数据分析

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Data analysis is a process of inspecting, cleansing, transforming and modeling data with the goal of discovering useful information, informing conclusions and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.^[1]

Data analysis is a process of inspecting, cleansing, transforming and modeling data with the goal of discovering useful information, informing conclusions and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.

数据分析Data analysis是一个对数据进行检查、清理、转换和建模的过程，其目的是发现有用的信息，为结论提供信息和支持决策。数据分析有多个方面和方法，包含了各种名称下的不同技术，被用于不同的商业、科学和社会科学领域。在当今的商业世界，数据分析在做出更科学的决策和帮助企业更有效地运营方面发挥着重要作用。

Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information.^[2] In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All of the above are varieties of data analysis.

Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All of the above are varieties of data analysis.

数据挖掘是一种特殊的数据分析技术，侧重于统计建模和知识发现，用于预测目的，而不是纯粹的描述目的，而商业智能涵盖了严重依赖于聚合的数据分析，主要侧重于商业信息。在统计应用中，数据分析可以分为描述统计学分析、探索性数据分析和验证性数据分析。Eda 侧重于发现数据中的新特征，而 CDA 侧重于确认或伪造现有的假设。预测分析的重点是应用统计模型进行预测预测或分类，而文本分析则应用统计学、语言学和结构化技术从文本来源中提取和分类信息，这是非结构化数据的一种。以上都是各种各样的数据分析。

Data integration is a precursor to data analysis,模板:According to whom and data analysis is closely linked模板:How to data visualization and data dissemination.

Data integration is a precursor to data analysis, and data analysis is closely linked to data visualization and data dissemination.

数据集成是数据分析的先驱，数据分析与数据可视化和数据传播密切相关。

The process of data analysis

Data science process flowchart from Doing Data Science, by Schutt & O'Neil (2013)

Data science process flowchart from Doing Data Science, by Schutt & O'Neil (2013)

数据科学处理流程图，来自《做数据科学》，Schutt & o’ neil (2013)

Analysis refers to breaking a whole into its separate components for individual examination. Data analysis is a process for obtaining raw data and converting it into information useful for decision-making by users. Data is collected and analyzed to answer questions, test hypotheses or disprove theories.引用错误：没有找到与</ref>对应的<ref>标签

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Statistician John Tukey defined data analysis in 1961 as: "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data."^[3]

Statistician John Tukey defined data analysis in 1961 as: "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data."

统计学家 John Tukey 在1961年将数据分析定义为: ”分析数据的程序，解释这些程序结果的技术，规划数据收集以使其分析更容易、更精确或更准确的方法，以及所有适用于数据分析的(数学)统计的机制和结果

There are several phases that can be distinguished, described below. The phases are iterative, in that feedback from later phases may result in additional work in earlier phases.引用错误：没有找到与</ref>对应的<ref>标签 The CRISP framework used in data mining has similar steps.

| isbn = 978-1-449-35865-5}}</ref> The CRISP framework used in data mining has similar steps.

| isbn 978-1-449-35865-5} / ref 用于数据挖掘的 CRISP 框架有类似的步骤。

Data requirements

The data are necessary as inputs to the analysis, which is specified based upon the requirements of those directing the analysis or customers (who will use the finished product of the analysis). The general type of entity upon which the data will be collected is referred to as an experimental unit (e.g., a person or population of people). Specific variables regarding a population (e.g., age and income) may be specified and obtained. Data may be numerical or categorical (i.e., a text label for numbers).^[4]

The data are necessary as inputs to the analysis, which is specified based upon the requirements of those directing the analysis or customers (who will use the finished product of the analysis). The general type of entity upon which the data will be collected is referred to as an experimental unit (e.g., a person or population of people). Specific variables regarding a population (e.g., age and income) may be specified and obtained. Data may be numerical or categorical (i.e., a text label for numbers).

这些数据作为分析的输入是必要的，分析是基于那些指导分析的人或客户(他们将使用分析的最终产品)的需求而规定的。收集数据的一般实体类型称为试验单位(例如，一个人或一群人)。关于人口的具体变量(例如，年龄和收入)可以指定和获得。数据可以是数字或范畴(例如，数字的文本标签)。

Data collection

Data are collected from a variety of sources. The requirements may be communicated by analysts to custodians of the data, such as information technology personnel within an organization. The data may also be collected from sensors in the environment, such as traffic cameras, satellites, recording devices, etc. It may also be obtained through interviews, downloads from online sources, or reading documentation.^[4]

Data are collected from a variety of sources. The requirements may be communicated by analysts to custodians of the data, such as information technology personnel within an organization. The data may also be collected from sensors in the environment, such as traffic cameras, satellites, recording devices, etc. It may also be obtained through interviews, downloads from online sources, or reading documentation.

数据是从各种来源收集的。需求可以由分析人员传达给数据保管人，例如组织内的信息技术人员。这些数据也可以从环境中的传感器收集，如交通摄像机、卫星、记录设备等。它也可以通过访谈，从网上资源下载，或阅读文档获得。

Data processing

The phases of the intelligence cycle used to convert raw information into actionable intelligence or knowledge are conceptually similar to the phases in data analysis.

[[用于将原始信息转化为可操作的情报或知识的情报周期在概念上类似于数据分析的阶段]

Data initially obtained must be processed or organised for analysis. For instance, these may involve placing data into rows and columns in a table format (i.e., structured data) for further analysis, such as within a spreadsheet or statistical software.^[4]

Data initially obtained must be processed or organised for analysis. For instance, these may involve placing data into rows and columns in a table format (i.e., structured data) for further analysis, such as within a spreadsheet or statistical software.

最初获得的数据必须经过处理或组织以便进行分析。例如，这可能涉及将数据以表格格式(即结构化数据)放置到行和列中，以便进一步分析，例如在电子表格或统计软件中。

Data cleaning

Once processed and organised, the data may be incomplete, contain duplicates, or contain errors. The need for data cleaning will arise from problems in the way that data are entered and stored. Data cleaning is the process of preventing and correcting these errors. Common tasks include record matching, identifying inaccuracy of data, overall quality of existing data, deduplication, and column segmentation.^[5] Such data problems can also be identified through a variety of analytical techniques. For example, with financial information, the totals for particular variables may be compared against separately published numbers believed to be reliable.^[6] Unusual amounts above or below pre-determined thresholds may also be reviewed. There are several types of data cleaning that depend on the type of data such as phone numbers, email addresses, employers etc. Quantitative data methods for outlier detection can be used to get rid of likely incorrectly entered data. Textual data spell checkers can be used to lessen the amount of mistyped words, but it is harder to tell if the words themselves are correct.^[7]

Once processed and organised, the data may be incomplete, contain duplicates, or contain errors. The need for data cleaning will arise from problems in the way that data are entered and stored. Data cleaning is the process of preventing and correcting these errors. Common tasks include record matching, identifying inaccuracy of data, overall quality of existing data, deduplication, and column segmentation. Such data problems can also be identified through a variety of analytical techniques. For example, with financial information, the totals for particular variables may be compared against separately published numbers believed to be reliable. Unusual amounts above or below pre-determined thresholds may also be reviewed. There are several types of data cleaning that depend on the type of data such as phone numbers, email addresses, employers etc. Quantitative data methods for outlier detection can be used to get rid of likely incorrectly entered data. Textual data spell checkers can be used to lessen the amount of mistyped words, but it is harder to tell if the words themselves are correct.

