Stat 200
Solutions to Inference Examples
These correspond to Examples 1 through 14, covered in Lecture 23
- Can't be solved, because the sample is not random.
- Can't be solved; this is a cluster sample, not an SRS.
- Can't be solved; observations are time-dependent and so do not
constitute an SRS
- Can't be solved; the distribution is skewed, not normal.
-
- 34.2 plus or minus 1.96(5.9)/(square root of 68)= (32.8, 35.6)
- Ho: mu=30 vs. Ha: mu>30; z = (34.2-30)/(5.9/square root of 68)=5.87,
so P-value is approximately 0; reject the null hypothesis and conclude that
mu>30. Note that 30 is far from being contained in the confidence interval
computed in part (a).
-
- 9.8 plus or minus 1.645(1.2)/(square root of 62)= (9.55, 10.05)
- Ho: mu=10 vs. Ha: mu<10; z = (9.8-10)/(1.2/square root of 62)= -4.59,
so P-value=P(Z-1.31)=.0951; in general this wouldn't be small enough to
reject the null hypothesis; we conclude that mu may equal 10. Note that
10 is contained in the confidence interval in (a), but just barely.
- Ho:mu=30 vs. Ha: mu>30; z = (38.1-30)/(10/square root of 22)= 3.8,
P-value = P(Z>3.8) = approximately 0; reject Ho and conclude mu>30.
- Ho: mu=1.5 vs. Ha: mu not equal to 1.5; z = (1.278-1.5)/
(1.6/square root of 18)= -.59, P-value = 2P(Z>|-.59|)=2(.2776)= .5552;
can't reject Ho, so we conclude that mu may well equal 30.
- Ho: mu=40 vs. Ha: mu not equal to 40; z = (47.6-40)/(5/square root of 10)
=4.81, P-value = 2P(Z>|4.81|)= approximately 0; reject Ho and conclude
that mu does not equal 40.
- Ho: mu=20000 vs. Ha: mu<20000; z=(19695-20000)/(1103/square root of 114)
=-2.95, P-value = P(Z<-2.95)=.0016; reject Ho and conclude mu<20000.
- Ho:mu=55 vs. Ha: mu>55; z =(58-55)/(20/10)= 1.5, P-value = P(Z>1.5)
= .0668, can't reject Ho, so we conclude mu may be 55.
- Ho: mu=1250 vs. Ha: mu<1250
- z = -2, P-value = .0228; reject Ho and conclude mu<1250
- z = -3, P-value = .0013; now there is even more evidence to reject Ho.
- Given s, not sigma, so we can't solve this by the methods of Chapter 6;
we'll learn to solve such problems in Chapter 7.
- This problem involves categorical data; we'll learn how to test
hypotheses about proportions in Chapter 8.
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