📚 The Large Data Set | 大数据集
The Large Data Set (LDS) is a collection of real-world weather data provided by Edexcel for the AS and A-level Mathematics examinations. It is used as a basis for many statistics questions in the exam, and you are expected to be familiar with its content and structure.
大数据集（LDS）是爱德思考试局为 AS 和 A-level 数学考试提供的真实天气数据集合。它被用作考试中许多统计问题的基础，考生应熟悉其内容和结构。
1. Why the Large Data Set Matters | 大数据集为何重要
In the Edexcel Statistics papers, you will be given an extract of the Large Data Set as part of the exam paper. You are not expected to memorise the data, but you must understand the variables, the locations, and the context of the data. Questions will test your ability to calculate summary statistics, compare distributions, and analyse relationships using the data.
在爱德思统计试卷中，考试会提供大数据集的一部分作为试题资料。你不必记住所有数据，但必须理解变量、地点以及数据的背景。考题将测试你计算汇总统计量、比较分布以及分析数据关系的能力。
2. Where Does the Data Come From? | 数据来自哪里
The LDS contains daily weather observations from 15 locations. Six of these are in the United Kingdom, and nine are located around the world. The UK stations are Aberporth, Camborne, Hurn, Leeming, Leuchars and London Heathrow. The international stations are Beijing, Chicago, Jacksonville, Perth, Reykjavik, Seoul, Tenerife, Trondheim and Hong Kong.
大数据集包含来自 15 个地点的每日天气观测数据。其中 6 个位于英国，9 个位于世界各地。英国站点为阿伯波特、坎伯恩、赫恩、利明、卢哈斯和伦敦希思罗。国际站点为北京、芝加哥、杰克逊维尔、珀斯、雷克雅未克、首尔、特内里费、特隆赫姆和香港。
3. Variables in the Data Set | 数据集中的变量
The main variables in the LDS are daily mean temperature, daily maximum temperature, daily minimum temperature, daily total rainfall, daily total sunshine, daily mean wind speed, daily maximum gust, daily mean cloud cover, daily mean pressure and daily mean wind direction.
大数据集中的主要变量包括日平均温度、日最高温度、日最低温度、日总降雨量、日总日照时数、日平均风速、日最大阵风、日平均云量、日平均气压和日平均风向。
| Variable | 变量 | Unit | 单位 | Description | 描述 |
|---|---|---|
| T (mean temperature) | 平均温度 | °C | 摄氏度 | Daily average air temperature | 每日平均气温 |
| T(max) | 最高温度 | °C | 摄氏度 | Maximum temperature of the day | 当日最高气温 |
| T(min) | 最低温度 | °C | 摄氏度 | Minimum temperature of the day | 当日最低气温 |
| R (rainfall) | 降雨量 | mm | 毫米 | Total rainfall in a day | 每日总降雨量 |
| S (sunshine) | 日照 | hours | 小时 | Total sunshine duration | 当日日照总时数 |
| W (wind speed) | 风速 | knots | 节 | Daily mean wind speed | 日平均风速 |
| G (gust) | 阵风 | knots | 节 | Maximum gust speed of the day | 当日最大阵风风速 |
| C (cloud cover) | 云量 | oktas | 八分之一 | Fraction of sky covered by cloud | 天空被云覆盖的比例 |
| P (pressure) | 气压 | hPa | 百帕 | Daily mean atmospheric pressure | 日平均大气压 |
| Wind direction | 风向 | degrees | 度 | Mean compass direction of wind | 风平均方向（罗盘角度） |
4. The Data Period and Format | 数据时段与格式
The data set contains daily observations from 1 May 2015 to 31 October 2015 inclusive. This gives 184 consecutive days for each of the 15 locations. The data is presented as a spreadsheet or table, with each row giving the date, location and values for all variables.
数据集包含从 2015 年 5 月 1 日到 2015 年 10 月 31 日（含）的每日观测数据。每个地点都有连续的 184 天数据。数据以电子表格或表格形式呈现，每行包含日期、地点以及所有变量的值。
5. Summary Statistics: Location and Spread | 汇总统计量：位置与离散程度
You must be able to calculate and interpret measures of central tendency (mean, median, mode) and measures of spread (range, interquartile range, standard deviation). For example, comparing the mean temperature of a UK site with an international site can reveal climate differences.
你必须能够计算和解释集中趋势的度量（平均值、中位数、众数）以及离散程度的度量（极差、四分位距、标准差）。例如，比较英国站点和国际站点的平均温度可以揭示气候差异。
Sample mean: x̄ = Σx / n
样本均值：x̄ = Σx / n
When comparing two locations, use the mean and standard deviation to describe the data. If the standard deviation is small, the values are consistent; if it is large, there is greater variation. The interquartile range is useful for indicating the spread of the middle 50% of the data.
比较两个地点时，使用平均值和标准差来描述数据。如果标准差较小，则数值稳定；如果标准差较大，则波动较大。四分位距用于表示中间 50% 数据的离散程度。
6. Comparing Distributions Using Box Plots | 用箱线图比较分布
Box plots (box-and-whisker diagrams) are used to display and compare distributions. To draw a box plot you need the minimum, lower quartile (Q1), median, upper quartile (Q3) and maximum value. Edexcel often asks you to compare two sites using these diagrams.
箱线图（箱须图）用于显示和比较分布。绘制箱线图需要最小值、下四分位数（Q1）、中位数、上四分位数（Q3）和最大值。爱德思常要求你使用这些图比较两个站点。
For example, a box plot of rainfall in Hurn (UK) and Beijing (China) might show that Beijing has a larger median but also a longer right whisker, suggesting skewed data with occasional heavy rain.
例如，英国赫恩和中国北京的降雨量箱线图可能显示北京的中位数更大，且右须更长，这表明数据偏斜且偶尔有暴雨。
7. Correlation and Regression | 相关与回归
You may be asked to investigate the relationship between two variables. For instance, the relationship between daily mean temperature and daily total sunshine. A scatter diagram can help visualise the relationship, and you can calculate the product-moment correlation coefficient (r) to measure its strength.
你可能需要研究两个变量之间的关系。例如，日平均温度和日总日照时数之间的关系。散点图有助于直观显示关系，同时可以计算积矩相关系数 r 来衡量其强度。
The regression line can be used to predict values. The equation of the least-squares regression line is usually written as y = a + bx, where a is the intercept and b is the slope. You can use this to estimate a variable from another.
回归线可用于预测值。最小二乘回归线的方程通常写作 y = a + bx，其中 a 是截距，b 是斜率。你可以用它从一个变量估计另一个变量。
8. Cleaning Data and Outliers | 数据清理与异常值
Real data often contains missing values or outliers. An outlier is a value that is unusually large or small compared with the rest of the data. A common rule for identifying outliers in a box plot is to use 1.5 × IQR below Q1 or above Q3. If a value lies outside these limits, it is often considered an outlier.
真实数据常常包含缺失值或异常值。异常值是与其他数值相比异常大或小的数值。在箱线图中识别异常值的一个常用规则是使用 Q1 − 1.5×IQR 或 Q3 + 1.5×IQR。如果数值超出这些界限，通常被认为是异常值。
When asked to clean data, you should decide whether to remove outliers, and you should justify your decision. Edexcel may ask you to comment on the effect of removing an outlier on the mean and standard deviation.
在要求清理数据时，你应决定是否移除异常值，并说明理由。爱德思可能要求你评论移除异常值
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