Statistics 101: Understanding the different type of variables.

统计101：了解变量的不同类型。

As we enter the latter part of the year 2020, it is safe to say that companies utilize data to assist in making business decisions. For example doing exploratory data analysis (EDA) to calculate statistics of where the business stands today, it may include a simple Linear Regression model to predict product prices in 2021. Perhaps it utilizes neither and instead uses clustering to determine relationships between data points. Regardless of how data is utilized, possessing a strong statistics background can only aid in the decision making process as to which approach is taken to best extract, hypothesize, and interpret data.

进入2020年下半年，可以肯定地说，公司利用数据来协助制定业务决策。 例如，进行探索性数据分析(EDA)以计算当前业务状况的统计数据，它可能包括一个简单的线性回归模型来预测2021年的产品价格。也许它既不使用也不用聚类来确定数据点之间的关系。 无论如何利用数据，拥有强大的统计背景都只能帮助决策过程确定采用哪种方法来最佳地提取，假设和解释数据。

With that being said let us start with the very basics of statistics: variables. Variables can be broken down into two different categories. Quantitative (Numerical) and Qualitative (Categorical). Quantitative variables can be further broken down into two subcategories: Continuous and Discrete.

话虽如此，让我们从统计学的最基本基础开始： 变量。 变量可以分为两个不同的类别。 定量(数字)和定性(分类)。 定量变量可以进一步细分为两个子类别： 连续和离散。

Continuous quantitative variable can be defined as a numerical value that may fall within a large range to which one may say “well it could be anything.” Yes I know that may not make sense but lets utilize a few examples: numerical values such as age, weight, height, BMI are examples of continuous quantitative variables. These are examples of numbers that are always changing and may be within an extremely large range. You may be asking “Well age does not seem like it could fall within a range, if someone asked me how old I am I could answer with an exact number.” Well is that true? Remember age is a form of time, in which it is always changing, therefore age is considered a continuous quantitative variable as well.

连续定量变量可以定义为一个数值，该数值可能会落在一个很大的范围内，人们可能会说“好吧，它可以是任何东西”。 是的，我知道这可能没有意义，但让我们举几个例子： 年龄，体重，身高，BMI等数值是连续定量变量的例子。 这些是数字的示例，这些数字总是在变化，并且可能在非常大的范围内。 您可能会问：“如果有人问我年龄多大，我可以用确切的数字回答，似乎年龄不会落在一定范围内。” 那是真的吗？ 请记住，年龄是时间的一种形式，它总是在变化，因此年龄也被视为连续的定量变量。

Discrete is an exact numerical value. When I think of discrete, I think of distinct. I think of an exact number. For example, if I was asked how much I spent today in dollars at the food truck. My response would be a distinct number.

离散是精确的数值。 当我想到离散时，我想到了独特。 我想到一个确切的数字。 例如，如果有人问我今天在食品卡车上花了多少美元。 我的回答是一个不同的数字。

Now let us discuss the categorical/qualitative variable. These variables represent a group of ordered/ranked or non-ordered/ranked set of values. For example utilizing high school class would be an example of categorical/qualitative data. Freshmen, Sophomore, Junior and Senior may be represented as 1 through 4 respectively.

现在让我们讨论分类/定性变量。 这些变量代表一组有序/排名或无序/排名的值。 例如，利用高中课程将是分类/定性数据的一个示例。 新生，大二，大三和大四分别可以代表1至4。

Similar to quantitative numerical variables, qualitative categorical variables also have two subtypes: Ordinal and Nominal. Remember earlier I stated that this type of data may be represented in an order or sequence. That describes Ordinal categorical variables. A great example is on a scale of 1–5 with 5 being the worst pain rank how you feel. Nominal is the opposite of ordinal in which it lacks order or ranking. For example: If an individual is over 18 years old mark the 0 and if the individual is less than 18 mark the number 1. An order or ranking is not present for it to be considered an ordinal quantitative variable.

与定量数值变量相似，定性类别变量也有两个子类型： 序数和标称。 请记住，我之前曾说过，此类数据可以按顺序或顺序表示。 描述了序数分类变量。 一个很好的例子是1–5的评分，其中5是您的感觉最差的疼痛等级。 标称与序数相反，序数缺乏顺序或等级。 例如：如果一个人的年龄超过18岁，则将0标记为数字；如果一个人的年龄小于18，则将数字标记为1。不存在订单或排名，才能将其视为序数定量变量。

To recap: I spoke about two categories of variables and their subclasses. This concept is extremely important when utilizing data science to assist in making hypothesis, and conclusions on data to improve business processes.

回顾一下：我谈到了变量的两类及其子类。 当利用数据科学来帮助进行假设和结论以改善业务流程时，这个概念非常重要。

Thank You for Reading!

感谢您的阅读！

— — — — — — — — — — — — — — — — — —

— — — — — — — — — — — — — — — — — — — —