一旦处理和组织，数据可能是不完整的，包含重复的，或包含错误。由于输入和存储数据的方式存在问题，因此需要进行数据清理。数据清理是预防和纠正这些错误的过程。常见的任务包括记录匹配、识别数据的不准确性、现有数据的整体质量、重复数据删除和列分割。这样的数据问题也可以通过各种分析技术来识别。例如，利用财务信息，可以将特定变量的总数与被认为可靠的单独公布的数字进行比较。也可审查超过或低于预先确定阈值的异常数额。有几种类型的数据清理依赖于数据的类型，如电话号码，电子邮件地址，雇主等。异常检测的定量数据方法可以用来去除可能输入错误的数据。文本数据拼写检查器可以用来减少拼写错误的单词数量，但是很难判断这些单词本身是否正确。

Exploratory data analysis

Once the data are cleaned, it can be analyzed. Analysts may apply a variety of techniques referred to as exploratory data analysis to begin understanding the messages contained in the data.^[8]^[9] The process of exploration may result in additional data cleaning or additional requests for data, so these activities may be iterative in nature. Descriptive statistics, such as the average or median, may be generated to help understand the data. Data visualization may also be used to examine the data in graphical format, to obtain additional insight regarding the messages within the data.^[4]

Once the data are cleaned, it can be analyzed. Analysts may apply a variety of techniques referred to as exploratory data analysis to begin understanding the messages contained in the data. The process of exploration may result in additional data cleaning or additional requests for data, so these activities may be iterative in nature. Descriptive statistics, such as the average or median, may be generated to help understand the data. Data visualization may also be used to examine the data in graphical format, to obtain additional insight regarding the messages within the data.

一旦数据被清理，就可以进行分析。分析师可能会运用各种被称为探索性数据分析分析的技术来开始理解包含在数据中的信息。勘探过程可能导致额外的数据清理或额外的数据请求，因此这些活动可能具有迭代性质。描述统计学，例如平均值或中位数，可以用来帮助理解数据。数据可视化还可以用于检查图形格式的数据，以获得关于数据中的消息的额外洞察力。

Modeling and algorithms

Mathematical formulas or models called algorithms may be applied to the data to identify relationships among the variables, such as correlation or causation. In general terms, models may be developed to evaluate a particular variable in the data based on other variable(s) in the data, with some residual error depending on model accuracy (i.e., Data = Model + Error).^[10]

Mathematical formulas or models called algorithms may be applied to the data to identify relationships among the variables, such as correlation or causation. In general terms, models may be developed to evaluate a particular variable in the data based on other variable(s) in the data, with some residual error depending on model accuracy (i.e., Data = Model + Error).

数学公式或称为算法的模型可应用于数据，以识别变量之间的关系，如相关性或因果关系。一般来说，模型可以根据数据中的其他变量来评估数据中的某一特定变量，而一些残差取决于模型的准确性(即数据模型 + 误差)。

Inferential statistics includes techniques to measure relationships between particular variables. For example, regression analysis may be used to model whether a change in advertising (independent variable X) explains the variation in sales (dependent variable Y). In mathematical terms, Y (sales) is a function of X (advertising). It may be described as Y = aX + b + error, where the model is designed such that a and b minimize the error when the model predicts Y for a given range of values of X. Analysts may attempt to build models that are descriptive of the data to simplify analysis and communicate results.^[10]

Inferential statistics includes techniques to measure relationships between particular variables. For example, regression analysis may be used to model whether a change in advertising (independent variable X) explains the variation in sales (dependent variable Y). In mathematical terms, Y (sales) is a function of X (advertising). It may be described as Y = aX + b + error, where the model is designed such that a and b minimize the error when the model predicts Y for a given range of values of X. Analysts may attempt to build models that are descriptive of the data to simplify analysis and communicate results.

推理统计学包括测量特定变量之间关系的技术。例如，回归分析可以用来模拟广告的变化(独立变量 x)是否可以解释销售的变化(因变量 y)。用数学术语来说，y (销售)是 x (广告)的函数。它可以被描述为 y aX + b + 误差，在这种情况下，模型的设计使得 a 和 b 在模型预测给定范围的 x 的 y 时最小化误差。分析人员可能会尝试建立描述数据的模型，以简化分析和传达结果。

Data product

A data product is a computer application that takes data inputs and generates outputs, feeding them back into the environment. It may be based on a model or algorithm. An example is an application that analyzes data about customer purchasing history and recommends other purchases the customer might enjoy.^[4]

A data product is a computer application that takes data inputs and generates outputs, feeding them back into the environment. It may be based on a model or algorithm. An example is an application that analyzes data about customer purchasing history and recommends other purchases the customer might enjoy.

数据产品是一种计算机应用程序，它接收数据输入并生成输出，将它们反馈回环境中。它可能基于一个模型或算法。例如，应用程序分析有关客户购买历史的数据，并推荐客户可能喜欢的其他购买。

Communication

Data visualization to understand the results of a data analysis.^[11]

Data visualization to understand the results of a data analysis.

[了解数据分析结果的数据可视化]

Once the data are analyzed, it may be reported in many formats to the users of the analysis to support their requirements. The users may have feedback, which results in additional analysis. As such, much of the analytical cycle is iterative.^[4]

Once the data are analyzed, it may be reported in many formats to the users of the analysis to support their requirements. The users may have feedback, which results in additional analysis. As such, much of the analytical cycle is iterative.

一旦数据被分析，它可能会以多种格式报告给分析的用户，以支持他们的需求。用户可能会得到反馈，从而导致额外的分析。因此，大部分的分析周期是迭代的。

When determining how to communicate the results, the analyst may consider data visualization techniques to help clearly and efficiently communicate the message to the audience. Data visualization uses information displays (such as tables and charts) to help communicate key messages contained in the data. Tables are helpful to a user who might look up specific numbers, while charts (e.g., bar charts or line charts) may help explain the quantitative messages contained in the data.

在决定如何传达结果的时候，分析师可能会考虑数据可视化技术来帮助清晰有效地向听众传达信息。数据可视化使用信息显示(如表格和图表)来帮助传递包含在数据中的关键信息。表格对查找特定数字的用户很有帮助，而图表(例如柱状图或折线图)可以帮助解释数据中包含的定量信息。

Quantitative messages

A time series illustrated with a line chart demonstrating trends in U.S. federal spending and revenue over time.

一个时间序列图表展示了美国联邦政府开支和收入随时间的变化趋势。

A scatterplot illustrating correlation between two variables (inflation and unemployment) measured at points in time.

散点图说明两个变量(通货膨胀和失业)在时间点上的相关性。

Stephen Few described eight types of quantitative messages that users may attempt to understand or communicate from a set of data and the associated graphs used to help communicate the message. Customers specifying requirements and analysts performing the data analysis may consider these messages during the course of the process.

Stephen Few 描述了用户可能试图从一组数据以及用于帮助传达信息的相关图表中理解或传达的八种定量信息。指定需求的客户和执行数据分析的分析人员可以在流程过程中考虑这些消息。

Time-series: A single variable is captured over a period of time, such as the unemployment rate over a 10-year period. A line chart may be used to demonstrate the trend.

