📚 Year 7 WJEC Statistics: Cross-Curricular Problem Solving | Year 7 WJEC 统计：跨学科综合题型训练
Cross-curricular problem solving shows just how powerful statistics can be. By handling data from science, geography, history, sport and everyday life, you learn to choose the right average, draw accurate charts and make sense of numbers in real situations. This article walks through eight integrated tasks designed for Year 7 WJEC Statistics, mixing key skills with subjects across the curriculum.
跨学科问题解决能让你看到统计学的强大力量。通过处理来自科学、地理、历史、体育和日常生活的数据，你将学会选择合适的平均数、绘制准确的图表以及理解实际情境中的数字。本文精心设计了八个面向 Year 7 WJEC 统计的综合任务，将核心技能与跨学科知识融为一体。
1. Science: Bean Plant Growth | 科学：豆苗生长
A Year 7 science class recorded the height of a bean plant over five days. The results are shown in the table below.
一个七年级科学班记录了五天中一株豆苗的高度，数据见下表。
| Day | Height (cm) |
|---|---|
| 1 | 2 |
| 2 | 3 |
| 3 | 5 |
| 4 | 8 |
| 5 | 12 |
First, find the mean height. Add the five values: 2 + 3 + 5 + 8 + 12 = 30 cm. Then divide by 5: 30 ÷ 5 = 6 cm. The mean height is 6 cm.
首先，计算平均高度。将五个数值相加：2 + 3 + 5 + 8 + 12 = 30 厘米。然后除以 5：30 ÷ 5 = 6 厘米。平均高度为 6 厘米。
Next, order the heights from smallest to largest: 2, 3, 5, 8, 12. The median (middle value) is 5 cm. The range is the difference between the largest and smallest values: 12 – 2 = 10 cm. The range tells us how spread out the data is.
接下来，将高度从小到大排列：2, 3, 5, 8, 12。中位数（中间值）为 5 厘米。极差是最大值与最小值之差：12 – 2 = 10 厘米。极差告诉我们数据的离散程度。
A line graph helps to show the growth trend. Put ‘Day’ on the horizontal axis (x-axis) and ‘Height (cm)’ on the vertical axis (y-axis). Plot each point and join them with straight lines. The steepness of the line between days 3 and 5 indicates faster growth, which a biologist might link to more sunlight or water.
折线图有助于展示生长趋势。将 “天数” 放在横轴（x 轴）上，“高度（厘米）” 放在纵轴（y 轴）上。标出每个点并用直线连接。第 3 天和第 5 天之间线条的陡峭程度表明生长更快，生物学家可能会将此与更多的阳光或水分联系起来。
2. Geography: Dream Destination Survey | 地理：梦想目的地调查
A geography class surveyed 42 Year 7 students about their dream holiday destination. The frequency table shows the results.
一个地理班级对 42 名七年级学生进行了关于梦想度假目的地的调查，频数表显示了结果。
| Destination | Frequency |
|---|---|
| Paris | 12 |
| Rome | 8 |
| New York | 10 |
| Tokyo | 7 |
| Sydney | 5 |
The mode is the category with the highest frequency. Here, ‘Paris’ is the mode with 12 votes. This tells the survey team which destination is most popular.
众数是频数最高的类别。这里，“巴黎” 以 12 票成为众数。这告诉调查小组哪个目的地最受欢迎。
To draw a bar chart, label the x-axis with the five destinations and the y-axis with frequency. Draw bars of equal width; the height of each bar equals its frequency. A bar chart makes it easy to compare categories at a glance—useful when presenting to the school travel club.
要绘制条形图，在 x 轴上标注五个目的地，y 轴标注频数。绘制等宽的条形，每个条形的高度等于其频数。条形图便于一目了然地比较各类别，在向学校旅行俱乐部汇报时非常有用。
Percentages connect the data to geography’s ‘sense of place’. For example, students wanting a European city = Paris (12) + Rome (8) = 20 out of 42. As a percentage: (20 ÷ 42) × 100 ≈ 47.6%. Almost half the group chose Europe.
