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It looks like you’ve uploaded an image containing a conditional probability exercise, but I can't interact with the text from the image directly. However, I can analyze what I see and guide you through the solution process. Here's a breakdown based on what I can observe:

### Problem Setup:
The exercise is about conditional probability, based on a survey conducted by *Money Magazine* involving **1,010 married adults** with household incomes over $50,000. The survey asks, "Who is better at getting deals?" and breaks down responses by **Husband**, **Wife**, and **We are equal**.

### Table Entries:
The table is organized by the **respondent's answer** (Husband or Wife) and the **categories of responses** (I am, My Spouse, We are equal). We need to complete this table and answer several questions.

#### Part (a): Joint Probability Table
You'll need to fill in the joint probability table. This means calculating the probability for each respondent (Husband/Wife) in each category (I am/My Spouse/We are Equal). Here's the approach:

1. **Calculate Totals**:
   - For Husbands: $ 278 + 127 + 102 $
   - For Wives: $ 290 + 111 + 102 $

2. **Find Overall Total**:
   - Add all values together to get the grand total.

3. **Joint Probabilities**:
   - Each joint probability is calculated as the frequency divided by the total number of respondents (1,010).

#### Part (b): Marginal Probabilities
These are already given as:
- Probability of "I Am" = 0.5624
- Probability of "My Spouse" = 0.2356
- Probability of "We Are Equal" = 0.2010

You will use these marginal probabilities to answer subsequent questions.

#### Part (c): Conditional Probability (Husband thinks he's better)
Given that the respondent is a husband, find the probability that he feels he is better at getting deals than his wife. This will be calculated by:
\[
P(\text{I Am} | \text{Husband}) = \frac{P(\text{I Am and Husband})}{P(\text{Husband})}
\]

#### Part (d): Conditional Probability (Wife thinks she's better)
This is similar to part (c), but now you're looking for:
\[
P(\text{I Am} | \text{Wife}) = \frac{P(\text{I Am and Wife})}{P(\text{Wife})}
\]

#### Part (e): Conditional Probability (Response "My Spouse")
Given the response was "My Spouse," find the probability the response came from a husband. This uses:
\[
P(\text{Husband} | \text{My Spouse}) = \frac{P(\text{Husband and My Spouse})}{P(\text{My Spouse})}
\]

#### Part (f): Conditional Probability (Response "We Are Equal")
Similar to part (e), but for "We Are Equal":
\[
P(\text{Husband} | \text{We Are Equal}) = \frac{P(\text{Husband and We Are Equal})}{P(\text{We Are Equal})}
\]

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Would you like me to guide you further in calculating these values? Let me know if you'd like detailed steps for any specific part. Here are some follow-up questions that might help:

1. How do you calculate joint probabilities using survey results?
2. What is the significance of marginal probabilities in conditional probability questions?
3. How does Bayes' Theorem apply to parts (e) and (f)?
4. Why is it necessary to normalize by the total number of responses?
5. What role do conditional probabilities play in real-world decision-making?

**Tip**: Always ensure that the probabilities sum up to 1 when working with joint or marginal distributions.

Who is better at getting deals? Conditional probability problem based on a Money Magazine survey of 1,010 married adults.

Learn to calculate joint, marginal, and conditional probabilities based on a survey of 1,010 married adults. This detailed breakdown uses real-world data to illustrate key concepts in probability, including Bayes' Theorem. Suitable for advanced high school or college students.

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