Time-series: A single variable is captured over a period of time, such as the unemployment rate over a 10-year period. A line chart may be used to demonstrate the trend.

时间序列: 在一段时间内捕捉单一变量，如10年期间的失业率。可以用折线图来说明趋势。

Ranking: Categorical subdivisions are ranked in ascending or descending order, such as a ranking of sales performance (the measure) by sales persons (the category, with each sales person a categorical subdivision) during a single period. A bar chart may be used to show the comparison across the sales persons.

Ranking: Categorical subdivisions are ranked in ascending or descending order, such as a ranking of sales performance (the measure) by sales persons (the category, with each sales person a categorical subdivision) during a single period. A bar chart may be used to show the comparison across the sales persons.

排名: 按升序或降序对分类细分进行排名，例如按销售人员(类别，每个销售人员都有一个分类细分)对一个时期内的销售业绩进行排名。条形图可以用来显示销售人员之间的比较。

Part-to-whole: Categorical subdivisions are measured as a ratio to the whole (i.e., a percentage out of 100%). A pie chart or bar chart can show the comparison of ratios, such as the market share represented by competitors in a market.

Part-to-whole: Categorical subdivisions are measured as a ratio to the whole (i.e., a percentage out of 100%). A pie chart or bar chart can show the comparison of ratios, such as the market share represented by competitors in a market.

部分对整体: 分类细分是以整体的比例来衡量的(即100% 的百分比)。饼图或条形图可以显示比率的比较，例如市场中竞争对手所占的市场份额。

Deviation: Categorical subdivisions are compared against a reference, such as a comparison of actual vs. budget expenses for several departments of a business for a given time period. A bar chart can show comparison of the actual versus the reference amount.

Deviation: Categorical subdivisions are compared against a reference, such as a comparison of actual vs. budget expenses for several departments of a business for a given time period. A bar chart can show comparison of the actual versus the reference amount.

偏差: 将分类细分与参考数据进行比较，例如对一个企业的几个部门在给定时间内的实际支出与预算支出进行比较。条形图可以显示实际金额与参考金额的比较。

Frequency distribution: Shows the number of observations of a particular variable for given interval, such as the number of years in which the stock market return is between intervals such as 0–10%, 11–20%, etc. A histogram, a type of bar chart, may be used for this analysis.

Frequency distribution: Shows the number of observations of a particular variable for given interval, such as the number of years in which the stock market return is between intervals such as 0–10%, 11–20%, etc. A histogram, a type of bar chart, may be used for this analysis.

频率分布: 显示特定变量在给定时间间隔内的观测数量，例如股票市场回报率在0-10% 、11-20% 等时间间隔内的年数。直方图，一种条形图，可以用来进行这种分析。

Correlation: Comparison between observations represented by two variables (X,Y) to determine if they tend to move in the same or opposite directions. For example, plotting unemployment (X) and inflation (Y) for a sample of months. A scatter plot is typically used for this message.

Correlation: Comparison between observations represented by two variables (X,Y) to determine if they tend to move in the same or opposite directions. For example, plotting unemployment (X) and inflation (Y) for a sample of months. A scatter plot is typically used for this message.

相关性: 用两个变量(x，y)表示的观测值之间的比较，以确定它们是否倾向于朝相同或相反的方向移动。例如，绘制个月的样本失业率(x)和通货膨胀率(y)。此消息通常使用散点图。

Nominal comparison: Comparing categorical subdivisions in no particular order, such as the sales volume by product code. A bar chart may be used for this comparison.

Nominal comparison: Comparing categorical subdivisions in no particular order, such as the sales volume by product code. A bar chart may be used for this comparison.

名义上的比较: 比较分类细分，没有特定的顺序，例如按产品代码的销售量。条形图可用于这种比较。

Geographic or geospatial: Comparison of a variable across a map or layout, such as the unemployment rate by state or the number of persons on the various floors of a building. A cartogram is a typical graphic used.^[12]^[13]

Geographic or geospatial: Comparison of a variable across a map or layout, such as the unemployment rate by state or the number of persons on the various floors of a building. A cartogram is a typical graphic used.

地理或地理空间: 在地图或布局中对一个变量的比较，例如按州分列的失业率或建筑物各层的人数。地图是一种典型的图形。

Techniques for analyzing quantitative data

Author Jonathan Koomey has recommended a series of best practices for understanding quantitative data. These include:

作者乔纳森 · 库米推荐了一系列理解定量数据的最佳实践。其中包括:

Check raw data for anomalies prior to performing an analysis;

Re-perform important calculations, such as verifying columns of data that are formula driven;

Confirm main totals are the sum of subtotals;

Check relationships between numbers that should be related in a predictable way, such as ratios over time;

Normalize numbers to make comparisons easier, such as analyzing amounts per person or relative to GDP or as an index value relative to a base year;

Break problems into component parts by analyzing factors that led to the results, such as DuPont analysis of return on equity.^[6]

For the variables under examination, analysts typically obtain descriptive statistics for them, such as the mean (average), median, and standard deviation. They may also analyze the distribution of the key variables to see how the individual values cluster around the mean.

对于被调查的变量，分析师通常会得到它们的描述统计学，比如平均值、中位数和标准差。他们还可以分析关键变量的分布情况，看看各个值是如何围绕平均值聚集的。

An illustration of the MECE principle used for data analysis.

The consultants at McKinsey and Company named a technique for breaking a quantitative problem down into its component parts called the MECE principle. Each layer can be broken down into its components; each of the sub-components must be mutually exclusive of each other and collectively add up to the layer above them. The relationship is referred to as "Mutually Exclusive and Collectively Exhaustive" or MECE. For example, profit by definition can be broken down into total revenue and total cost. In turn, total revenue can be analyzed by its components, such as revenue of divisions A, B, and C (which are mutually exclusive of each other) and should add to the total revenue (collectively exhaustive).

An illustration of the MECE principle used for data analysis. The consultants at McKinsey and Company named a technique for breaking a quantitative problem down into its component parts called the MECE principle. Each layer can be broken down into its components; each of the sub-components must be mutually exclusive of each other and collectively add up to the layer above them. The relationship is referred to as "Mutually Exclusive and Collectively Exhaustive" or MECE. For example, profit by definition can be broken down into total revenue and total cost. In turn, total revenue can be analyzed by its components, such as revenue of divisions A, B, and C (which are mutually exclusive of each other) and should add to the total revenue (collectively exhaustive).

对[[用于数据分析的 MECE 原理]的说明麦肯锡咨询公司的顾问们提出了一种将定量问题分解为其组成部分的技术，称为 MECE 原理。每一层都可以分解成它的组件; 每一个子组件必须相互排斥，共同构成它们上面的层。这种关系被称为“相互排斥和集体详尽”或 MECE。例如，根据定义，利润可以分为总收入和总成本。反过来，总收入可以通过其组成部分进行分析，如部门 a、 b 和 c 的收入(它们相互排斥) ，并且应该增加总收入(总体上详尽无遗)。

Analysts may use robust statistical measurements to solve certain analytical problems. Hypothesis testing is used when a particular hypothesis about the true state of affairs is made by the analyst and data is gathered to determine whether that state of affairs is true or false. For example, the hypothesis might be that "Unemployment has no effect on inflation", which relates to an economics concept called the Phillips Curve. Hypothesis testing involves considering the likelihood of Type I and type II errors, which relate to whether the data supports accepting or rejecting the hypothesis.

