College

Select the correct answer.



\[

\begin{tabular}{|c|c|c|c|c|}

\hline

\text{Weight/Calories per Day} & \text{1000 to 1500 cal.} & \text{1500 to 2000 cal.} & \text{2000 to 2500 cal.} & \text{Total} \\

\hline

\text{120 lb.} & 90 & 80 & 10 & 180 \\

\hline

\text{145 lb.} & 35 & 143 & 25 & 203 \\

\hline

\text{165 lb.} & 15 & 27 & 75 & 117 \\

\hline

\text{Total} & 140 & 250 & 110 & 500 \\

\hline

\end{tabular}

\]



Based on the data in the two-way table, what is the probability that a person weighs 120 pounds, given that he or she consumes 2,000 to 2,500 calories per day?



A. 0.09

B. 0.12

C. 0.22

D. 0.35

Answer :

- Define events: A (weighs 120 lb), B (consumes 2000-2500 calories).
- Calculate the conditional probability: $P(A|B) = \frac{{P(A \text{{ and }} B)}}{{P(B)}}$.
- Extract values from the table: $P(A \text{{ and }} B) = 10$, $P(B) = 110$.
- Compute the probability: $P(A|B) = \frac{{10}}{{110}} = \frac{{1}}{{11}} \approx 0.09$. The answer is $\boxed{0.09}$.

### Explanation
1. Understand the problem
We are given a two-way table that shows the distribution of people by weight and calorie consumption. We want to find the probability that a person weighs 120 pounds, given that they consume 2000 to 2500 calories per day. This is a conditional probability problem.

2. Define events and conditional probability
Let A be the event that a person weighs 120 pounds.
Let B be the event that a person consumes 2000 to 2500 calories per day.
We want to find P(A|B), which is the probability of A given B.
P(A|B) = P(A and B) / P(B).

3. Extract data from the table
From the table, the number of people who weigh 120 lb and consume 2000-2500 calories is 10.
The total number of people who consume 2000-2500 calories is 110.

4. Calculate the conditional probability
P(A|B) = (Number of people who weigh 120 lb and consume 2000-2500 calories) / (Total number of people who consume 2000-2500 calories) = 10 / 110 = 1/11.

5. Find the closest answer
1/11 is approximately 0.0909. Looking at the answer choices, the closest value is 0.09.

### Examples
Conditional probability is used in many real-world scenarios, such as medical diagnosis, risk assessment, and marketing. For example, a doctor might use conditional probability to determine the probability that a patient has a certain disease, given that the patient has certain symptoms. In marketing, conditional probability can be used to determine the probability that a customer will buy a product, given that the customer has certain characteristics.