Tony is playing a game in which he spins a spinner with 6 equal-sized slices numbered 1 through 6. The spinner stops on a numbered slice at random. This game is this: Tony spins the spinner once. He wins $1 if the spinner stops on the number 1, $3 if the spinner stops on the number 2, $5 if the spinner stops on the number 3, and $7 if the spinner stops on the number 4. He loses $8 if the spinner stops on 5 or 6. (a) Find the expected value of playing the game. dollars (b) What can Tony expect in the long run, after playing the game many times? O Tony can expect to gain money. He can expect to win dollars per spin. O Tony can expect to lose money. He can expect to lose dollars per spin. O Tony can expect to break even (neither gain nor lose money). 5 ?

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**Title: Expected Value in Probability Games**

**Tony's Spinner Game: Understanding Expected Value**

Tony is playing a game in which he spins a spinner with 6 equal-sized slices numbered 1 through 6. The spinner stops on a numbered slice at random.

**The Game Rules:**
- Tony spins the spinner once.
- He wins $1 if the spinner stops on the number 1.
- He wins $3 if the spinner stops on the number 2.
- He wins $5 if the spinner stops on the number 3.
- He wins $7 if the spinner stops on the number 4.
- He loses $8 if the spinner stops on 5 or 6.

**Problem Analysis:**

(a) **Find the expected value of playing the game.**
   \[ \text{Expected value} = \_ \_ \_ \_ \_ \text{ dollars} \]

(b) **What can Tony expect in the long run, after playing the game many times?**

   - Tony can expect to gain money. He can expect to win \_ \_ \_ \_ \_ dollars per spin.
   - Tony can expect to lose money. He can expect to lose \_ \_ \_ \_ \_ dollars per spin.
   - Tony can expect to break even (neither gain nor lose money).

**Calculation:**

To determine the expected value, we multiply each outcome by its probability and sum the results.

Here are the possible winnings and their corresponding probabilities:
1. Win $1: \(\frac{1}{6}\)
2. Win $3: \(\frac{1}{6}\)
3. Win $5: \(\frac{1}{6}\)
4. Win $7: \(\frac{1}{6}\)
5. Lose $8: \(\frac{2}{6}\)

The expected value (E) can be calculated using the formula:
\[ E = \left(1 \times \frac{1}{6}\right) + \left(3 \times \frac{1}{6}\right) + \left(5 \times \frac{1}{6}\right) + \left(7 \times \frac{1}{6}\right) + \left(-8 \times \frac{2}{6}\right) \]

This educational module explores how understanding expected value in probability can illuminate the long-term outcomes of
Transcribed Image Text:**Title: Expected Value in Probability Games** **Tony's Spinner Game: Understanding Expected Value** Tony is playing a game in which he spins a spinner with 6 equal-sized slices numbered 1 through 6. The spinner stops on a numbered slice at random. **The Game Rules:** - Tony spins the spinner once. - He wins $1 if the spinner stops on the number 1. - He wins $3 if the spinner stops on the number 2. - He wins $5 if the spinner stops on the number 3. - He wins $7 if the spinner stops on the number 4. - He loses $8 if the spinner stops on 5 or 6. **Problem Analysis:** (a) **Find the expected value of playing the game.** \[ \text{Expected value} = \_ \_ \_ \_ \_ \text{ dollars} \] (b) **What can Tony expect in the long run, after playing the game many times?** - Tony can expect to gain money. He can expect to win \_ \_ \_ \_ \_ dollars per spin. - Tony can expect to lose money. He can expect to lose \_ \_ \_ \_ \_ dollars per spin. - Tony can expect to break even (neither gain nor lose money). **Calculation:** To determine the expected value, we multiply each outcome by its probability and sum the results. Here are the possible winnings and their corresponding probabilities: 1. Win $1: \(\frac{1}{6}\) 2. Win $3: \(\frac{1}{6}\) 3. Win $5: \(\frac{1}{6}\) 4. Win $7: \(\frac{1}{6}\) 5. Lose $8: \(\frac{2}{6}\) The expected value (E) can be calculated using the formula: \[ E = \left(1 \times \frac{1}{6}\right) + \left(3 \times \frac{1}{6}\right) + \left(5 \times \frac{1}{6}\right) + \left(7 \times \frac{1}{6}\right) + \left(-8 \times \frac{2}{6}\right) \] This educational module explores how understanding expected value in probability can illuminate the long-term outcomes of
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