百分比将数据与地理的 “地方感” 联系起来。例如，想去欧洲城市的学生 = 巴黎（12）+ 罗马（8）= 42 人中的 20 人。百分比为：(20 ÷ 42) × 100 ≈ 47.6%。近一半的小组选择了欧洲。
3. History: Tudor Monarchs’ Reigns | 历史：都铎王朝君主在位年数
When studying the Tudors, historians often look at how long each monarch ruled. The lengths of reign (in years) for the five Tudor monarchs are: Henry VII 24, Henry VIII 38, Edward VI 6, Mary I 5, Elizabeth I 45.
在研究都铎王朝时，历史学家经常关注每位君主的统治时间。五位都铎君主的在位年数（年）为：亨利七世 24 年、亨利八世 38 年、爱德华六世 6 年、玛丽一世 5 年、伊丽莎白一世 45 年。
To find the median reign, order the data: 5, 6, 24, 38, 45. The middle value is 24 years. So the median reign length is 24 years, meaning half the monarchs ruled for less than 24 years and half for more.
要找到在位年数的中位数，将数据排序：5, 6, 24, 38, 45。中间值是 24 年。因此，在位年数的中位数为 24 年，意味着一半的君主治时间少于 24 年，另一半多于 24 年。
Calculate the mean reign: (5 + 6 + 24 + 38 + 45) ÷ 5 = 118 ÷ 5 = 23.6 years. Notice how the short reigns of Edward and Mary pull the mean down, while Elizabeth’s long reign pulls it up. The range is 45 – 5 = 40 years, showing a huge spread in power duration.
计算平均在位年数：(5 + 6 + 24 + 38 + 45) ÷ 5 = 118 ÷ 5 = 23.6 年。请注意，爱德华和玛丽较短的统治时间拉低了平均值，而伊丽莎白漫长的统治则拉高了平均值。极差为 45 – 5 = 40 年，显示出权力持续时间的巨大差异。
Historians might use a bar chart to display these lengths and quickly spot the stability of the Elizabethan era. Linking numbers to historical questions—like ‘Was Tudor rule stable?’—shows how statistics strengthens historical argument.
历史学家可能会用条形图来展示这些在位时间，并迅速发现伊丽莎白时代的稳定。将数字与历史问题（如 “都铎王朝的统治稳定吗？”）联系起来，展示了统计学如何强化历史论点。
4. Physical Education: Sprint Time Analysis | 体育：短跑时间分析
Six Year 7 pupils ran 100 m, and their times in seconds were recorded: 13.5, 14.2, 15.1, 14.0, 13.8, 14.5. The PE teacher wants to pick the most consistent runner for a relay team.
六名七年级学生跑了 100 米，记录的时间（秒）为：13.5，14.2，15.1，14.0，13.8，14.5。体育老师想挑选最稳定的选手参加接力队。
First, order the times: 13.5, 13.8, 14.0, 14.2, 14.5, 15.1. With an even number of values, the median is the mean of the two middle numbers: (14.0 + 14.2) ÷ 2 = 14.1 seconds.
首先，将时间排序：13.5，13.8，14.0，14.2，14.5，15.1。当有偶数个数值时，中位数是中间两个数的平均值：(14.0 + 14.2) ÷ 2 = 14.1 秒。
Calculate the mean time: (13.5 + 14.2 + 15.1 + 14.0 + 13.8 + 14.5) ÷ 6 = 85.1 ÷ 6 ≈ 14.18 seconds. The mean tells you the typical performance, but the range reveals consistency. Range = 15.1 – 13.5 = 1.6 seconds. A small range means times are close together.
计算平均时间：(13.5 + 14.2 + 15.1 + 14.0 + 13.8 + 14.5) ÷ 6 = 85.1 ÷ 6 ≈ 14.18 秒。平均数告诉你一般表现，但极差揭示了稳定性。极差 = 15.1 – 13.5 = 1.6 秒。极差小意味着时间彼此接近。
Pupils could compare this range with another group’s range to see which squad is more consistent. In PE, statistics help make fair selection decisions based on numbers, not just guesses.
学生可以将此极差与另一组的极差进行比较，看哪个小队更稳定。在体育中，统计学有助于根据数字而不仅仅是猜测做出公平的选择决定。
5. Environmental Studies: School Recycling Data | 环境研究：学校回收数据
As part of an eco-committee project, students weighed the recycling collected in one week. The masses (in kg) are: Paper 30, Plastic 22, Glass 15, Metal 10, Organic 25.
