📚 Continuous Random Variables: Core Concepts | 连续随机变量基础概念
在 A-Level 进阶数学(Edexcel Further Statistics)中,连续随机变量(Continuous Random Variable)是概率论从”数点”走向”量面”的关键一步。掌握了它,你就掌握了均匀分布、指数分布和正态分布的公共语言。本文用中英双语,从定义出发,逐层拆解概率密度函数、累积分布函数、期望与方差,并配以 Edexcel 真题风格的完整例题。
In Edexcel A-Level Further Mathematics (Further Statistics), the continuous random variable marks the step from counting outcomes to measuring areas. Master it, and you master the common language of the uniform, exponential and normal distributions. This bilingual article builds the concept layer by layer — from the probability density function and cumulative distribution function to expectation and variance — with full Edexcel-style worked examples.
1. The Essential Difference Between Discrete and Continuous Variables | 离散与连续的本质区别
离散随机变量(Discrete Random Variable)的取值是可以一一列出的,比如掷一枚骰子的点数 {1,2,3,4,5,6},或某路口一小时内通过的车辆数 {0,1,2,…}。对离散变量,我们关心的是”取到某个具体值”的概率 P(X = x),这些概率由概率质量函数(PMF)给出,且所有概率之和等于 1。
A discrete random variable takes values you can list one by one, such as the score of a die {1,2,3,4,5,6} or the number of cars passing a junction in an hour {0,1,2,…}. For a discrete variable we care about the probability of hitting a specific value, P(X = x), given by the probability mass function (PMF), and these probabilities sum to 1.
连续随机变量的取值则充满一个区间,例如等待公交车的时间(0 到 15 分钟内的任意实数)、一支笔的质量、一段电线的长度。这样的变量取值有不可数无穷多个,任何具体值被精确取到的概率都是 0,即 P(X = x) = 0。因此我们不能再问”X 恰好等于 3 的概率是多少”,而只能问”X 落在某个区间内的概率是多少”。
A continuous random variable takes any value within an interval — the waiting time for a bus (any real number between 0 and 15 minutes), the mass of a pen, the length of a wire. Its values are uncountably infinite, so the probability of hitting any exact value is zero: P(X = x) = 0. We therefore never ask “what is the probability that X equals 3 exactly?” but only “what is the probability that X lies in some interval?”
这个区别带来一个重要的直觉:对连续变量,概率不再是”点”的重量,而是”面”的面积。后续所有公式——PDF、CDF、期望——都可以统一理解为”求面积”的不同姿势。这也是 Edexcel 考纲中把连续随机变量放在统计模块核心位置的原因。
连续随机变量在现实中无处不在:测量误差服从正态分布,放射性衰变的等待时间服从指数分布,随机数生成器产生均匀分布。理解这些模型背后的”为什么”,比死记公式更能帮助你在应用题中快速识别分布类型——看到”等待时间”想指数,看到”误差与测量”想正态,看到”完全随机选取”想均匀。
Continuous random variables are everywhere in the real world: measurement errors follow the normal distribution, waiting times for radioactive decay follow the exponential, and random number generators produce the uniform. Understanding the “why” behind these models helps you identify the distribution quickly in application questions — “waiting time” suggests exponential, “error or measurement” suggests normal, and “completely random pick” suggests uniform.
This difference gives a crucial intuition: for a continuous variable, probability is no longer the weight of a point but the area under a curve. Every formula that follows — the PDF, the CDF, the expectation — is a different way of computing area. This is why Edexcel places continuous random variables at the heart of its statistics specification.
2. The Probability Density Function: Definition, Properties and Geometric Meaning | 概率密度函数:定义、性质与几何意义
连续随机变量 X 的概率密度函数(Probability Density Function,记作 f(x))是描述其分布的”曲线”。它本身不是概率,而是”概率的密度”——就像人口密度地图上的等高线,高度本身不是人数,但围成的面积才是人数。Edexcel 考纲中 PDF 必须满足两条核心性质。
The probability density function (PDF), written f(x), is the curve that describes the distribution of a continuous random variable X. The PDF itself is not a probability but a density of probability — like the contours on a population map: the height alone is not the population, but the area enclosed by it is. Two core properties are required by the Edexcel specification.