分析师可能会使用强有力的统计测量来解决某些分析问题。当分析师对事件的真实状态做出特定假设并收集数据以确定事件的真实状态时，就使用假设检验。例如，假设可能是“失业对通货膨胀没有影响” ，这与一个叫做菲利普斯曲线的经济学概念有关。假设检验包括考虑第一类和第二类错误的可能性，这些错误与数据是否支持接受或拒绝假设有关。

Regression analysis may be used when the analyst is trying to determine the extent to which independent variable X affects dependent variable Y (e.g., "To what extent do changes in the unemployment rate (X) affect the inflation rate (Y)?"). This is an attempt to model or fit an equation line or curve to the data, such that Y is a function of X.

当分析师试图确定自变量 x 对因变量 y 的影响程度时，可以使用回归分析分析法(例如，“失业率(x)的变化对通货膨胀率(y)的影响程度”) .这是一个试图模型或拟合一个方程线或曲线的数据，这样 y 是一个函数的 x。

Necessary condition analysis (NCA) may be used when the analyst is trying to determine the extent to which independent variable X allows variable Y (e.g., "To what extent is a certain unemployment rate (X) necessary for a certain inflation rate (Y)?"). Whereas (multiple) regression analysis uses additive logic where each X-variable can produce the outcome and the X's can compensate for each other (they are sufficient but not necessary), necessary condition analysis (NCA) uses necessity logic, where one or more X-variables allow the outcome to exist, but may not produce it (they are necessary but not sufficient). Each single necessary condition must be present and compensation is not possible.

当分析师试图确定自变量 x 在多大程度上允许变量 y 时，可以使用 https://www.erim.eur.nl/centres/Necessary-condition-analysis/ 必要条件分析(NCA)(例如，“在多大程度上某个失业率(x)对某个通货膨胀率(y)是必要的? ”) .然而(多重)回归分析分析使用附加逻辑，其中每个 x 变量可以产生结果，x 可以相互补偿(他们是充分的，但不是必要的) ，必要条件分析(NCA)使用必要逻辑，其中一个或多个 x 变量允许结果存在，但可能不产生它(他们是必要的，但不是充分的)。每一个单一的必要条件必须存在，补偿是不可能的。

Analytical activities of data users

Users may have particular data points of interest within a data set, as opposed to general messaging outlined above. Such low-level user analytic activities are presented in the following table. The taxonomy can also be organized by three poles of activities: retrieving values, finding data points, and arranging data points.^[14]^[15]^[16]^[17]

Users may have particular data points of interest within a data set, as opposed to general messaging outlined above. Such low-level user analytic activities are presented in the following table. The taxonomy can also be organized by three poles of activities: retrieving values, finding data points, and arranging data points.

与上面概述的一般消息传递相反，用户可能对数据集中的特定数据点感兴趣。下表介绍了这种低层次的用户分析活动。分类还可以由三个活动极点组织: 检索值、查找数据点和排列数据点。

{ | 类“ wikitable”边框“1” - How long is the movie Gone with the Wind? - How long is the movie Gone with the Wind? 电影《乱世佳人》有多长？ - What comedies have won awards? - What comedies have won awards? - 哪些喜剧获奖？ - Which funds underperformed the SP-500? - Which funds underperformed the SP-500? 哪些基金表现不如 SP-500？ - Rank the cereals by calories. - Rank the cereals by calories. - 按卡路里排列谷物。 - What is the range of car horsepowers? - What is the range of car horsepowers? 汽车的马力范围是多少？ - What actresses are in the data set? - What actresses are in the data set? 数据库里有哪些女演员？ - What is the age distribution of shoppers? - What is the age distribution of shoppers? 购物者的年龄分布情况如何？ - Are there any outliers in protein? - Are there any outliers in protein? 蛋白质中是否有异常值？ - Is there a cluster of typical film lengths? - Is there a cluster of typical film lengths? 是否有一组典型的胶片长度？ - Is there a correlation between country of origin and MPG? - Is there a correlation between country of origin and MPG? 起源国和 MPG 之间有联系吗？ - Do different genders have a preferred payment method? - Do different genders have a preferred payment method? - 不同性别是否有首选的付款方式？ - Is there a trend of increasing film length over the years? - Is there a trend of increasing film length over the years? - 电影长度是否有逐年增加的趋势？