作为生态委员会项目的一部分，学生们称量了一周内收集的回收物。质量（千克）为：纸 30，塑料 22，玻璃 15，金属 10，有机垃圾 25。
First, find the total mass: 30 + 22 + 15 + 10 + 25 = 102 kg. Then work out the percentage each material contributes. For paper: (30 ÷ 102) × 100 ≈ 29.4%. For organic waste: (25 ÷ 102) × 100 ≈ 24.5%. These percentages help the school see which recycling stream is the busiest.
首先，计算总质量：30 + 22 + 15 + 10 + 25 = 102 千克。然后计算每种材料所占的百分比。纸类：(30 ÷ 102) × 100 ≈ 29.4%。有机垃圾：(25 ÷ 102) × 100 ≈ 24.5%。这些百分比帮助学校了解哪条回收流最为繁忙。
To display the data in a pie chart, divide a circle into sectors. Multiply each percentage by 3.6° to get the angle. For paper: 29.4 × 3.6 ≈ 106°. Draw the sectors carefully and label each one. A pie chart clearly shows that paper and organic waste make up over half of all recycling.
要用饼图显示数据，将一个圆分成多个扇形。将每个百分比乘以 3.6° 得到角度。纸类：29.4 × 3.6 ≈ 106°。仔细画出各个扇形并标注。饼图清楚地显示纸类和有机垃圾占了所有回收物的一半以上。
The eco-committee can then set targets, such as reducing plastic use because plastic makes up only 22 kg but has a big environmental impact. Statistics turns raw data into action.
然后生态委员会可以设定目标，比如减少塑料使用，因为塑料只有 22 千克但对环境影响很大。统计学将原始数据转化为行动。
6. Design and Technology: Material Strength Test | 设计与技术：材料强度测试
In D&T, students tested the maximum load a card beam could hold before bending. They varied the thickness of the card and recorded the breaking mass.
在设计与技术课中，学生们测试了纸板梁在弯曲前能承受的最大负荷。他们改变了纸板的厚度并记录了断裂质量。
| Thickness (mm) | Breaking mass (g) |
|---|---|
| 1 | 200 |
| 2 | 350 |
| 3 | 480 |
| 4 | 600 |
| 5 | 750 |
Plot these points on a scatter graph: thickness on the x-axis and breaking mass on the y-axis. The points rise steadily, showing a positive correlation. This means as thickness increases, the strength of the beam also increases.
将这些点绘制在散点图上：厚度在 x 轴上，断裂质量在 y 轴上。各点稳步上升，显示出正相关。这意味着随着厚度增加，梁的强度也增加。
Draw a line of best fit through the middle of the points. Use this line to predict the breaking mass for a thickness of 2.5 mm. Reading from the line gives about 415 g. This is called interpolation, because we estimate a value within the data range.
通过各点中间画一条最佳拟合线。利用这条线预测厚度为 2.5 毫米时的断裂质量。从线上读出大约 415 克。这称为内插法，因为我们在数据范围内估算数值。
D&T students can then discuss whether the relationship is linear and how to make the test fair. Combining measurement skills with scatter graphs turns a workshop task into a reliable investigation.
然后设计与技术课的学生可以讨论该关系是否呈线性以及如何使测试公平。将测量技能与散点图相结合，将车间任务转化为可靠的调查。
7. Food Technology: Juice Flavour Ratings | 食品技术：果汁口味评分
After a taste test of a new orange-and-mango juice, 35 students gave star ratings from 1 (dislike) to 5 (excellent). The ratings are shown in the frequency table.
在对一款新橙芒果汁的口味测试之后，35 名学生给出了从 1 星（不喜欢）到 5 星（极佳）的星级评分，频数表如下。
| Rating (stars
Published by TutorHao | Year 7 统计 Revision Series | aleveler.com 更多咨询请联系16621398022（同微信） CommentsMore posts |
|---|
屏轩国际教育cambridge primary/secondary checkpoint, cat4, ukiset,ukcat,igcse,alevel,PAT,STEP,MAT, ibdp,ap,ssat,sat,sat2课程辅导，国外大学本科硕士研究生博士课程论文辅导