性质一:非负性。对定义域内所有 x,f(x) ≥ 0。曲线不能落到 x 轴下方,否则”面积”会出现负数,概率就失去意义。
Property 1: Non-negativity. For all x in the domain, f(x) ≥ 0. The curve cannot dip below the x-axis, or “areas” would go negative and probability would lose its meaning.
性质二:归一性。曲线与 x 轴围成的总面积必须等于 1,即 ∫ f(x) dx = 1(对全部实数域积分)。这对应”所有可能结果的总概率为 1″。
Property 2: Normalisation. The total area between the curve and the x-axis must equal 1, i.e. ∫ f(x) dx = 1 over the whole real line. This mirrors the rule that all possible outcomes together have probability 1.
区间概率 = 面积。X 落在区间 (a, b) 内的概率为 P(a < X < b) = ∫ₐᵇ f(x) dx,即曲线下 a 到 b 之间的面积。由于 P(X = x) = 0,开区间与闭区间的概率完全相同,P(a ≤ X ≤ b) = P(a < X < b)。
Interval probability = area. The probability that X lies in the interval (a, b) is P(a < X < b) = ∫ₐᵇ f(x) dx, the area under the curve between a and b. Because P(X = x) = 0, open and closed intervals give identical probabilities: P(a ≤ X ≤ b) = P(a < X < b).
在 Edexcel 题目中,PDF 最常见的出场方式是”含未知常数 k 的分段函数”,第一步永远是用归一性 ∫ f(x) dx = 1 解出 k。这条铁律请你务必牢记。
如何快速判断一个函数能否充当合法的 PDF?三步检验:其一,定义域内处处 f(x) ≥ 0;其二,∫ f(x) dx = 1;其三,注意分段函数的积分必须分段进行。Edexcel 有时会给出一个”伪装”的密度函数,例如定义在 [0, 2] 上的 f(x) = x − 0.5——它在 x < 0.5 时取负值,直接违反非负性,永远不可能成为合法的 PDF。这类判断要一眼看穿,它是选择题与"说明理由"小题的常见考点。
How do you quickly decide whether a function can serve as a valid PDF? A three-step test: first, f(x) ≥ 0 everywhere on the domain; second, ∫ f(x) dx = 1; third, when the function is piecewise, integrate piece by piece. Edexcel sometimes offers a disguised density such as f(x) = x − 0.5 on [0, 2] — it is negative for x < 0.5, violating non-negativity, so it can never be a valid PDF. Learn to spot this at a glance; it is a favourite of multiple-choice and "give a reason" questions.
In Edexcel questions the PDF most often appears as a piecewise function containing an unknown constant k, and the first step is always to solve ∫ f(x) dx = 1 for k. Remember this golden rule.
3. The Cumulative Distribution Function: The Bridge from Density to Probability | 累积分布函数:从密度到概率的桥梁
累积分布函数(Cumulative Distribution Function,CDF)定义为 F(x) = P(X ≤ x) = ∫₋∞ˣ f(t) dt。直观地说,F(x) 是”从最左边一直扫到 x 为止累计的面积”,因此它回答的是”X 不超过 x 的概率”这类累积性问题。
The cumulative distribution function (CDF) is defined as F(x) = P(X ≤ x) = ∫₋∞ˣ f(t) dt. Intuitively, F(x) is the area accumulated from the far left up to x, so it answers cumulative questions such as “what is the probability that X is no greater than x?”
