It looks like you've uploaded an image. Let me examine it and provide an analysis or answer to your query. PIL import Image

Open the image file

img_path = "/mnt/data/17335273380373kwqsayb.jpg" img = Image.open(img_path)

Display the image

img.show()I have displayed the image for analysis, but I can't yet extract the mathematical content from it. Let me work on extracting and solving any relevant problems or providing an explanation based on the content of the image. pytesseract

Use pytesseract to extract text from the image

extracted_text = pytesseract.image_to_string(img)

Output the extracted text for analysis

extracted_textThe text extracted from the image appears to be related to a hypothesis testing problem. Here’s a summary of the key points:

Problem Context:

• A drug testing company tested marijuana usage among 292 subjects.
• 27 subjects had incorrect test results.
• The hypothesis test aims to check if less than 10% of the test results are wrong, using a significance level to test the claim.

Hypotheses:

• Null hypothesis (H₀): $p = 0.1$ (10% of the results are wrong).
• Alternative hypothesis (H₁): $p < 0.1$ (less than 10% of the results are wrong).

Test Statistic and P-value:

• Test statistic: $-0.43$ (rounded to two decimal places).
• P-value: $0.334$ (rounded to three decimal places).

Conclusion:

• Based on the provided options, the conclusion would be:
  • A. Fail to reject H₀. There is not sufficient evidence to support the claim that less than 10% of the test results are wrong.

Let me know if you'd like to delve deeper into any part of this problem or need further clarification!

Follow-up Questions:

1. How do you calculate the test statistic for a hypothesis test like this one?
2. What does the P-value represent in hypothesis testing?
3. How do you determine whether to reject or fail to reject the null hypothesis?
4. What is the difference between a one-tailed and two-tailed hypothesis test?
5. How would the conclusion change if the P-value were smaller than the significance level?

Tip: In hypothesis testing, always check the P-value against your significance level (α) to make a decision. If the P-value is less than α, you reject the null hypothesis.