#	Task	General Description	Pro Forma Abstract	Examples	#	Task	General Description	Pro Forma Abstract	Examples	任务! ！一般介绍！形式 / 摘要！宽度“35% ” \| 例子
1	1 对齐”中心”	Retrieve Value	Retrieve Value	检索价值	Given a set of specific cases, find attributes of those cases.	Given a set of specific cases, find attributes of those cases. 给定一组特定的案例，找出这些案例的属性。	What are the values of attributes {X, Y, Z, ...} in the data cases {A, B, C, ...}?	What are the values of attributes {X, Y, Z, ...} in the data cases {A, B, C, ...}?	数据案例{ a，b，c，... }中属性{ x，y，z，... }的值是什么？	- What is the mileage per gallon of the Ford Mondeo?	- What is the mileage per gallon of the Ford Mondeo?
2	2 对齐“ center” \| 2	Filter	Filter 滤镜	Given some concrete conditions on attribute values, find data cases satisfying those conditions.	Given some concrete conditions on attribute values, find data cases satisfying those conditions. 给定属性值的一些具体条件，找出满足这些条件的数据案例。	Which data cases satisfy conditions {A, B, C...}?	Which data cases satisfy conditions {A, B, C...}?	哪些数据案例满足条件{ a，b，c. . . } ？	- What Kellogg's cereals have high fiber?	- What Kellogg's cereals have high fiber?
3	3 对齐中心	Compute Derived Value	Compute Derived Value	计算派生值	Given a set of data cases, compute an aggregate numeric representation of those data cases.	Given a set of data cases, compute an aggregate numeric representation of those data cases.	给定一组数据用例，计算这些数据用例的聚合数值表示形式。	What is the value of aggregation function F over a given set S of data cases?	What is the value of aggregation function F over a given set S of data cases?	聚合函数 f 在给定数据集 s 上的值是多少？	- What is the average calorie content of Post cereals?	- What is the average calorie content of Post cereals? 波斯特谷物的平均热量是多少？ - What is the gross income of all stores combined? - What is the gross income of all stores combined? 所有商店的总收入是多少？ - How many manufacturers of cars are there? - How many manufacturers of cars are there? 有多少汽车制造商？
4	4 对齐”中心”	Find Extremum	Find Extremum	寻找极端情况	Find data cases possessing an extreme value of an attribute over its range within the data set.	Find data cases possessing an extreme value of an attribute over its range within the data set.	查找数据集中具有属性在其范围内的极值的数据案例。	What are the top/bottom N data cases with respect to attribute A?	What are the top/bottom N data cases with respect to attribute A?	关于属性 a 的顶部 / 底部 n 个数据用例是什么？	- What is the car with the highest MPG?	- What is the car with the highest MPG? 什么是最高 MPG 的汽车？ - What director/film has won the most awards? - What director/film has won the most awards? - 哪部导演 / 电影获奖最多？ - What Marvel Studios film has the most recent release date? - What Marvel Studios film has the most recent release date? - 漫威电影公司的哪部电影最近上映日期？
5	5 对齐”中心”	Sort	Sort	排序	Given a set of data cases, rank them according to some ordinal metric.	Given a set of data cases, rank them according to some ordinal metric. 给定一组数据案例，根据某种序数度量对它们进行排序。	What is the sorted order of a set S of data cases according to their value of attribute A?	What is the sorted order of a set S of data cases according to their value of attribute A?	根据属性 a 的值，一组数据案例的排序顺序是什么？	- Order the cars by weight.	- Order the cars by weight.
6	6 对齐”中心”	Determine Range	Determine Range	确定范围	Given a set of data cases and an attribute of interest, find the span of values within the set.	Given a set of data cases and an attribute of interest, find the span of values within the set.	给定一组数据用例和一个感兴趣的属性，查找该组中的值的范围。	What is the range of values of attribute A in a set S of data cases?	What is the range of values of attribute A in a set S of data cases?	在一组数据案例中，属性 a 的值范围是多少？	- What is the range of film lengths?	- What is the range of film lengths?
7	7 对齐”中心”	Characterize Distribution	Characterize Distribution	特征分布	Given a set of data cases and a quantitative attribute of interest, characterize the distribution of that attribute's values over the set.	Given a set of data cases and a quantitative attribute of interest, characterize the distribution of that attribute's values over the set.	给定一组数据用例和一个感兴趣的数量属性，刻画该属性值在该集上的分布情况。	What is the distribution of values of attribute A in a set S of data cases?	What is the distribution of values of attribute A in a set S of data cases?	属性 a 的值在一组数据案例中的分布情况如何？	- What is the distribution of carbohydrates in cereals?	- What is the distribution of carbohydrates in cereals?
8	8 对齐“ center” \| 8	Find Anomalies	Find Anomalies 寻找异常点	Identify any anomalies within a given set of data cases with respect to a given relationship or expectation, e.g. statistical outliers.	Identify any anomalies within a given set of data cases with respect to a given relationship or expectation, e.g. statistical outliers. 识别给定数据集中与给定关系或期望有关的任何异常，例如:。统计异常值。	Which data cases in a set S of data cases have unexpected/exceptional values?	Which data cases in a set S of data cases have unexpected/exceptional values?	在一组数据情况中，哪些数据情况具有意外 / 异常值？	- Are there exceptions to the relationship between horsepower and acceleration?	- Are there exceptions to the relationship between horsepower and acceleration?
9	9 对齐”中心”	Cluster	Cluster	Cluster	Given a set of data cases, find clusters of similar attribute values.	Given a set of data cases, find clusters of similar attribute values.	给定一组数据用例，找出相似属性值的集群。	Which data cases in a set S of data cases are similar in value for attributes {X, Y, Z, ...}?	Which data cases in a set S of data cases are similar in value for attributes {X, Y, Z, ...}?	一组数据用例中的哪些数据用例在属性{ x，y，z，... }的值上相似？	- Are there groups of cereals w/ similar fat/calories/sugar?	- Are there groups of cereals w/ similar fat/calories/sugar?
10	10 对齐中心	Correlate	Correlate	Correlate	Given a set of data cases and two attributes, determine useful relationships between the values of those attributes.	Given a set of data cases and two attributes, determine useful relationships between the values of those attributes.	给定一组数据用例和两个属性，确定这些属性值之间的有用关系。	What is the correlation between attributes X and Y over a given set S of data cases?	What is the correlation between attributes X and Y over a given set S of data cases?	在给定的数据案例集 s 中，属性 x 和 y 之间的相关性是什么？	- Is there a correlation between carbohydrates and fat?	- Is there a correlation between carbohydrates and fat?
11	11 11点，中心对齐	Contextualization^[17]	Contextualization	语境化	Given a set of data cases, find contextual relevancy of the data to the users.	Given a set of data cases, find contextual relevancy of the data to the users.	给定一组数据案例，找出数据与用户上下文的相关性。	Which data cases in a set S of data cases are relevant to the current users' context?	Which data cases in a set S of data cases are relevant to the current users' context?	一组数据用例中的哪些数据用例与当前用户的上下文相关？	- Are there groups of restaurants that have foods based on my current caloric intake?	- Are there groups of restaurants that have foods based on my current caloric intake?

|}

Barriers to effective analysis

Barriers to effective analysis may exist among the analysts performing the data analysis or among the audience. Distinguishing fact from opinion, cognitive biases, and innumeracy are all challenges to sound data analysis.

进行数据分析的分析人员之间或受众之间可能存在有效分析的障碍。区分事实与观点、认知偏见和数学盲都是对完善数据分析的挑战。

Confusing fact and opinion

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Effective analysis requires obtaining relevant facts to answer questions, support a conclusion or formal opinion, or test hypotheses. Facts by definition are irrefutable, meaning that any person involved in the analysis should be able to agree upon them. For example, in August 2010, the Congressional Budget Office (CBO) estimated that extending the Bush tax cuts of 2001 and 2003 for the 2011–2020 time period would add approximately $3.3 trillion to the national debt.^[18] Everyone should be able to agree that indeed this is what CBO reported; they can all examine the report. This makes it a fact. Whether persons agree or disagree with the CBO is their own opinion.

Effective analysis requires obtaining relevant facts to answer questions, support a conclusion or formal opinion, or test hypotheses. Facts by definition are irrefutable, meaning that any person involved in the analysis should be able to agree upon them. For example, in August 2010, the Congressional Budget Office (CBO) estimated that extending the Bush tax cuts of 2001 and 2003 for the 2011–2020 time period would add approximately $3.3 trillion to the national debt. Everyone should be able to agree that indeed this is what CBO reported; they can all examine the report. This makes it a fact. Whether persons agree or disagree with the CBO is their own opinion.

有效的分析需要获得相关的事实来回答问题，支持结论或正式的意见，或者检验假设。事实的定义是不可辩驳的，这意味着任何参与分析的人都应该能够同意它们。例如，2010年8月，国会预算办公室(CBO)估计，延长布什2001年和2003年的2011-2020年减税政策将使国家债务增加约3.3万亿美元。每个人都应该能够同意，这确实是国会预算办公室报告的; 他们都可以检查报告。这使它成为一个事实。人们是否同意国会预算办公室是他们自己的观点。

As another example, the auditor of a public company must arrive at a formal opinion on whether financial statements of publicly traded corporations are "fairly stated, in all material respects." This requires extensive analysis of factual data and evidence to support their opinion. When making the leap from facts to opinions, there is always the possibility that the opinion is erroneous.

另一个例子是，上市公司的审计师必须就上市公司的财务报表是否”在所有重大方面得到公允陈述”达成正式意见这需要对事实数据和证据进行广泛的分析，以支持他们的观点。在从事实到观点的飞跃中，总是存在着观点错误的可能性。

Cognitive biases

There are a variety of cognitive biases that can adversely affect analysis. For example, confirmation bias is the tendency to search for or interpret information in a way that confirms one's preconceptions. In addition, individuals may discredit information that does not support their views.