CDF 有三个必须掌握的性质。第一,值域在 0 与 1 之间:当 x 趋近负无穷时 F(x) → 0,当 x 趋近正无穷时 F(x) → 1。第二,单调不减:x 越大,累计面积只会增加或持平。第三,导数还原:F'(x) = f(x),即 CDF 求导回到 PDF,这是两类函数互相转换的钥匙。
The CDF has three properties you must know. First, its range is between 0 and 1: as x → −∞, F(x) → 0, and as x → +∞, F(x) → 1. Second, it is non-decreasing: a larger x can only add area, never remove it. Third, differentiation recovers the PDF: F'(x) = f(x), which is the key that converts between the two functions.
用 CDF 表示区间概率非常简洁:P(a < X < b) = F(b) − F(a)。此外,CDF 还是求分位数的工具:中位数(median)m 满足 F(m) = 0.5,下四分位数 q₁ 满足 F(q₁) = 0.25,上四分位数 q₃ 满足 F(q₃) = 0.75。Edexcel 常考"求中位数",本质就是解一个积分方程。
反过来,题目也可能给出 CDF 让你求 PDF:对 F(x) 逐段求导即得 f(x)。检验 CDF 是否正确,可以看两个端点:F 在定义域左端应为 0,右端应为 1;再看单调性——F 必须全程不减。一个常见陷阱是分段点处 F 的值要连续,例如某分段函数在 x = 1 处左段算到 0.6、右段却从 0.3 开始,那一定是计算错误,因为累积概率不可能”掉下来”。
Conversely, a question may give the CDF and ask for the PDF: differentiate F piece by piece. To check a CDF, look at the two ends — F should be 0 at the left end of the domain and 1 at the right end — and check monotonicity: F must never decrease. A common trap is discontinuity at a splice point: if the left piece reaches 0.6 at x = 1 but the right piece starts at 0.3, something is wrong, because an accumulated probability cannot jump downwards.
Interval probabilities become very compact with the CDF: P(a < X < b) = F(b) − F(a). The CDF is also the tool for quantiles: the median m satisfies F(m) = 0.5, the lower quartile q₁ satisfies F(q₁) = 0.25, and the upper quartile q₃ satisfies F(q₃) = 0.75. When Edexcel asks for the median, you are really solving an integral equation.
4. Expectation and Variance: Integral Forms for Continuous Variables | 期望与方差:连续情形的积分形式
离散变量中,期望是”值 × 概率”的求和:E(X) = Σ x·P(X = x)。连续变量把求和换成积分,”值 × 概率密度”在全部定义域上积分:E(X) = ∫ x·f(x) dx。期望的几何意义是分布的重心,也就是长期观测的均值。
For a discrete variable, expectation is a sum of “value × probability”: E(X) = Σ x·P(X = x). For a continuous variable the sum becomes an integral of “value × density” over the whole domain: E(X) = ∫ x·f(x) dx. Geometrically, the expectation is the centre of mass of the distribution — the long-run average.
方差的定义与离散情形完全平行:Var(X) = E(X²) − [E(X)]²,其中 E(X²) = ∫ x²·f(x) dx。注意顺序:先算 E(X²) 和 E(X),再相减,切勿先减后平方。标准差(standard deviation)是方差的平方根,记作 σ。
Variance is defined in exact parallel with the discrete case: Var(X) = E(X²) − [E(X)]², where E(X²) = ∫ x²·f(x) dx. Note the order: compute E(X²) and E(X) first, then subtract — never square first. The standard deviation σ is the square root of the variance.
