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第118行： − + − {{See also|Problem solving}} − − Author Jonathan Koomey has recommended a series of best practices for understanding quantitative data. These include: − − Author Jonathan Koomey has recommended a series of best practices for understanding quantitative data. These include: − *Check raw data for anomalies prior to performing an analysis; − + + − + − − − * 重新执行重要的计算，例如验证'''<font color = '#ff8000'>公式驱动formula driven</font>'''的数据列； − − *Confirm main totals are the sum of subtotals; − − * 确认总计是小计的和； − − *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.<ref name="Koomey1"/>+ − − − − 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 [[probability distribution|distribution]] of the key variables to see how the individual values cluster around the mean. − 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变量，比如平均数、中位数和标准差。他们还可以分析关键变量的分布情况，来看各个值是如何围绕平均数聚集的。+
→Techniques for analyzing quantitative data 分析定性数据的技术
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==Techniques for analyzing quantitative data 分析定性数据的技术==
==分析定量数据的技术==
作者Jonathan Koomey推荐了一系列理解定量数据的最佳方法。其中包括:
作者Jonathan Koomey推荐了一系列理解定量数据的最佳方法。其中包括:
* 在实施数据分析之前检查原始数据中的异常值；
* 在实施数据分析之前检查原始数据中的'''异常值 anomalies'''；
* 重新执行重要的计算，例如验证'''公式驱动formula driven'''的数据列；
* 确认总和是部分的和；
*Re-perform important calculations, such as verifying columns of data that are formula driven;
* 检查那些可以通过一些方法预测的数字之间的关系，例如随时间变化的比例；
* 检查那些可以通过一些方法预测的数字之间的关系，例如随时间变化的比例；
* 使数字正态化以便于比较，例如分析每个人的数量，或相对于GDP 的数量，或相对于基准年的数量指数；
* 使数字正态化以便于比较，例如分析每个人的数量，或相对于GDP 的数量，或相对于基准年的数量指数；
* 通过分析导致结果的因素将问题整体分解为几个部分，如'''净资产收益率return on equity'''的'''杜邦分析DuPont analysis'''<ref name="Koomey1"/>。
* 通过分析导致结果的因素将问题整体分解为几个部分，如'''<font color = '#ff8000'>净资产收益率return on equity</font>'''的'''<font color = '#ff8000'>杜邦分析DuPont analysis</font>'''<ref name="Koomey1"/>。
对于被调查的变量，分析师通常会得到它们的描述统计 descriptive statistics变量，比如平均数、中位数和标准差。他们还可以分析关键变量的分布情况，来看各个值是如何围绕平均数聚集的。
[[File:US_Employment_Statistics_-_March_2015.png|thumb|250px|right|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 events|mutually exclusive]] of each other and [[Collectively exhaustive events|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).
[[File:US_Employment_Statistics_-_March_2015.png|thumb|250px|right|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 events|mutually exclusive]] of each other and [[Collectively exhaustive events|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).