有各种各样的认知偏差会对分析产生不利影响。例如，确认性偏见是指人们倾向于以确认自己先入之见的方式来寻找或解释信息。此外，个人可能会怀疑那些不支持他们观点的信息。

Analysts may be trained specifically to be aware of these biases and how to overcome them. In his book Psychology of Intelligence Analysis, retired CIA analyst Richards Heuer wrote that analysts should clearly delineate their assumptions and chains of inference and specify the degree and source of the uncertainty involved in the conclusions. He emphasized procedures to help surface and debate alternative points of view.^[19]

Analysts may be trained specifically to be aware of these biases and how to overcome them. In his book Psychology of Intelligence Analysis, retired CIA analyst Richards Heuer wrote that analysts should clearly delineate their assumptions and chains of inference and specify the degree and source of the uncertainty involved in the conclusions. He emphasized procedures to help surface and debate alternative points of view.

分析师可能会接受专门培训，以了解这些偏见以及如何克服这些偏见。退休的中央情报局分析师理查德 · 霍伊尔在他的《情报分析心理学》一书中写道，分析师应该清楚地描述他们的假设和推断链，明确结论中包含的不确定性的程度和来源。他强调有助于提出和辩论不同观点的程序。

Innumeracy

Effective analysts are generally adept with a variety of numerical techniques. However, audiences may not have such literacy with numbers or numeracy; they are said to be innumerate. Persons communicating the data may also be attempting to mislead or misinform, deliberately using bad numerical techniques.^[20]

Effective analysts are generally adept with a variety of numerical techniques. However, audiences may not have such literacy with numbers or numeracy; they are said to be innumerate. Persons communicating the data may also be attempting to mislead or misinform, deliberately using bad numerical techniques.

高效的分析师通常善于使用各种数值技巧。然而，观众可能没有数字和算术这样的读写能力; 他们被认为是不识数的。传递数据的人也可能试图误导或误导，故意使用糟糕的数字技术。

For example, whether a number is rising or falling may not be the key factor. More important may be the number relative to another number, such as the size of government revenue or spending relative to the size of the economy (GDP) or the amount of cost relative to revenue in corporate financial statements. This numerical technique is referred to as normalization^[6] or common-sizing. There are many such techniques employed by analysts, whether adjusting for inflation (i.e., comparing real vs. nominal data) or considering population increases, demographics, etc. Analysts apply a variety of techniques to address the various quantitative messages described in the section above.

For example, whether a number is rising or falling may not be the key factor. More important may be the number relative to another number, such as the size of government revenue or spending relative to the size of the economy (GDP) or the amount of cost relative to revenue in corporate financial statements. This numerical technique is referred to as normalization or common-sizing. There are many such techniques employed by analysts, whether adjusting for inflation (i.e., comparing real vs. nominal data) or considering population increases, demographics, etc. Analysts apply a variety of techniques to address the various quantitative messages described in the section above.

例如，一个数字是上升还是下降可能不是关键因素。更重要的可能是相对于另一个数字的数字，例如相对于经济规模(国内生产总值)的政府收入或支出规模，或者相对于公司财务报表中的收入的成本金额。这种数值技术称为归一化或通用尺寸。分析师们使用了许多这样的技术，无论是对通货膨胀进行调整(比如，比较实际数据与名义数据) ，还是考虑人口增长、人口统计等等。分析人员应用各种技术来处理上面一节中描述的各种定量信息。

Analysts may also analyze data under different assumptions or scenarios. For example, when analysts perform financial statement analysis, they will often recast the financial statements under different assumptions to help arrive at an estimate of future cash flow, which they then discount to present value based on some interest rate, to determine the valuation of the company or its stock. Similarly, the CBO analyzes the effects of various policy options on the government's revenue, outlays and deficits, creating alternative future scenarios for key measures.

分析师也可能在不同的假设或情景下分析数据。例如，当分析师进行财务报表分析时，他们通常会根据不同的假设重新编制财务报表，以帮助得出对未来现金流量的估计，然后根据一定的利率贴现现值，以确定公司或其股票的估值。同样，国会预算办公室分析了各种政策选择对政府收入、支出和赤字的影响，为关键措施创造了可供选择的未来情景。

Practitioner notes

This section contains rather technical explanations that may assist practitioners but are beyond the typical scope of a Wikipedia article.

这个部分包含了一些技术性的解释，可能对从业者有所帮助，但是超出了维基百科文章的典型范围。

Initial data analysis

The most important distinction between the initial data analysis phase and the main analysis phase, is that during initial data analysis one refrains from any analysis that is aimed at answering the original research question. The initial data analysis phase is guided by the following four questions:模板:Sfn

The most important distinction between the initial data analysis phase and the main analysis phase, is that during initial data analysis one refrains from any analysis that is aimed at answering the original research question. The initial data analysis phase is guided by the following four questions:

在初始数据分析阶段和主要分析阶段之间最重要的区别是，在初始数据分析阶段，人们不进行任何旨在回答原始研究问题的分析。初始数据分析阶段以下列四个问题为指导:

Quality of data

The quality of the data should be checked as early as possible. Data quality can be assessed in several ways, using different types of analysis: frequency counts, descriptive statistics (mean, standard deviation, median), normality (skewness, kurtosis, frequency histograms), n: variables are compared with coding schemes of variables external to the data set, and possibly corrected if coding schemes are not comparable.

应尽早检查数据的质量。数据质量可以通过几种方式进行评估，使用不同类型的分析: 频率计数、描述统计学(平均值、标准差、中位数)、正态性(偏态、峰度、频率直方图)、 n: 变量与数据集外部变量的编码方案进行比较，如果编码方案不具有可比性，则可能进行修正。

Test for common-method variance.

The choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase.模板:Sfn

The choice of analyses to assess the data quality during the initial data analysis phase depends on the analyses that will be conducted in the main analysis phase.

在初始数据分析阶段评估数据质量的分析的选择取决于将在主要分析阶段进行的分析。

Quality of measurements

The quality of the measurement instruments should only be checked during the initial data analysis phase when this is not the focus or research question of the study. One should check whether structure of measurement instruments corresponds to structure reported in the literature.

测量仪器的质量只能在初始数据分析阶段进行检验，这不是本研究的重点或研究问题。应该检查测量仪器的结构是否符合文献报道的结构。

There are two ways to assess measurement: [NOTE: only one way seems to be listed]

有两种方法来评估测量: [注意: 似乎只列出了一种方法]

Analysis of homogeneity (internal consistency), which gives an indication of the reliability of a measurement instrument. During this analysis, one inspects the variances of the items and the scales, the Cronbach's α of the scales, and the change in the Cronbach's alpha when an item would be deleted from a scale模板:Sfn

Initial transformations

After assessing the quality of the data and of the measurements, one might decide to impute missing data, or to perform initial transformations of one or more variables, although this can also be done during the main analysis phase.模板:Sfn

After assessing the quality of the data and of the measurements, one might decide to impute missing data, or to perform initial transformations of one or more variables, although this can also be done during the main analysis phase.