线性变换公式在考试中高频出现:E(aX + b) = aE(X) + b,Var(aX + b) = a²Var(X)。特别地,Var(3X + 2) = 9Var(X)。注意常数 b 对期望有影响、对方差无影响,而系数 a 对方差是平方关系——这直接对应”拉伸分布会放大波动”的直觉。
更一般的公式值得记下:对 X 的任意函数 g(X),E[g(X)] = ∫ g(x)·f(x) dx。它把”求函数的期望”统一到同一条积分流水线上,例如 E[X(X − 1)] = E(X²) − E(X)。这个公式在 Edexcel 中常被用来包装题目:题目不直接给 E(X²),而是给出 E[X(X + 2)] 之类的表达式,需要你先展开成 E(X²) + 2E(X),再与已知条件联立求解。展开、代入、求解,三步缺一不可。
A more general formula is worth memorising: for any function g of X, E[g(X)] = ∫ g(x)·f(x) dx. It puts “expectation of a function” on the same integral conveyor belt — for instance E[X(X − 1)] = E(X²) − E(X). Edexcel likes to wrap questions in this formula: instead of giving E(X²) directly, the paper gives an expression such as E[X(X + 2)], which you must expand to E(X²) + 2E(X) and then solve together with the other conditions. Expand, substitute, solve — all three steps are needed.
Linear transformation formulas appear frequently in exams: E(aX + b) = aE(X) + b and Var(aX + b) = a²Var(X). In particular, Var(3X + 2) = 9Var(X). The constant b shifts the expectation but not the variance, while the factor a enters the variance squared — matching the intuition that stretching a distribution magnifies its spread.
5. Uniform and Exponential Distributions: Two Fundamental Continuous Distributions | 均匀分布与指数分布:两个基础连续分布
连续均匀分布(Continuous Uniform Distribution)U(a, b)。若 X ~ U(a, b),则 X 在区间 [a, b] 内”处处等可能”,密度函数为常数 f(x) = 1/(b − a),其余区间为 0。它的期望 E(X) = (a + b)/2,正好是区间中点;方差 Var(X) = (b − a)²/12。均匀分布常用于”随机选取一个时刻””误差均匀分布”等模型。
举一个具体的数字例子:若 X ~ U(2, 10),则 f(x) = 1/(10 − 2) = 1/8(当 2 ≤ x ≤ 10)。于是 P(X > 6) = (10 − 6)/8 = 1/2,E(X) = (2 + 10)/2 = 6,Var(X) = (10 − 2)²/12 = 64/12 = 16/3 ≈ 5.33。对均匀分布,任何区间概率都可以按”长度比例”心算——这正是它作为入门分布的价值所在。
Here is a concrete numeric example: if X ~ U(2, 10), then f(x) = 1/(10 − 2) = 1/8 for 2 ≤ x ≤ 10. So P(X > 6) = (10 − 6)/8 = 1/2, E(X) = (2 + 10)/2 = 6, and Var(X) = (10 − 2)²/12 = 64/12 = 16/3 ≈ 5.33. With the uniform distribution, every interval probability can be worked out in your head by length ratio — that is its value as an entry-level distribution.
The continuous uniform distribution U(a, b). If X ~ U(a, b), then X is equally likely anywhere in [a, b]; the density is the constant f(x) = 1/(b − a) and 0 elsewhere. Its expectation E(X) = (a + b)/2 is exactly the midpoint of the interval, and Var(X) = (b − a)²/12. The uniform distribution models “pick a random instant” or “error spread evenly” situations.
指数分布(Exponential Distribution)Exp(λ)。若 X ~ Exp(λ),则对 x ≥ 0,f(x) = λe^(−λx)。它的期望 E(X) = 1/λ,方差 Var(X) = 1/λ²。指数分布用于建模”两次事件之间的等待时间”,例如顾客到达间隔、设备无故障工作时间。其 CDF 特别简洁:F(x) = 1 − e^(−λx),因此 P(X > x) = e^(−λx)。
The exponential distribution Exp(λ). If X ~ Exp(λ), then for x ≥ 0 we have f(x) = λe^(−λx). Its expectation is E(X) = 1/λ and its variance is Var(X) = 1/λ². The exponential distribution models waiting times between events — customer arrival gaps, or the lifetime of equipment before failure. Its CDF is beautifully simple: F(x) = 1 − e^(−λx), so P(X > x) = e^(−λx).
