Statistics 101: Understanding the different type of variables.

統計101：了解變量的不同類型。

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.

進入2020年下半年，可以肯定地說，公司利用數據來協助制定業務決策。 例如，進行探索性數據分析(EDA)以計算當前業務狀況的統計數據，它可能包括一個簡單的線性回歸模型來預測2021年的產品價格。也許它既不使用也不用聚類來確定數據點之間的關系。 無論如何利用數據，擁有強大的統計背景都只能幫助決策過程確定采用哪種方法來最佳地提取，假設和解釋數據。

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.

話雖如此，讓我們從統計學的最基本基礎開始： 變量。 變量可以分為兩個不同的類別。 定量(數字)和定性(分類)。 定量變量可以進一步細分為兩個子類別： 連續和離散。

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.

連續定量變量可以定義為一個數值，該數值可能會落在一個很大的范圍內，人們可能會說“好吧，它可以是任何東西”。 是的，我知道這可能沒有意義，但讓我們舉幾個例子： 年齡，體重，身高，BMI等數值是連續定量變量的例子。 這些是數字的示例，這些數字總是在變化，并且可能在非常大的范圍內。 您可能會問：“如果有人問我年齡多大，我可以用確切的數字回答，似乎年齡不會落在一定范圍內。” 那是真的嗎？ 請記住，年齡是時間的一種形式，它總是在變化，因此年齡也被視為連續的定量變量。

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.

離散是精確的數值。 當我想到離散時，我想到了獨特。 我想到一個確切的數字。 例如，如果有人問我今天在食品卡車上花了多少美元。 我的回答是一個不同的數字。

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.

現在讓我們討論分類/定性變量。 這些變量代表一組有序/排名或無序/排名的值。 例如，利用高中課程將是分類/定性數據的一個示例。 新生，大二，大三和大四分別可以代表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.

與定量數值變量相似，定性類別變量也有兩個子類型： 序數和標稱。 請記住，我之前曾說過，此類數據可以按順序或順序表示。 描述了序數分類變量。 一個很好的例子是1–5的評分，其中5是您的感覺最差的疼痛等級。 標稱與序數相反，序數缺乏順序或等級。 例如：如果一個人的年齡超過18歲，則將0標記為數字；如果一個人的年齡小于18，則將數字標記為1。不存在訂單或排名，才能將其視為序數定量變量。

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.

回顧一下：我談到了變量的兩類及其子類。 當利用數據科學來幫助進行假設和結論以改善業務流程時，這個概念非常重要。

Thank You for Reading!

感謝您的閱讀！

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