University of Minnesota, Twin Cities School of Statistics Stat 3011 Rweb Textbook (Wild and Seber)
Go to assignment: 1 2 3 4 5 6 7 8 9 10 11 12
Note: The problems assigned will all be "Exercises" or "Review Exercises" not "Quiz" questions.
No. | Due Date | Ch. or Sec. | Exercises | Comments |
---|---|---|---|---|
1 | Fri Sep 15 | 1.1 | 2, 4 | |
1.2 | 1 | |||
1.3 | 2 | |||
1 (Review) | 2, 3, 6 12, 16 | |||
2 | Fri Sep 22 | 2.3.1 | 1 | |
2.3.2 | 4 | |||
2.3.3 | 2 | histogram only, use computer and use right=FALSE
for comparison with Figure 2.3.8 in the textbook and the
histogram example from class.
| ||
2.3.4 | 2 | |||
2.4.1 | 4 | use computer to find median | ||
2.4.2 | 1(b), 4 | see data entry example from class. | ||
2.4.3 | 2 | |||
2 (Review) | 4, 6 | |||
3 | Fri Sep 29 | 2 (Review) | 9abde | |
3.1.2 | 1, 3 | |||
3 (Review) | 3 | You don't have to do anything special to avoid plotting the 24th point.
Its y value is NA (no value) so it will be ignored.
| ||
4.3 | 1, 3 | 2 was previously assigned, now dropped | ||
4.4.2 | 3 | |||
4.4.3 | 2 | |||
4.4.4 | 2 | |||
4.5 | 1, 4 | |||
4 (Review) | 2, 3 | |||
4 | Fri Oct 6 | 4.7.1 | 2 | |
4.7.3 | 1 | |||
4 (Review) | 5, 18 | |||
5.2 | 1, 5b-i | |||
5.4.1 | 1 | |||
5.4.2 | 2 | |||
5.4.3 | a-i | no number on question, do all parts | ||
5 (Review) | 11 | |||
A | 1, 2 | "additional problems" see below. | ||
5 | Fri Oct 13 | 6.2.2 | 2, 3 | You should also do 1 in the sense of visiting the examples page but needn't hand anything in. |
6.2.3 | 2, 3 | You should also do 1 in the sense of visiting the examples page but needn't hand anything in. | ||
6.2.4 | 1, 2 | |||
6.4.3 | 1abcf, 2 | |||
6 (Review) | 1, 2, 4, 10 | |||
6 | Fri Oct 27 | 7.2.1 | 1 | |
7.2.2 | 1, 2 | |||
7.2.3 | 1, 3 | |||
7.3.1 | 1, 3 | |||
7.5 | 1, 3 | |||
7 (Review) | 4, 10, 12, 20 | |||
7 | Fri Nov 3 | A | 3, 4, 5, 6 | "additional problems" see below. Note that the notes for 3(c) and 4(c) have been corrected. |
7 (Review) | 15 | |||
8.2 | 2 | Recall that the R command fred <- c(0.513, 0.524, 0.529)
creates a data vector of those three numbers, and similarly for longer
data vectors.
| ||
8.3 | 1, 2 | Do each of these problems twice, once using the
methods described in Wild and Seber getting the answer in the back of the
book, then again using the R function prop.test described
in the Lecture Examples for Chapter 8.
| ||
8 (Review) | 1 | Problem 4, originally assigned, is moved to next week. | ||
8 | Fri Nov 10 | 7.5 | 2 | This problem and the next are paired. The two samples can be obtained
in Rweb by the statements
x <- density[1:6] y <- density[7:29]when you are on the Rweb for 3011 page with the dataset Table 7.2.1 (p. 291) cavend.txtselected in the "Datasets from Wild and Seber" chooser. |
8.4 | 1 | For (a) use the R function t.test , which does the Right
Thing, not the answer in the back of the book.