指数分布还有一个著名性质——无记忆性(memoryless property):P(X > s + t | X > s) = P(X > t)。直觉上,一台已经工作了 s 小时的设备,”再撑 t 小时”的概率与一台全新的设备完全相同——它不记得自己已经工作多久。这个性质在 Edexcel 应用题(如”灯泡寿命”)中反复出现,常与条件概率结合考查。
The exponential distribution also has a famous property — the memoryless property: P(X > s + t | X > s) = P(X > t). Intuitively, a machine that has already run for s hours has the same probability of surviving another t hours as a brand-new machine — it does not remember its age. This property recurs in Edexcel applications such as light-bulb lifetimes, often combined with conditional probability.
6. The Normal Distribution: The Core Continuous Distribution | 正态分布:连续随机变量的核心
正态分布(Normal Distribution)N(μ, σ²) 是统计学的基石。它的密度函数为 f(x) = (1/(σ√(2π)))·e^(−(x−μ)²/(2σ²)),曲线呈对称钟形,均值 μ 是对称轴,σ 决定胖瘦。约 68% 的数据落在 μ ± σ 内,约 95% 落在 μ ± 2σ 内,约 99.7% 落在 μ ± 3σ 内——这就是著名的 68-95-99.7 法则。
The normal distribution N(μ, σ²) is the cornerstone of statistics. Its density is f(x) = (1/(σ√(2π)))·e^(−(x−μ)²/(2σ²)): a symmetric bell-shaped curve with mean μ as its axis of symmetry and σ controlling the spread. Roughly 68% of the data lies within μ ± σ, 95% within μ ± 2σ and 99.7% within μ ± 3σ — the famous 68-95-99.7 rule.
由于正态密度的积分没有初等表达式,Edexcel 要求先标准化(standardise):令 Z = (X − μ)/σ,则 Z ~ N(0, 1),再用标准正态分布表(Z-table)查概率。例如 X ~ N(100, 16),求 P(X > 105):Z = (105 − 100)/4 = 1.25,查表得 P(Z < 1.25) = 0.8944,因此 P(X > 105) = 1 − 0.8944 = 0.1056。
Because the normal density has no elementary antiderivative, Edexcel requires you to standardise first: let Z = (X − μ)/σ, so that Z ~ N(0, 1), then read probabilities from the standard normal table. For example, with X ~ N(100, 16), to find P(X > 105): Z = (105 − 100)/4 = 1.25, the table gives P(Z < 1.25) = 0.8944, hence P(X > 105) = 1 − 0.8944 = 0.1056.
注意对称性技巧:P(Z < −z) = P(Z > z) = 1 − P(Z < z),而 P(−z < Z < z) = 2P(Z < z) − 1。逆向查表同样常考:给定概率反查 z 值,再解出 X = μ + zσ。Edexcel 统计试卷中正态分布通常占 10 分左右,标准化步骤正确即可拿到大部分分数。
逆向查表是另一类高频考法:已知概率反求边界。例如 X ~ N(12, 4),且 P(X < a) = 0.90,求 a。先反查标准正态表,P(Z < 1.2816) = 0.90,于是 a = μ + 1.2816σ = 12 + 1.2816 × 2 ≈ 14.5632。这类题目常用于"设门槛"场景——如某奖项只颁发给成绩前 10% 的学生,最低分数线是多少?先标准化、再查表、最后解方程,顺序固定,缺一步都会丢分。
Inverse reading is another high-frequency pattern: given a probability, find the boundary. For example, X ~ N(12, 4) and P(X < a) = 0.90. Reading the table backwards, P(Z < 1.2816) = 0.90, so a = μ + 1.2816σ = 12 + 1.2816 × 2 ≈ 14.5632. Such questions appear as threshold scenarios — an award is given only to the top 10% of students; what is the cut-off score? Standardise, read the table, solve the equation — the order is fixed and skipping a step costs marks.