在对数据和测量数据的质量进行评估之后，人们可能会决定填补缺失的数据，或者对一个或多个变量进行初始转换，尽管这也可以在主要分析阶段进行。 Br /

Possible transformations of variables are:^[23]

Possible transformations of variables are:

变量的可能转换如下:

Square root transformation (if the distribution differs moderately from normal)

Log-transformation (if the distribution differs substantially from normal)

Inverse transformation (if the distribution differs severely from normal)

Make categorical (ordinal / dichotomous) (if the distribution differs severely from normal, and no transformations help)

Did the implementation of the study fulfill the intentions of the research design?

One should check the success of the randomization procedure, for instance by checking whether background and substantive variables are equally distributed within and across groups.

人们应该检查随机化程序是否成功，例如通过检查背景变量和实质变量是否在组内和组间均匀分布。Br /

If the study did not need or use a randomization procedure, one should check the success of the non-random sampling, for instance by checking whether all subgroups of the population of interest are represented in sample.

如果研究不需要或不使用随机化程序，则应检查非随机抽样的成功与否，例如通过检查样本中是否代表了相关人口的所有分组。 Br /

Other possible data distortions that should be checked are:

应该检查的其他可能的数据扭曲有:

dropout (this should be identified during the initial data analysis phase)

Item nonresponse (whether this is random or not should be assessed during the initial data analysis phase)

Treatment quality (using manipulation checks).模板:Sfn

Characteristics of data sample

In any report or article, the structure of the sample must be accurately described. It is especially important to exactly determine the structure of the sample (and specifically the size of the subgroups) when subgroup analyses will be performed during the main analysis phase.

在任何报告或文章中，样品的结构必须被准确描述。在主要分析阶段进行子群分析时，准确确定样本的结构(特别是子群的大小)尤为重要。 Br /

The characteristics of the data sample can be assessed by looking at:

数据样本的特征可以通过以下方式进行评估:

Basic statistics of important variables

Scatter plots

Correlations and associations

Cross-tabulations模板:Sfn

Final stage of the initial data analysis

During the final stage, the findings of the initial data analysis are documented, and necessary, preferable, and possible corrective actions are taken.

在最后阶段，记录初始数据分析的结果，并采取必要的、可取的和可能的纠正措施。 Br /

Also, the original plan for the main data analyses can and should be specified in more detail or rewritten.
In order to do this, several decisions about the main data analyses can and should be made:

此外，主要数据分析的原始计划可以而且应该更详细地说明或重写。为了做到这一点，可以而且应该对主要数据分析作出以下几个决定:

In the case of non-normals: should one transform variables; make variables categorical (ordinal/dichotomous); adapt the analysis method?

In the case of missing data: should one neglect or impute the missing data; which imputation technique should be used?

In the case of outliers: should one use robust analysis techniques?

In case items do not fit the scale: should one adapt the measurement instrument by omitting items, or rather ensure comparability with other (uses of the) measurement instrument(s)?

In the case of (too) small subgroups: should one drop the hypothesis about inter-group differences, or use small sample techniques, like exact tests or bootstrapping?

In case the randomization procedure seems to be defective: can and should one calculate propensity scores and include them as covariates in the main analyses?模板:Sfn

Analysis

Several analyses can be used during the initial data analysis phase:模板:Sfn

Several analyses can be used during the initial data analysis phase:

在初始数据分析阶段可以使用以下几种分析:

Univariate statistics (single variable)

Bivariate associations (correlations)

Graphical techniques (scatter plots)

It is important to take the measurement levels of the variables into account for the analyses, as special statistical techniques are available for each level:模板:Sfn

It is important to take the measurement levels of the variables into account for the analyses, as special statistical techniques are available for each level:

在进行分析时必须考虑到变量的衡量水平，因为每个水平都有专门的统计技术:

Nominal and ordinal variables

- Frequency counts (numbers and percentages)

- Associations

- - circumambulations (crosstabulations)

- - hierarchical loglinear analysis (restricted to a maximum of 8 variables)

- - loglinear analysis (to identify relevant/important variables and possible confounders)

- Exact tests or bootstrapping (in case subgroups are small)

- Computation of new variables

Continuous variables

- Distribution

- - Statistics (M, SD, variance, skewness, kurtosis)

- - Stem-and-leaf displays

- - Box plots

Nonlinear analysis

Nonlinear analysis is often necessary when the data is recorded from a nonlinear system. Nonlinear systems can exhibit complex dynamic effects including bifurcations, chaos, harmonics and subharmonics that cannot be analyzed using simple linear methods. Nonlinear data analysis is closely related to nonlinear system identification.^[24]

Nonlinear analysis is often necessary when the data is recorded from a nonlinear system. Nonlinear systems can exhibit complex dynamic effects including bifurcations, chaos, harmonics and subharmonics that cannot be analyzed using simple linear methods. Nonlinear data analysis is closely related to nonlinear system identification.

非线性分析通常是必要的时候，数据是记录从一个非线性。非线性系统可以表现出复杂的动力学效应，包括分岔、混沌、谐波和次谐波，这些效应不能用简单的线性方法进行分析。非线性数据分析与非线性系统辨识密切相关。

Main data analysis

In the main analysis phase analyses aimed at answering the research question are performed as well as any other relevant analysis needed to write the first draft of the research report.模板:Sfn

In the main analysis phase analyses aimed at answering the research question are performed as well as any other relevant analysis needed to write the first draft of the research report.

在主要分析阶段，进行了旨在回答研究问题的分析，以及撰写研究报告初稿所需的其他相关分析。

Exploratory and confirmatory approaches

In the main analysis phase either an exploratory or confirmatory approach can be adopted. Usually the approach is decided before data is collected. In an exploratory analysis no clear hypothesis is stated before analysing the data, and the data is searched for models that describe the data well. In a confirmatory analysis clear hypotheses about the data are tested.

在主要的分析阶段，可以采用探索性或验证性的方法。通常这种方法是在收集数据之前决定的。在探索性分析中，在分析数据之前没有明确的假设，而是搜索能够很好地描述数据的模型。在验证性分析中，对数据进行了明确的假设检验。

Exploratory data analysis should be interpreted carefully. When testing multiple models at once there is a high chance on finding at least one of them to be significant, but this can be due to a type 1 error. It is important to always adjust the significance level when testing multiple models with, for example, a Bonferroni correction. Also, one should not follow up an exploratory analysis with a confirmatory analysis in the same dataset. An exploratory analysis is used to find ideas for a theory, but not to test that theory as well. When a model is found exploratory in a dataset, then following up that analysis with a confirmatory analysis in the same dataset could simply mean that the results of the confirmatory analysis are due to the same type 1 error that resulted in the exploratory model in the first place. The confirmatory analysis therefore will not be more informative than the original exploratory analysis.模板:Sfn

Exploratory data analysis should be interpreted carefully. When testing multiple models at once there is a high chance on finding at least one of them to be significant, but this can be due to a type 1 error. It is important to always adjust the significance level when testing multiple models with, for example, a Bonferroni correction. Also, one should not follow up an exploratory analysis with a confirmatory analysis in the same dataset. An exploratory analysis is used to find ideas for a theory, but not to test that theory as well. When a model is found exploratory in a dataset, then following up that analysis with a confirmatory analysis in the same dataset could simply mean that the results of the confirmatory analysis are due to the same type 1 error that resulted in the exploratory model in the first place. The confirmatory analysis therefore will not be more informative than the original exploratory analysis.