| ||
8.5 | 1, 2 | |||
8.6 | 1, 2 | |||
8 (Review) | 4, 12 | |||
A | 7, 8 | "additional problems" see below. | ||
9 | Wed Nov 22 | A | 9 | "additional problems" see below. |
9.2 | 1, 3, 4 | |||
9.3 | 2, 3, 7 | |||
9 (Review) | 2, 12, 18 | |||
10 | Fri Dec 1 | 10.1.2 | 1 | |
10.3 | 1, 2, 3, 4, 5 | |||
10 (Review) | 4, 6abdef, 12abcefg | omit part (c) of 6 and part (d) of 12 | ||
11 | Fri Dec 8 | 11.1 | 1 | |
11.2.1 to 11.2.3 | 2 | |||
11 (Review) | 2, 5, 6 | |||
12.1.3 | the exercise | |||
12.2 | the exercise | |||
12.3 | the exercise | Ignore the part of (c) about "Superimpose the same line on your plot in (a)" | ||
12 | Wed Dec 13 | A | 10, 11, 12, 13 | "additional problems" see below. Also see Chi-Square Tests for 2 by 2 Tables for help with additional problem 10 |
12.4.2 | 1, 2 | |||
12 (Review) | 5 |
1. Suppose the probability of a widget being defective is 0.02. Suppose widgets come in boxes of 12. Assume widget defects are statistically independent.
2. For the probability model for the random variable X defined by the following table
x | 0 | 1 |
pr(x) | 1 - p | p |
3. Suppose the random variable T has Student(10) distribution (Student's t-distribution with 10 degrees of freedom).
4. Suppose the random variable T has Student(7) distribution
5. Widgets produced at Acme Widget Works are specified to have 7.00 mm frammis diameter. A random sample of 5 widgets are taken from the production line and their diameters accurately measured. The sample mean was 6.9123 mm and the sample standard deviation 0.0884 mm. Assume that the distribution of frammis diameters is normal, and give an interval that has 95% coverage probability for the true mean frammis diameter of widgets being produced based on Student's t-distribution. Answer: (6.80, 7.02).
6. Jones and Smith are running for Mayor of the town of Outer Boondock. Two polls taken one month apart by the local paper, both with sample sizes of 500, had the results shown below
candidate | first poll | second poll |
---|---|---|
Jones | 37.2% | 42.6% |
Smith | 45.4% | 42.8% |
Undecided | 17.4% | 14.6% |
It appears from the polls that Jones is gaining. But appearances may be deceiving.
7.
Redo part (a) of Additional Problem 6 using the R
function prop.test
rather than hand calculation.
8. Using the data for the first poll in Additional Problem 6 calculate an approximate confidence interval for the difference of proportions of voters favoring Jones and favoring Smith. (Hint: Which of Wild and Seber's three cases is this?)
9. In two polls taken a month apart, each poll sampling 600 likely voters, the preferences expressed for the candidates were
First Poll | Second Poll | |
Shrub | 45% | 50% |
Pierce | 35% | 36% |
Bottom | 12% | 8% |
Undecided | 8% | 6% |
In the second poll the following results were reported for suburban college educated women (67 were in the sample, about 1 / 9 of the sample).
Second Poll | |
Shrub | 62% |
Pierce | 24% |
Bottom | 9% |
Undecided | 5% |
In the questions below, you do not have to be precise (though you can if you want). The simple ``mental adjustments'' recommended by Wild and Seber in Section 8.5.3 for these situations are good enough.
10. For the two polls in additional question 9 the table below gives the actual counts (how many actual people correspond to each cell of the table), which you need for this problem.
First Poll | Second Poll | |
Shrub | 270 | 302 |
Pierce | 210 | 215 |
Bottom | 72 | 48 |
Undecided | 48 | 35 |
The sample size for both polls was 600.
11. This is just the exercise for Section 12.4.3 in Wild and Seber (p. 535). The only point of making it an "additional problem" is that you can use the Rweb form below to do it. This loads the data with the outlier removed (from the file gauge.txt which is the same as Wild and Seber's except for the deletion of case 35), so you don't have to dink around with the outlier removal.
12.
This is just exercise 3 for Section 12.4.4 in Wild and Seber but done
my way rather than their way. The data in question are in the first dataset
in the book
(heart.txt)
and the variables in question are SYSVOL
,
which is the predictor, and DIAVOL
, which is the
response.
13. For the coyote data described on p. 56 in Wild and Seber (in the file coyote.txt) recall that the R commands
males <- length[gender == "male"] females <- length[gender == "female"]put the lengths of the male and female coyotes in different R variables. Suppose we want to test whether there is any "statistically significant" difference in body length between the sexes.