Business Statistics — Application Questions (商业统计 应用题)
Question 1 — Definition of Data (数据的定义)
What is data in business statistics?
在商业统计中，数据的定义是什么？
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Data are fact-based information (numbers, figures, tables) collected, analyzed, and summarized for interpretation. 数据是基于事实的信息（数字、图表、表格），用于收集、分析和解释。📝 点击查看解析
Data provide the foundation for business and economic decision-making. 数据是商业与经济决策的基础。Question 2 — Elements (要素)
In a survey of students’ GPA, what is the element?
在一次关于学生绩点的调查中，什么是“要素”？
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Each student is an element. 每个学生就是一个要素。📝 点击查看解析
Elements are the objects or entities on which data are collected. 要素是收集数据的对象或实体。Question 3 — Variables (变量)
If we record a student’s age, gender, and GPA, what are these called?
如果我们记录学生的年龄、性别和绩点，这些叫什么？
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They are variables. 这些是变量。📝 点击查看解析
Variables are characteristics or attributes of elements. 变量是要素的特征或属性。Question 4 — Observation (观测值)
If one student’s data is {Age=20, Gender=Female, GPA=3.5}, what is this record?
如果一个学生的数据是 {年龄=20，性别=女，绩点=3.5}，这条记录是什么？
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It is an observation. 这是一个观测值。📝 点击查看解析
An observation is the set of measurements collected for each variable of an element. 观测值是对要素各个变量的测量集合。Question 5 — Nominal Scale (名义尺度)
Classifying students as Male or Female uses which measurement scale?
将学生分为男性或女性，属于哪种测量尺度？
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Nominal scale. 名义尺度。📝 点击查看解析
Nominal scale is used for categories without order. 名义尺度用于没有顺序的分类。Question 6 — Ordinal Scale (顺序尺度)
Customer ratings such as Very Good=5, Very Bad=1 belong to which scale?
顾客评分如“非常好=5，非常差=1”属于哪种尺度？
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Ordinal scale. 顺序尺度。📝 点击查看解析
Ordinal scale shows order but not equal differences. 顺序尺度体现顺序，但差距不一定相等。Question 7 — Interval Scale (区间尺度)
Temperature measured in Celsius is an example of which scale?
以摄氏度测量的温度属于哪种尺度？
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Interval scale. 区间尺度。📝 点击查看解析
Interval scale allows meaningful differences but no true zero. 区间尺度有意义的差值，但没有绝对零点。Question 8 — Ratio Scale (比率尺度)
Income measured in dollars is which type of scale?
以美元计量的收入属于哪种尺度？
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Ratio scale. 比率尺度。📝 点击查看解析
Ratio scale has a true zero and meaningful ratios. 比率尺度有绝对零点，比例有意义。Question 9 — Categorical Data (类别数据)
Which type of data is “Yes/No” answers?
“是/否”答案属于哪类数据？
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Categorical data. 类别数据。📝 点击查看解析
Categorical data represent categories or labels. 类别数据表示类别或标签。Question 10 — Quantitative Data (定量数据)
A survey records the weight of students. What type of data is this?
调查记录学生的体重，这属于哪类数据？
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Quantitative data. 定量数据。📝 点击查看解析
Quantitative data are numerical and measurable. 定量数据是数值型、可测量的。Question 11 — Cross-sectional Data (横截面数据)
A survey of 100 people’s opinions on one day is what type of data?
在同一天调查100个人的意见，这是什么类型的数据？
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Cross-sectional data. 横截面数据。📝 点击查看解析
Cross-sectional data are collected at one point in time. 横截面数据在某一时间点收集。Question 12 — Time Series Data (时间序列数据)
Monthly unemployment rate from 2010 to 2025 is which type?
2010年至2025年的月度失业率属于哪类数据？
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Time series data. 时间序列数据。📝 点击查看解析
Time series data are collected over multiple time periods. 时间序列数据在多个时间段收集。Question 13 — Internal Data (内部数据)
Company sales records belong to which source of data?
公司销售记录属于哪种数据来源？
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Internal existing sources. 内部现有来源。📝 点击查看解析
Internal sources are self-owned data. 内部数据是企业自身的数据。Question 14 — External Data (外部数据)
Purchased industry reports are what type of source?
购买的行业报告属于哪种来源？
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External existing sources. 外部现有来源。📝 点击查看解析
External sources are data acquired outside the organization. 外部数据是从组织外部获取的。Question 15 — Experimental Study (实验研究)
Testing a new medicine with a treatment group and a control group is what type of study?
