Esta lección aún no está traducida. Por ahora se muestra en inglés; la versión en español está en camino.
So far events were described in words: "even," "a heart." Often the outcome we care about is a number: the number of heads, the sum of two dice, the number of customers in line. A random variable turns each outcome of a sample space into a number, so that we can compute with it.
🔑 Idea clave
A random variable $X$ assigns a number to each outcome of a chance process. A discrete random variable has separate possible values (often counts). Its probability distribution lists each value $x$ with its probability $P(X = x)$. Every probability is between 0 and 1, and together they add to exactly 1.
💡 Por qué funciona
The values of $X$ split the sample space into separate pieces: every outcome gives exactly one value. So the pieces cover all of $S$ without overlapping, and their probabilities must add to $P(S) = 1$. That gives a free check on every table, and a way to fill in one missing probability.
✏️ Ejemplo resuelto: Building a distribution
Flip a fair coin 3 times. Let $X$ be the number of heads. Find the probability distribution of $X$.
The sum of two dice is a random variable with values 2 to 12. Roll many times: the histogram settles onto the true distribution (outlined), and the proportion of 7s settles near 6/36.
⚠️ Error común
Writing a probability table whose probabilities do not add to 1, often by leaving out a value (such as 0 pets) or double-counting outcomes. Check every table by adding the probabilities.
Práctica
Apruebas con 3 respuestas correctas, las 2 últimas seguidas. Cada respuesta correcta da 1 XP.