏计数，并且它是数字(想想数量 -您可以计数)。 定性意味着您不能，⽽且不是数字(请考虑质量 -⽽是分类数据)。
Boom! Simple, right?
繁荣！ 简单吧？
There's one more distinction we should get straight before moving on to the actual data types, and it has to do with quantitative (numbers) data: discrete vs. continuous data.
在继续介绍实际的数据类型之前，我们还应该弄清楚⼀个区别，它与定量(数字)数据有关：离散数据与连续数据。
Discrete data involves whole numbers (integers - like 1, 356, or 9) that can't be divided based on the nature of what they are.
离散数据涉及整数(例如1、356或9之类的整数)，这些整数⽆法根据它们的本质进⾏划分。
Like the number of people in a class, the number of fingers on your hands, or the number of children someone has. You can't have 1.9 children in a family (despite what the ).
就像班上的⼈数，您的⼿指上的⼿指数或某⼈的孩⼦数⼀样。 ⼀个家庭中不能有1.9个孩⼦(尽管 )。
Continuous data, on the other hand, is the opposite. It can be divided up as much as you want, and measured to many decimal places.
另⼀⽅⾯， 连续数据则相反。 可以根据需要将其划分为多个⼩数位。
Like the weight of a car (can be calculated to many decimal places), temperature (32.543 degrees, and so on), or the speed of an airplane.
就像汽车的重量(可以计算到许多⼩数位)，温度(32.543度，等等)或飞机的速度⼀样。
Now for the fun stuff.
现在来看看有趣的东西。
定性数据类型 (Qualitative data types)
名义数据 (Nominal data)
Nominal data are used to label variables without any quantitative value. Common examples include male/female (albeit somewhat outdated), hair color, nationalities, names of people, and so on.
decimal是整数数据类型
标称数据⽤于标记没有任何定量值的变量。 常见的⽰例包括男性/⼥性(尽管有些过时)，头发颜⾊，国籍，姓⽒等等。
In plain English: basically, they're labels (and nominal comes from "name" to help you remember). You have brown hair (or brown eyes). You are American. Your name is Jane.
⽤简单的英语来说：基本上，它们是标签(名义名称来⾃“名称”，以帮助您记住)。 您有⼀头棕⾊的头发(或棕⾊的眼睛) 。 你是美国⼈ 。你的名字叫简 。
Examples:
例⼦：
What color hair do you have?
你有什么颜⾊的头发？
Brown
棕⾊
Blonde
⾦发⼥郎
Black
⿊⾊
Rainbow unicorn
彩虹独⾓兽
What's your nationality?
你是哪个国家的？
American
美国⼈
German
德语
Kenyan
肯尼亚⼈
Japanese
⽇本
Notice that these variables don't overlap. For the purposes of statistics, anyway, you can't have both brown and rainbow unicorn-colored hair. And they're only really related by the main category of which they're a part.
请注意，这些变量不重叠。 ⽆论如何，出于统计⽬的，您不能同时拥有棕⾊和彩虹独⾓兽⾊的头发。 ⽽且它们只是与它们所属的主要类别真正相关。
序数数据 (Ordinal data)
The key with ordinal data is to remember that ordinal sounds like order - and it's the order of the variables which matters. Not so much the differences between those values.
序数数据的关键是要记住序数听起来像顺序-这是重要的变量顺序。 这些值之间的差异不⼤。
Ordinal scales are often used for measures of satisfaction, happiness, and so on. Have you ever taken one of those surveys, like this?
顺序量表通常⽤于满意度，幸福感等的量度。 您是否曾经参加过其中⼀项调查？
"How likely are you to recommend our services to your friends?"
“您向您的朋友推荐我们服务的可能性有多⼤？”
Very likely
很可能
Likely
可能的
Neutral
中性
Unlikely
不太可能
Very unlikely
不太可能
See, we don't really know what the difference is between very unlikely and unlikely - or if it's the same amount of likeliness (or, unlikeliness) as between likely and very likely. But that's ok. We just know that likely is more than neutral and unlikely is more than very unlikely. It's all in the order.
瞧，我们真的不知道极不可能和不太可能之间有什么区别-或可能性与可能性之间是否存在相同的可能性(或可能性)。 但是没关系。 我们只知道，可能性不仅是中⽴的，⽽且可能性是⾮常不可能的。 全部按顺序进⾏。
定量数据类型 (Quantitative data types)
间隔数据 (Interval Data)
Interval data is fun (and useful) because it's concerned with both the order and difference between your variables. This allows you to measure standard deviation and .
间隔数据很有趣(⽽且很有⽤)，因为它与变量的顺序和差异有关。 这使您可以测量标准偏差和 。
Everyone's favorite example of interval data is temperatures in degrees celsius. 20 degrees C is warmer than 10, and the difference between 20 degrees and 10 degrees is 10 degrees. The difference between 10 and 0 is also 10 degrees.
每个⼈最喜欢的时间间隔数据⽰例是摄⽒温度。 20摄⽒度⽐10摄⽒度⾼，⽽20摄⽒度和10摄⽒度之间的差为10摄⽒度。 10与0之间的差也是10度。
If you need help remembering what interval scales are, just think about the meaning of interval: the space between. So not only do you care about the order of variables, but also about the values in between them.
如果需要帮助您记住间隔标度是多少，请考虑⼀下间隔的含义： 之间的间隔。 因此，您不仅关⼼变量的顺序，⽽且关⼼变量之间的值。
There is a little problem with intervals, however: there's no "true zero." A true zero has no value - there is none of that thing -but 0 degrees C definitely has a value: it's quite chilly. You can also have negative numbers.
但是，间隔有⼀个⼩问题：没有“真零”。 真正的零没有任何价值-没有任何东西-但0摄⽒度绝对有⼀个价值：这很冷。 您也可以使⽤负数。
If you don't have a true zero, you can't calculate ratios. This means addition and subtraction work, but division and multiplication don't.
如果没有真正的零，则⽆法计算⽐率。 这意味着加法和减法⼯作，但除法和乘法却没有。
⽐率数据 (Ratio data)
Thank goodness there's ratio data. It solves all our problems.
谢天谢地，这⾥有⽐例数据。 它解决了我们所有的问题。
Ratio data tells us about the order of variables, the differences between them, and they have that absolute zero. Which allows all sorts of calculations and inferences to be performed and drawn.
⽐率数据告诉我们变量的顺序，变量之间的差，并且变量的绝对值为零。 这允许执⾏和绘制各种计算和推论。
Ratio data is very similar interval data, except zero means none. For ratio data, it is not possible to have negative values.
⽐率数据与间隔数据⾮常相似，但零表⽰⽆。 对于⽐率数据，不可能有负值。
For instance, height is ratio data. It is not possible to have negative height. If an object's height is zero, then there is no object. This is different than something like temperature. Both 0 degrees and -5 degrees are completely valid and meaningful temperatures.
例如，⾼度是⽐率数据。 不可能有负⾼度。 如果对象的⾼度为零，则没有对象。 这不同于温度。 0度和-5度都是完全有效且有意义的温度。
Now that you have a basic handle on these data types you should be a bit more ready to tackle that stats exam.
既然您已经掌握了这些数据类型的基本知识，那么您应该准备好应对该统计数据考试了。
标称型数据和数值型数据

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