Use symmetry tricks: P(Z < −z) = P(Z > z) = 1 − P(Z < z), and P(−z < Z < z) = 2P(Z < z) − 1. Inverse reading of the table is also common: given a probability, read off z, then solve X = μ + zσ. The normal distribution typically carries around 10 marks in the Edexcel statistics papers, and a correct standardisation step earns most of them.
7. Common Edexcel Question Types and Strategies | Edexcel 常见题型与解题策略
综合 Edexcel 近年真题,连续随机变量的题目可以归纳为五种固定套路。第一类”求常数”:用 ∫ f(x) dx = 1 解 k。第二类”求概率”:直接积分或作差 F(b) − F(a)。第三类”求期望与方差”:分别积分 E(X)、E(X²),再套 Var(X) = E(X²) − [E(X)]²。第四类”求中位数与四分位数”:令 F(x) = 0.5 / 0.25 / 0.75 解方程。第五类”求众数(mode)”:在定义域内最大化 f(x),通常求导令 f'(x) = 0。
Across recent Edexcel papers, continuous random variable questions fall into five recurring patterns. Type 1, “find the constant”: solve ∫ f(x) dx = 1 for k. Type 2, “find a probability”: integrate directly or use F(b) − F(a). Type 3, “find expectation and variance”: integrate E(X) and E(X²), then apply Var(X) = E(X²) − [E(X)]². Type 4, “find the median and quartiles”: set F(x) = 0.5 / 0.25 / 0.75 and solve. Type 5, “find the mode”: maximise f(x) over the domain, usually by setting f'(x) = 0.
三个高频失分点值得警惕。其一,忘记检查分段函数的分段点:积分必须在正确的区间上分段进行,例如 f(x) 只在 [0, 2] 上非零,∫₋∞ˣ 就要拆成”0 到 2 内”与”2 之后”两段讨论。其二,中位数方程解出两个根时,要舍弃落在定义域之外的根。其三,写最终答案时漏掉单位或概率的小数位要求(Edexcel 通常要求保留 3 位有效数字或按题目指定精度)。
书写规范同样影响得分。Edexcel 统计大题按步骤给方法分(method marks):写出 PDF 归一性方程得 1 分,正确积分得 1 分,解出 k 再得 1 分。即使最终答案算错,只要步骤完整也能保住大部分分数。因此务必把”设积分、列式子、写结果”三行都写清楚,切忌心算跳步。遇到含参数的密度函数时,先画出分段区间再动笔,能有效避免”积分区间张冠李戴”的低级错误。
Presentation also affects your marks. Edexcel statistics questions award method marks step by step: writing the normalisation equation earns 1 mark, integrating correctly earns 1 mark, and solving for k earns another. Even a wrong final answer keeps most of the marks if the steps are complete. So always write the integral setup, the substitution, and the result as three clear lines — never skip steps by mental arithmetic. When the density is piecewise, sketch the domain before writing anything; it prevents the classic mistake of integrating over the wrong interval.
Three common mark-losers deserve attention. First, forgetting the piecewise structure: integrate over the correct pieces — if f(x) is non-zero only on [0, 2], then ∫₋∞ˣ must be split into “within [0, 2]” and “beyond 2”. Second, when the median equation yields two roots, discard the one outside the domain. Third, final answers must respect rounding rules — Edexcel usually wants 3 significant figures or the precision stated in the question.
8. Worked Examples: From Understanding to Full Marks | 综合例题演练:从理解到得分
例题 1(完整套路,10 分风格)。设连续随机变量 X 的密度函数为 f(x) = k(1 + x),0 ≤ x ≤ 2,其余为 0。(a) 求常数 k;(b) 求 P(X < 1);(c) 求 E(X);(d) 求 Var(X);(e) 求中位数 m。
Example 1 (the full routine, 10-mark style). A continuous random variable X has density f(x) = k(1 + x) for 0 ≤ x ≤ 2, and 0 otherwise. (a) Find k; (b) find P(X < 1); (c) find E(X); (d) find Var(X); (e) find the median m.
