Name: finding probability with area under a pdf
Uploaded: 2026-01-12T12:24:35.000Z
Duration: 1 min 27 s
Channel: JoVE

Le distribuzioni di probabilità continue modellano variabili casuali che possono assumere qualsiasi valore reale all'interno di un intervallo. Ad esempio, l'altezza delle femmine adulte può essere di 163,5, 165,25 centimetri o qualsiasi valore intermedio. Questo rende l'altezza una variabile casuale continua.La probabilità di questa variabile è descritta usando una funzione densità di probabilità—una curva liscia sull'intervallo della variabile. L'unità di questa densità è il reciproco dell'unità della variabile.La probabilità totale che la variabile rientri in un intervallo specifico si determina calcolando l'area sotto la curva per quell'intervallo. Ad esempio, la probabilità che l'altezza di una donna sia compresa tra 150 e 170 centimetri si determina integrando la funzione densità su quell'intervallo.Se il risultato è 0,75, significa che il 75% delle donne nella popolazione ha altezze entro quell'intervallo.Ma per un'altezza esatta, la probabilità è zero perché integrare su un singolo punto dà area zero.Una funzione di densità di probabilità valida è sempre non negativa e si integra a 1 su tutto l'intervallo di valori possibili per la variabile casuale.

applicazioni dell’integrazione alle funzioni di densità di probabilità

Applications of Integration to Probability Density Functions

Le distribuzioni di probabilit&#xE0; continue sono utilizzate per modellare variabili aleatorie che possono assumere qualsiasi valore reale in un dato intervallo. Queste variabili non assumono valori isolati o numerabili, ma variano su un continuum. Ad esempio, l&#x2019;altezza di un individuo pu&#xF2; essere misurata con precisione crescente &#x2013; come 163,5 o 165,25 cm &#x2013; mostrando che l&#x2019;altezza &#xE8; una variabile casuale continua.Il comportamento di tali variabili &#xE8; descritto mediante una funzione di densit&#xE0; di probabilit&#xE0;, o PDF, che definisce come le probabilit&#xE0; si distribuiscono sull&#x2019;insieme dei valori possibili. A differenza delle distribuzioni discrete, in cui i singoli esiti hanno probabilit&#xE0; specifiche, le distribuzioni continue assegnano probabilit&#xE0; a intervalli di valori. La probabilit&#xE0; totale che una variabile cada in un determinato intervallo &#xE8; rappresentata dall&#x2019;area sottesa alla curva della funzione di densit&#xE0; di probabilit&#xE0; in tale intervallo. Ci&#xF2; si calcola mediante integrazione:\begin{equation*}P(a \leq X \leq b) = \int_{a}^{b} f(x)\, dx\end{equation*}Ad esempio, se l&#x2019;area sotto la curva compresa tra 150 e 170 cm &#xE8; pari a 0,75, allora vi &#xE8; una probabilit&#xE0; del 75 % che un individuo selezionato casualmente abbia un&#x2019;altezza compresa in tale intervallo. Tuttavia, la probabilit&#xE0; che la variabile assuma un singolo valore esatto &#xE8; sempre zero, poich&#xE9; l&#x2019;area sottesa in corrispondenza di un singolo punto &#xE8; nulla.Per essere valida, una funzione di densit&#xE0; di probabilit&#xE0; deve essere non negativa per ogni valore e deve avere integrale pari a 1 sull&#x2019;intero dominio della variabile. Queste propriet&#xE0; garantiscono che la funzione rappresenti correttamente una distribuzione di probabilit&#xE0; e che la probabilit&#xE0; totale su tutti i possibili valori sia uguale a 1.

Applicazioni dell’integrazione alle funzioni di densità di probabilità

Le distribuzioni di probabilit&#xE0; continue sono utilizzate per modellare variabili aleatorie che possono assumere qualsiasi valore reale in un dato ...

applications-of-integration-to-probability-density-functions

Education

Didattica

JoVE Core

Calculus

Analisi matematica

Unit

Unità

Application of Techniques of Integration

Applicazione delle tecniche di integrazione

KEY TERMS AND CONCEPTS

TERMINI E CONCETTI CHIAVE

finding probability with area under a pdf

Continuous probability distributions describe random variables that can take any real value within a set range. Height is one example, since it can be measured with growing precision, such as 163.5 or 165.25 centimeters. That makes height a continuous random variable.

The pattern of a continuous random variable is shown with a probability density function, or PDF. A PDF shows how probability is spread across the possible values. In a continuous distribution, probabilities are assigned to intervals of values, not to single outcomes.

Integration gives the total probability for a chosen interval. The area under the PDF curve over that interval is the probability. For example, if the area under the curve from 150 to 170 centimeters is 0.75, then there is a 75% chance that a randomly selected individual falls in that height range.

A single exact value has probability zero in a continuous distribution. That is because the area under one point is zero. A valid PDF must also stay non-negative for all values and integrate to 1 over the entire domain. These rules make sure the function represents a true probability distribution and that the total probability across all possible outcomes equals 1.

