HW10 Skeleton

10.1

• 1. Hypothesis.
• 2. Hypothesis testing.
• 3. Null hypothesis.
• 4. Alternative hypothesis.
• 5. Type I error.
• 6. Type II error.
• 7. Level of significance.
• 8. False since we are using a parameter.
• 9. Right-tailed, population mean.
• 10. Left-tailed, population proportion.
• 11. Two tailed, standard deviation.

 

18. 1. h0: p=32, h1: p doesn’t = 32.

    2. The consumer is making a type I error by saying that the peanut butter in the jar is < 32 ounces when it is actually 32 ounces.

    3. If the peanut butter factory doesn’t reject that its 32 ounces when it is not 32 ounces, this is a type II error.

19. 1. h0: standard deviation = 0.7psi, h1: standard deviation < 0.7psi.

    2. the quality-control manager rejects the hypothesis that the variability in the pressure required is 0.7psi, when the actual variability is 0.7psi.

    3. the quality-control manager doesn’t reject that the variability in the pressure required is 0.7psi when the variability is less than 0.7psi.

 

36. 1. h0: population mean = 54, h1: population mean > 54.

    2. The campaign promoting consumers to eat more popcorn was successful. Our research demonstrates that the mean annual consumption of popcorn is now greater than 54 quarts (the mean consumption before the campaign).

    3. Type I error, probability of a type I error is 0.

37. 1. h0: p = 0.028, h1: p > 0.028.

    2. There is not enough evidence to conclude the proportion of high school students is > 0.028 at the counselor’s high school.

    3. A type II error occurred because the sample evidence led the counselor to conclude the proportion of e-cig users at her school was 0.028, when the proportion is actually higher.