靜態(tài)變數(shù)和非靜態(tài)變數(shù)

Statistics 101: Understanding the different type of variables.

統(tǒng)計(jì)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(jìn)入2020年下半年，可以肯定地說，公司利用數(shù)據(jù)來協(xié)助制定業(yè)務(wù)決策。 例如，進(jìn)行探索性數(shù)據(jù)分析(EDA)以計(jì)算當(dāng)前業(yè)務(wù)狀況的統(tǒng)計(jì)數(shù)據(jù)，它可能包括一個簡單的線性回歸模型來預(yù)測2021年的產(chǎn)品價格。也許它既不使用也不用聚類來確定數(shù)據(jù)點(diǎn)之間的關(guān)系。 無論如何利用數(shù)據(jù)，擁有強(qiáng)大的統(tǒng)計(jì)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(shè)和解釋數(shù)據(jù)。

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(tǒng)計(jì)學(xué)的最基本基礎(chǔ)開始： 變量。 變量可以分為兩個不同的類別。 定量(數(shù)字)和定性(分類)。 定量變量可以進(jìn)一步細(xì)分為兩個子類別： 連續(xù)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(xù)定量變量可以定義為一個數(shù)值，該數(shù)值可能會落在一個很大的范圍內(nèi)，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(shù)值是連續(xù)定量變量的例子。 這些是數(shù)字的示例，這些數(shù)字總是在變化，并且可能在非常大的范圍內(nèi)。 您可能會問：“如果有人問我年齡多大，我可以用確切的數(shù)字回答，似乎年齡不會落在一定范圍內(nèi)。” 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(xù)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(shù)值。 當(dāng)我想到離散時，我想到了獨(dú)特。 我想到一個確切的數(shù)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(shù)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(xiàn)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(shù)據(jù)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(shù)值變量相似，定性類別變量也有兩個子類型： 序數(shù)和標(biāo)稱。 請記住，我之前曾說過，此類數(shù)據(jù)可以按順序或順序表示。 描述了序數(shù)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(biāo)稱與序數(shù)相反，序數(shù)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(biāo)記為數(shù)字；如果一個人的年齡小于18，則將數(shù)字標(biāo)記為1。不存在訂單或排名，才能將其視為序數(shù)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(dāng)利用數(shù)據(jù)科學(xué)來幫助進(jìn)行假設(shè)和結(jié)論以改善業(yè)務(wù)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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翻譯自: https://medium.com/swlh/statistics-understanding-variables-9eccf1e8338

靜態(tài)變數(shù)和非靜態(tài)變數(shù)

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