解答 (a)。由归一性:∫₀² k(1 + x) dx = k[x + x²/2]₀² = k(2 + 2) = 4k = 1,故 k = 1/4。
Solution (a). By normalisation: ∫₀² k(1 + x) dx = k[x + x²/2]₀² = k(2 + 2) = 4k = 1, so k = 1/4.
解答 (b)。P(X < 1) = ∫₀¹ (1/4)(1 + x) dx = (1/4)[x + x²/2]₀¹ = (1/4)(1 + 1/2) = 3/8 = 0.375。
Solution (b). P(X < 1) = ∫₀¹ (1/4)(1 + x) dx = (1/4)[x + x²/2]₀¹ = (1/4)(1 + 1/2) = 3/8 = 0.375.
解答 (c)。E(X) = ∫₀² x·(1/4)(1 + x) dx = (1/4)∫₀² (x + x²) dx = (1/4)[x²/2 + x³/3]₀² = (1/4)(2 + 8/3) = (1/4)(14/3) = 7/6 ≈ 1.167。
Solution (c). E(X) = ∫₀² x·(1/4)(1 + x) dx = (1/4)∫₀² (x + x²) dx = (1/4)[x²/2 + x³/3]₀² = (1/4)(2 + 8/3) = (1/4)(14/3) = 7/6 ≈ 1.167.
解答 (d)。先算 E(X²) = (1/4)∫₀² (x² + x³) dx = (1/4)[x³/3 + x⁴/4]₀² = (1/4)(8/3 + 4) = (1/4)(20/3) = 5/3。于是 Var(X) = 5/3 − (7/6)² = 5/3 − 49/36 = 60/36 − 49/36 = 11/36 ≈ 0.3056。
Solution (d). First compute E(X²) = (1/4)∫₀² (x² + x³) dx = (1/4)[x³/3 + x⁴/4]₀² = (1/4)(8/3 + 4) = (1/4)(20/3) = 5/3. Hence Var(X) = 5/3 − (7/6)² = 5/3 − 49/36 = 60/36 − 49/36 = 11/36 ≈ 0.3056.
解答 (e)。令 F(m) = 1/2:(1/4)(m + m²/2) = 1/2,即 m² + 2m − 4 = 0。解得 m = −1 ± √5。因 m 必须在 [0, 2] 内,舍去负根,得 m = √5 − 1 ≈ 1.236。
Solution (e). Set F(m) = 1/2: (1/4)(m + m²/2) = 1/2, i.e. m² + 2m − 4 = 0. Solving gives m = −1 ± √5. Since m must lie in [0, 2], discard the negative root: m = √5 − 1 ≈ 1.236.
例题 2(指数分布应用)。某品牌灯泡寿命 X(小时)服从参数 λ = 0.002 的指数分布。求一只灯泡寿命超过 500 小时的概率。由 P(X > x) = e^(−λx),P(X > 500) = e^(−0.002×500) = e^(−1) ≈ 0.3679。这道题用 CDF 一步完成,不需要任何积分计算——这就是熟记”指数分布尾概率公式”的回报。
Example 2 (exponential application). The lifetime X (in hours) of a brand of light bulb follows an exponential distribution with parameter λ = 0.002. Find the probability that a bulb lasts more than 500 hours. Using P(X > x) = e^(−λx), we get P(X > 500) = e^(−0.002×500) = e^(−1) ≈ 0.3679. This question is done in one step with the CDF — no integration needed. That is the payoff for memorising the exponential tail formula.