用实验组和对照组测试新药属于哪种研究？
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Experimental study. 实验研究。📝 点击查看解析
Experimental studies involve manipulation and control. 实验研究包含操控和控制。Question 16 — Observational Study (观察研究)
Watching how customers behave in a supermarket is what type of study?
观察顾客在超市中的行为属于哪种研究？
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Observational study. 观察研究。📝 点击查看解析
Observational studies collect data without manipulation. 观察研究是不干预的记录。Question 17 — Data Cleaning (数据清理)
Why is removing outliers important?
为什么要去除异常值？
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To reduce errors and improve accuracy. 减少误差，提高准确性。📝 点击查看解析
Data cleaning ensures reliable analysis results. 数据清理保证分析结果可靠。Question 18 — Outlier Example (异常值例子)
If one survey reports age = 200, how should we treat it?
如果一份调查报告年龄=200，我们应该如何处理？
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Reject it as an outlier. 将其剔除，作为异常值。📝 点击查看解析
Extreme unrealistic values must be removed. 极端不合理值需要被去除。Question 19 — Categorical vs Quantitative (类别 vs 定量)
Is “Favorite Color” categorical or quantitative?
“最喜欢的颜色”是类别数据还是定量数据？
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Categorical. 类别数据。📝 点击查看解析
Colors are labels without numeric meaning. 颜色是没有数值意义的标签。Question 20 — Numeric Example (数值例子)
Is “Monthly Salary” categorical or quantitative?
“月薪”是类别数据还是定量数据？
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Quantitative. 定量数据。📝 点击查看解析
Salary is numerical and measurable. 薪水是数值型且可测量。Question 21 — Interval vs Ratio (区间 vs 比率)
Why is Celsius temperature interval, not ratio?
为什么摄氏温度是区间尺度，而不是比率尺度？
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Because 0°C does not mean “no temperature”. 因为0℃并不代表“没有温度”。📝 点击查看解析
Interval has no true zero, ratio has true zero. 区间没有绝对零点，而比率有。Question 22 — True Zero (绝对零点)
Give one variable that has a true zero.
举一个有绝对零点的变量。
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Weight or income. 体重或收入。📝 点击查看解析
Zero weight/income means none exists. 零体重/收入代表不存在。Question 23 — Frequency Analysis (频率分析)
Which type of data is typically summarized in frequency tables?
哪类数据通常用频率表总结？
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Categorical data. 类别数据。📝 点击查看解析
Frequencies count how often categories occur. 频率表统计类别出现次数。Question 24 — Mean Calculation (均值计算)
Can we compute a mean for ordinal data?
顺序数据可以计算均值吗？
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Not strictly valid. 严格来说不可以。📝 点击查看解析
Ordinal shows order, but distances between ranks may not be equal. 顺序只体现顺序，等级间的距离不一定相等。Question 25 — Time Series Example (时间序列例子)
Is “Daily Stock Prices in 2024” cross-sectional or time series?
“2024年每日股价”是横截面数据还是时间序列数据？
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Time series. 时间序列。📝 点击查看解析
Because the data cover multiple time points. 因为数据涵盖多个时间点。Question 26 — Data Reliability (数据可靠性)
Why is external data sometimes less reliable?
为什么外部数据有时不可靠？
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Because the organization cannot control its collection. 因为组织无法控制其收集过程。📝 点击查看解析
External data may have biases or errors. 外部数据可能有偏差或错误。Question 27 — Data Accessibility (数据可获得性)
Why do firms sometimes use external data despite cost?
为什么企业即使花费高也要用外部数据？
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Because it provides broader information. 因为它提供更广泛的信息。📝 点击查看解析
External data supplement internal data for decision-making. 外部数据补充内部数据以支持决策。Question 28 — Data Error Check (数据错误检查)
What methods can reduce acquisition errors?
哪些方法能减少数据获取误差？
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Internal consistency and common sense checks. 内部一致性检查和常识判断。📝 点击查看解析
Consistency helps reject outliers; common sense rules out extremes. 一致性剔除异常值；常识排除极端情况。Question 29 — Dataset Structure (数据集结构)
In a dataset table, rows represent what?
在数据表中，行表示什么？
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Observations. 观测值。📝 点击查看解析
Each row records measurements for one element. 每一行记录一个要素的测量值。Question 30 — Dataset Columns (数据表列)
In a dataset table, columns represent what?
在数据表中，列表示什么？