对探索性数据分析的理解应该非常谨慎。当同时测试多个模型时，发现其中至少一个模型有意义的几率很高，但这可能是由于类型1错误。在测试多个模型时，总是调整显著性水平是很重要的，例如，使用邦弗朗尼校正。另外，不应该在同一数据集中进行探索性分析和验证性分析。探索性分析是用来为一个理论寻找想法，但不是用来检验这个理论。当一个模型在一个数据集中被发现是探索性的，然后在同一个数据集中进行验证性分析，这可能仅仅意味着验证性分析的结果是由于同样的1类错误导致了探索性模型放在首位。因此，验证性分析不会比最初的探索性分析更有用。

Stability of results

It is important to obtain some indication about how generalizable the results are.模板:Sfn While this is often difficult to check, one can look at the stability of the results. Are the results reliable and reproducible? There are two main ways of doing that.

It is important to obtain some indication about how generalizable the results are. While this is often difficult to check, one can look at the stability of the results. Are the results reliable and reproducible? There are two main ways of doing that.

重要的是要得到一些指示，说明这些结果是多么普遍。虽然这通常很难检验，但可以看看结果的稳定性。结果是否可靠和重现性好？有两种主要的方法来做到这一点。

Cross-validation. By splitting the data into multiple parts, we can check if an analysis (like a fitted model) based on one part of the data generalizes to another part of the data as well. Cross-validation is generally inappropriate, though, if there are correlations within the data, e.g. with panel data. Hence other methods of validation sometimes need to be used. For more on this topic, see statistical model validation.

Sensitivity analysis. A procedure to study the behavior of a system or model when global parameters are (systematically) varied. One way to do that is via bootstrapping.

Free software for data analysis

! ——这个列表中的自由软件应该“值得注意” ，其源文章见 wp: gng，wp: wtaf. --

Notable free software for data analysis include:

著名的数据分析免费软件包括:

DevInfo – a database system endorsed by the United Nations Development Group for monitoring and analyzing human development.

ELKI – data mining framework in Java with data mining oriented visualization functions.

KNIME – the Konstanz Information Miner, a user friendly and comprehensive data analytics framework.

Orange – A visual programming tool featuring interactive data visualization and methods for statistical data analysis, data mining, and machine learning.

Pandas – Python library for data analysis

PAW – FORTRAN/C data analysis framework developed at CERN

R – a programming language and software environment for statistical computing and graphics.

ROOT – C++ data analysis framework developed at CERN

SciPy – Python library for data analysis

Data.Analysis – A .NET library for data analysis and transformation

International data analysis contests

Different companies or organizations hold a data analysis contests to encourage researchers utilize their data or to solve a particular question using data analysis. A few examples of well-known international data analysis contests are as follows.

不同的公司或组织举办数据分析竞赛，以鼓励研究人员利用他们的数据或利用数据分析解决特定的问题。以下是一些著名的国际数据分析比赛的例子。

Kaggle competition held by Kaggle^[25]

LTPP data analysis contest held by FHWA and ASCE.^[26]^[27]

|title = Data.Gov:Long-Term Pavement Performance (LTPP)|access-date = November 10, 2017 }}</ref>

| title Data. Gov: Long-Term Pavement Performance (ltp) | access-date November 10,2017} / ref

References

Citations

↑ Xia, B. S., & Gong, P. (2015). Review of business intelligence through data analysis. Benchmarking, 21(2), 300-311. doi:10.1108/BIJ-08-2012-0050
↑ Exploring Data Analysis
↑ John Tukey-The Future of Data Analysis-July 1961
↑ ^4.0 ^4.1 ^4.2 ^4.3 ^4.4 ^4.5 引用错误：无效<ref>标签；未给name属性为Schutt & O'Neil的引用提供文字
↑ "Data Cleaning". Microsoft Research. Retrieved 26 October 2013.
↑ ^6.0 ^6.1 ^6.2 Perceptual Edge-Jonathan Koomey-Best practices for understanding quantitative data-February 14, 2006
↑ Hellerstein, Joseph (27 February 2008). "Quantitative Data Cleaning for Large Databases" (PDF). EECS Computer Science Division: 3. Retrieved 26 October 2013.
↑ Stephen Few-Perceptual Edge-Selecting the Right Graph For Your Message-September 2004
↑ Behrens-Principles and Procedures of Exploratory Data Analysis-American Psychological Association-1997
↑ ^10.0 ^10.1 引用错误：无效<ref>标签；未给name属性为Judd and McClelland 1989的引用提供文字
↑ Grandjean, Martin (2014). "La connaissance est un réseau" (PDF). Les Cahiers du Numérique. 10 (3): 37–54. doi:10.3166/lcn.10.3.37-54.
↑ Stephen Few-Perceptual Edge-Selecting the Right Graph for Your Message-2004
↑ Stephen Few-Perceptual Edge-Graph Selection Matrix
↑ Robert Amar, James Eagan, and John Stasko (2005) "Low-Level Components of Analytic Activity in Information Visualization"
↑ William Newman (1994) "A Preliminary Analysis of the Products of HCI Research, Using Pro Forma Abstracts"
↑ Mary Shaw (2002) "What Makes Good Research in Software Engineering?"
↑ ^17.0 ^17.1 "ConTaaS: An Approach to Internet-Scale Contextualisation for Developing Efficient Internet of Things Applications". ScholarSpace. HICSS50. Retrieved May 24, 2017.
↑ "Congressional Budget Office-The Budget and Economic Outlook-August 2010-Table 1.7 on Page 24" (PDF). Retrieved 2011-03-31.
↑ "Introduction". cia.gov.
↑ Bloomberg-Barry Ritholz-Bad Math that Passes for Insight-October 28, 2014
↑ Aarons, D. (2009). Report finds states on course to build pupil-data systems. Education Week, 29(13), 6.
↑ Rankin, J. (2013, March 28). How data Systems & reports can either fight or propagate the data analysis error epidemic, and how educator leaders can help. Presentation conducted from Technology Information Center for Administrative Leadership (TICAL) School Leadership Summit.
↑ Tabachnick & Fidell, 2007, p. 87-88.
↑ Billings S.A. "Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains". Wiley, 2013
↑ "The machine learning community takes on the Higgs". Symmetry Magazine. July 15, 2014. Retrieved 14 January 2015.
↑ Nehme, Jean (September 29, 2016). "LTPP International Data Analysis Contest". Federal Highway Administration. Retrieved October 22, 2017.
↑ "Data.Gov:Long-Term Pavement Performance (LTPP)". May 26, 2016. Retrieved November 10, 2017.

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Adèr, Herman J. (2008b). "Chapter 15: The main analysis phase". Advising on research methods : a consultant's companion. Huizen, Netherlands: Johannes van Kessel Pub. pp. 357–386. ISBN 9789079418015. OCLC 905799857.

Tabachnick, B.G. & Fidell, L.S. (2007). Chapter 4: Cleaning up your act. Screening data prior to analysis. In B.G. Tabachnick & L.S. Fidell (Eds.), Using Multivariate Statistics, Fifth Edition (pp. 60–116). Boston: Pearson Education, Inc. / Allyn and Bacon.