https://file.aiquickdraw.com/mathbot/file/1729782725858tjmvpzwi.jpg

To help you with the assignment from the image, here's a breakdown of the steps:

1. **Select a quarterback and game**:
   - Choose one quarterback and their corresponding game from the list on the right (e.g., Aaron Rodgers, Green Bay Packers, 9/11/2016 vs. Jacksonville Jaguars).
   
2. **Formulate the research question**:
   - Question of interest: You could frame it as, "Is the quarterback's pass completion rate significantly different from a league average?"
   - Null hypothesis (H₀): The quarterback's pass completion rate is equal to the league average.
   - Alternative hypothesis (H₁): The quarterback's pass completion rate is not equal to the league average (could be greater or lesser depending on the data).

3. **Analyze the data**:
   - Go to the sports-reference website, locate the game logs, and record the quarterback's pass sequence from the selected game (C for completed, I for incomplete/intercepted). This will be your observed data under "Results of observed PERFORMANCES".

4. **Simulation**:
   - Use the "Streaks" applet provided on the publisher’s website. The instructions are likely in your textbook, but this is to simulate possible pass completion streaks and compare them to what you observed.

5. **Hypothesis testing**:
   - Complete the hypothesis test by calculating the test statistic, creating a dot plot from the simulation, finding the p-value, and making a conclusion based on whether the result is statistically significant.

The question being tested: **Is the quarterback's pass completion rate in this game significantly different from the league average?**

Would you like more details on how to approach any particular part of the assignment?

Here are 5 related questions for further exploration:
1. How do you calculate the p-value for a hypothesis test in this context?
2. What assumptions are made when simulating pass completions using streaks?
3. How does the null hypothesis influence the structure of a hypothesis test?
4. What is the significance level, and how is it used to interpret the results?
5. What are the potential errors in hypothesis testing, and how can they affect your conclusions?

**Tip**: When analyzing the game logs, make sure to double-check for any interceptions, as they count as incomplete passes in most analyses.

What question is being tested in the hypothesis test for a quarterback's pass completion rate?

Explore hypothesis testing with quarterback pass completion data. Learn how to frame questions, simulate results, calculate p-values, and reach statistically significant conclusions. Perfect for students studying hypothesis tests and probability in college-level statistics.

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