Finding Probability with Area Under a PDF

finding-probability-with-area-under-a-pdf

arc length of a curve

Measuring Curved Paths with Calculus

Measuring curved paths with calculus is important in engineering projects. Roller coaster tracks, cables, and railway lines often follow smooth curves instead of straight lines. Accurate path length helps with planning and design. A curve is first split into small segments. Each short segment is treated as a straight line between two nearby points on the curve. Adding these line segments gives an estimate of the total distance along the path. The estimate becomes better when the segments are...

arc length of a curve: problem solving

Catenary Cable Length in Power Lines

Catenary cable length helps engineers measure the full span of a hanging power line. A high-voltage line stretches 40 meters between two transmission towers and sags 10 meters because of gravity and thermal expansion. The cable forms a catenary, which is the curve of a uniform, flexible cable under its own weight. A catenary is not the same as a parabola. It is described by the hyperbolic cosine function, which gives a more accurate shape for a suspended cable. In this setup, engineers use the...

arc length function

Measuring Distance Along a Curve

The arc length function measures the distance traveled along a smooth curve from a fixed starting point to a moving endpoint. It is useful when a straight-line estimate is not accurate enough. For curves that are continuous and differentiable, arc length gives a precise way to track length along the curve. To find this distance, the curve is broken into many small pieces. Each piece is treated like a short straight line. Its length depends on the horizontal change and the vertical change over...

area of a surface of revolution

Calculating Surface Area from Rotation

Surfaces of revolution are created when a two-dimensional curve is rotated around an axis. The rotation produces a three-dimensional shape. In engineering, this calculation can help determine the surface area of a rocket nozzle. That information matters when a heat-resistant coating must be applied evenly. When a curve is revolved about the x-axis, it sweeps out a continuous surface. To find its area, the curve is first broken into small straight-line pieces over short intervals. Each rotated...

applications of integration to find hydrostatic pressure

applications-of-integration-to-find-hydrostatic-pressure

Hydrostatic Force on Dam Walls with Integration

Hydrostatic force is the total force a fluid at rest exerts on a surface. For a horizontal surface at a fixed depth, the pressure is constant. It is found by multiplying fluid density, gravitational acceleration, and depth. A vertical dam wall is different because pressure increases with depth. The water above each point adds to the pressure below it. That means the force is not spread evenly across the wall. Integration helps measure this changing force. Pressure in a still fluid rises...

applications of integration to find centers of mass

applications-of-integration-to-find-centers-of-mass

Center of Mass in Discrete and Continuous Systems

Center of mass can be found from rotational equilibrium and from integration. A balanced plank on a pivot shows the idea clearly. When the net torque about the pivot is zero, the system is in balance. Torque is the force on an object times its perpendicular distance from the pivot. At that point, the pivot lies at the center of mass. For a set of discrete point masses, the center of mass is the position where the masses balance by weight. Each mass contributes its mass times its position,...

theorem of pappus

Pappus Theorem for Volume and Surface Area

Pappus Theorem connects centroid motion to the volume and surface area of solids of revolution. It is also called the Pappus–Guldinus Theorem. The method uses geometry to find these values when a plane region or a plane curve is rotated about an external axis. The theorem has two related parts. The first part gives the volume of a solid formed by rotating a plane region around an axis outside the region. In this case, the volume equals the area of the region multiplied by the distance traveled...

applications of integration to find consumer surplus

applications-of-integration-to-find-consumer-surplus

Consumer Surplus from Demand and Integration

Consumer surplus measures the economic gain buyers receive when they pay less than the highest price they are willing to pay. In microeconomics, this idea comes from the demand function, which links quantity to the price consumers will pay. Because willingness to pay usually falls as quantity rises, the demand curve slopes downward. When a product is sold at a fixed price, some buyers would have paid more for the first units they bought. The difference between that willingness to pay and the...

applications of integration to find blood flow

applications-of-integration-to-find-blood-flow

Using Integration to Model Blood Flow

Blood flow in a cylindrical blood vessel can be modeled with integration and laminar flow. Laminar flow is smooth motion in parallel layers. In this model, blood speed changes with distance from the center of the vessel. The blood moves fastest along the central axis. As the distance from the center increases, the velocity decreases. At the vessel wall, the velocity drops to zero because of viscous drag. This changing speed across the cross-section is the key idea behind the calculation. To...

introduction to normal distributions

Normal Distribution in Test Scores

Normal distribution helps explain how standardized test scores can be spread across a group. It is a key idea in statistics for describing symmetry, center, and variability in large sets of data. Test score data often start as a histogram. In a histogram, each bar shows how many scores fall within a score range. To turn that count-based graph into a continuous probability model, the histogram is normalized by dividing each bar height by the total number of observations and by the bin width. <br...

applications of integration to probability density functions

Continuous probability distributions describe random variables that can take any real value within a set range. Height is one example, since it can be measured with growing precision, such as 163.5 or 165.25 centimeters. That makes height a continuous random variable. The pattern of a continuous random variable is shown with a probability density function, or PDF. A PDF shows how probability is spread across the possible values. In a continuous distribution, probabilities are assigned to...

key terms and concepts

Learn how continuous probability distributions use PDFs and integration to find probability from the area under a curve, using height as an example.

Finding Probability with Area Under a PDF | JoVE K12

The JoVE Core Calculus collection provides a comprehensive visual guide to the entire calculus sequence, seamlessly connecting mathematical theory to real-world applications in physics and economics. Through dynamic animated lessons, the course transitions smoothly from foundational single-variable concepts to advanced multivariable topics. Students will explore everything from standard differentiation and integration to spatial vectors, multi-dimensional derivatives, and complex vector fields. By breaking down abstract coordinate systems, infinite series, and spatial motion, this structured approach builds the robust analytical skills and advanced mastery essential for STEM success.

calculus

Finding Probability with Area Under a PDF

Description

Transcript

Tags