例题 3(正态标准化)。X ~ N(100, 16),求 P(96 < X < 108)。标准化:Z₁ = (96 − 100)/4 = −1,Z₂ = (108 − 100)/4 = 2。于是 P(96 < X < 108) = P(−1 < Z < 2) = P(Z < 2) − P(Z < −1) = 0.9772 − 0.1587 = 0.8185。注意:P(Z < −1) 用对称性 = 1 − P(Z < 1) = 1 − 0.8413 = 0.1587。
例题 4(均匀分布快速题)。地铁列车每 8 分钟一班,乘客随机到站,设等待时间 X ~ U(0, 8)。(a) 求等待超过 5 分钟的概率;(b) 求平均等待时间与标准差。解答:(a) P(X > 5) = (8 − 5)/8 = 3/8 = 0.375;(b) E(X) = (0 + 8)/2 = 4 分钟,Var(X) = 64/12 = 16/3,故标准差 σ = √(16/3) = 4/√3 ≈ 2.31 分钟。注意均匀分布的期望恰为区间中点——这是不需要任何积分的送分结论。
Example 4 (a quick uniform question). Trains arrive every 8 minutes and a passenger arrives at random, so the waiting time X ~ U(0, 8). (a) Find the probability of waiting more than 5 minutes; (b) find the mean waiting time and the standard deviation. Solution: (a) P(X > 5) = (8 − 5)/8 = 3/8 = 0.375; (b) E(X) = (0 + 8)/2 = 4 minutes, Var(X) = 64/12 = 16/3, so the standard deviation is σ = √(16/3) = 4/√3 ≈ 2.31 minutes. Notice that the mean of a uniform distribution is exactly the midpoint of the interval — an easy mark that needs no integration at all.
Example 3 (normal standardisation). X ~ N(100, 16), find P(96 < X < 108). Standardise: Z₁ = (96 − 100)/4 = −1 and Z₂ = (108 − 100)/4 = 2. So P(96 < X < 108) = P(−1 < Z < 2) = P(Z < 2) − P(Z < −1) = 0.9772 − 0.1587 = 0.8185. Note P(Z < −1) follows from symmetry: 1 − P(Z < 1) = 1 − 0.8413 = 0.1587.
复习路径建议:先把四条核心公式——归一性、区间概率、期望、方差——抄在一张 A4 纸上默写三遍,确保不看书也能完整写出;接着合上笔记,独立完成例题 1 的全部五个小问,卡住的地方回到对应章节再看一遍;最后限时 15 分钟,做一套 Edexcel Further Statistics 真题中连续随机变量的部分,对照评分方案逐条核对自己的方法分。坚持完成三套以上真题,这一考点基本可以做到不丢分。数学没有捷径,但真题是最短的捷径。
A suggested revision path: copy the four core formulas — normalisation, interval probability, expectation, variance — onto one sheet of A4 and write them from memory three times until they flow without notes; then close your notes and work through all five parts of Example 1 independently, returning to the relevant section whenever you get stuck; finally, give yourself 15 minutes on the continuous random variable questions of an Edexcel Further Statistics past paper and check your method marks against the mark scheme one by one. After three past papers, this topic is essentially secure. There is no shortcut in mathematics, but past papers are the shortest path there is.
三条例题分别覆盖了”分段 PDF 全套路””指数分布尾概率”和”正态标准化”,正是 Edexcel 连续随机变量考点的三大支柱。做题时坚持”先写公式、再代入、后化简”的书写习惯,既能避免计算错误,也能让阅卷老师轻松找到给分点。
These three examples cover the three pillars of the Edexcel syllabus on continuous random variables: the full piecewise-PDF routine, the exponential tail probability, and normal standardisation. Always write the formula first, then substitute, then simplify — this habit prevents arithmetic slips and makes it easy for examiners to award method marks.
连续随机变量看似抽象,本质却只有一个:概率是面积,密度是曲线。把 PDF、CDF、期望、方差四件工具与均匀、指数、正态三个分布串联起来,配合真题练习,这部分分数完全可以稳稳拿下。祝备考顺利!
Continuous random variables may look abstract, but the essence is a single idea: probability is area, and density is the curve. Connect the four tools — PDF, CDF, expectation, variance — with the three distributions — uniform, exponential, normal — and practise with past papers, and these marks are firmly within reach. Good luck with your revision!
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