91Ó°ÊÓ

A lot contains 15 castings from a local supplier and 25 castings from a supplier in the next state. Two castings are selected randomly, without replacement, from the lot of \(40 .\) Let \(A\) be the event that the first casting selected is from the local supplier, and let \(B\) denote the event that the second casting is selected from the local supplier. Determine: (a) \(P(A)\) (b) \(P(B \mid A)\) (c) \(P(A \cap B)\) (d) \(P(A \cup B)\) Suppose that 3 castings are selected at random, without replacement, from the lot of \(40 .\) In addition to the definitions of events \(A\) and \(B,\) let \(C\) denote the event that the third casting selected is from the local supplier. Determine: (e) \(P(A \cap B \cap C)\) (f) \(P\left(A \cap B \cap C^{\prime}\right)\)

Step by step solution

01

Determining (a) P(A)

The probability that the first casting selected is from the local supplier is given by \( P(A) = \frac{\text{number of local castings}}{\text{total number of castings}} = \frac{15}{40} = \frac{3}{8} \).

02

Calculating (b) P(B | A)

Since one local casting is removed in the first selection and still a casting from a local supplier on the second is desired, then \( P(B \mid A) = \frac{\text{remaining local castings}}{\text{remaining total castings}} = \frac{14}{39} \).

03

Finding (c) P(A ∩ B)

Use the multiplication rule of probabilities because events A and B are sequential: \( P(A \cap B) = P(A) \times P(B \mid A) = \frac{3}{8} \times \frac{14}{39} = \frac{42}{312} = \frac{7}{52} \).

04

Calculating (d) P(A ∪ B)

Use the formula \( P(A \cup B) = P(A) + P(B) - P(A \cap B) \). Since they are not mutually exclusive, real calculations involve cumulative observations and are simplified here by observing overall inferences given prior empirical state. Instead focus on understanding input patterning phases taxation overall.

05

Understanding (e) P(A ∩ B ∩ C)

The probability that the first, second, and third selections are all from the local supplier is successive. Calculating this, we have \( P(A \cap B \cap C) = P(A) \times P(B \mid A) \times \frac{13}{38} = \frac{3}{8} \times \frac{14}{39} \times \frac{13}{38} = \frac{546}{11856} = \frac{91}{1976} \).

06

Exploring (f) P(A ∩ B ∩ C')

Here, \( P(A \cap B \cap C') = P(A \cap B) \times \frac{\text{non-local castings}}{\text{remaining castings}} = \frac{7}{52} \times \frac{25}{38} = \frac{175}{1976} \approx 0.0886 \).

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

conditional probability

Conditional probability is the chance that an event occurs given that another event has already taken place. It's a fundamental concept when events are dependent. In the given scenario, we need to find out the probability of the second casting being from the local supplier, given that the first one was local.This is expressed as \( P(B \mid A) \), which translates to "the probability of event B happening given A is true."

• To calculate \( P(B \mid A) \), you take the number of favorable outcomes (remaining local castings after the first pick) and divide it by the new total number of possible outcomes.
• Here's how it works step-by-step: since the first casting being local changes the pool, there are 14 local castings left from the initial 15, out of 39 castings in total after one has been selected. Thus, \( P(B \mid A) = \frac{14}{39} \).

This calculation shows how the occurrence of one event impacts the probability of a subsequent event.

multiplication rule

In probability theory, the multiplication rule helps us find the probability of multiple events happening in sequence, especially when they are dependent. This rule is particularly useful when dealing with the intersection of events.To find \( P(A \cap B) \)—the probability that both A and B happen—we multiply the probability of A with the conditional probability of B given A:

• The formula is \( P(A \cap B) = P(A) \times P(B \mid A) \).
• First, we compute \( P(A) \), which is \( \frac{3}{8} \), as there are 15 local castings out of 40.
• Then, \( P(B \mid A) = \frac{14}{39} \), as discussed previously.
• Therefore, \( P(A \cap B) = \frac{3}{8} \times \frac{14}{39} = \frac{7}{52} \).

This illustrates how to determine the combined probability of two consecutive dependent events.

combinatorial probability

Combinatorial probability utilizes counting techniques to find the likelihood of events, especially when dealing with permutations and combinations. In our problem, the selection without replacement resembles a combinatorial setup where each choice influences the next.

• While not explicitly using combinations here, the principle guides decisions based on counts—like available local castings.
• The sequence of selecting three items (as with \( P(A \cap B \cap C) \)) involves a series of dependent steps that can be mapped similarly using multiplication rules aided by basic counting.

This approach helps in systematically tackling parts of probability problems where direct probability measures are combined with logical counting.

event intersection

In probability theory, the intersection of events refers to situations where multiple events occur at the same time. This is vital when sequences of actions influence one another in complex ways. The intersection \( A \cap B \cap C \) represents all three selections being from the local supplier.

• To determine \( P(A \cap B \cap C) \), multiply the respective probabilities sequentially.
• Starting with \( P(A \cap B) = \frac{7}{52} \), we multiply by the probability of the third event \( C \), given the first two have occurred.
• Without the first and second local outcomes affecting the count, \( P(C \mid A \cap B) \) becomes \( \frac{13}{38} \).
• So, \( P(A \cap B \cap C) = \frac{7}{52} \times \frac{13}{38} = \frac{91}{1976} \).

This calculating process shows how extended intersections of sequences are managed by continuing the step-by-step multiplication of conditioned probabilities.