HMWK7
STAT 200 Week 7 Homework Problem Solutions
#1 10.1.2
Table #10.1.6 contains the value of the house and the amount of rental income in a year that the house brings in ("Capital and rental," 2013). Create a scatter plot and find a regression equation between house value and rental income. Then use the regression equation to find the rental income a house worth $230,000 and for a house worth $400,000. Which rental income that you calculated do you think is closer to the true rental income? Why?
Table #10.1.6: Data of House Value versus Rental
|
Value |
Rental |
Value |
Rental |
Value |
Rental |
Value |
Rental |
|
81000 |
6656 |
77000 |
4576 |
75000 |
7280 |
67500 |
6864 |
|
95000 |
7904 |
94000 |
8736 |
90000 |
6240 |
85000 |
7072 |
|
121000 |
12064 |
115000 |
7904 |
110000 |
7072 |
104000 |
7904 |
|
135000 |
8320 |
130000 |
9776 |
126000 |
6240 |
125000 |
7904 |
|
145000 |
8320 |
140000 |
9568 |
140000 |
9152 |
135000 |
7488 |
|
165000 |
13312 |
165000 |
8528 |
155000 |
7488 |
148000 |
8320 |
|
178000 |
11856 |
174000 |
10400 |
170000 |
9568 |
170000 |
12688 |
|
200000 |
12272 |
200000 |
10608 |
194000 |
11232 |
190000 |
8320 |
|
214000 |
8528 |
208000 |
10400 |
200000 |
10400 |
200000 |
8320 |
|
240000 |
10192 |
240000 |
12064 |
240000 |
11648 |
225000 |
12480 |
|
289000 |
11648 |
270000 |
12896 |
262000 |
10192 |
244500 |
11232 |
|
325000 |
12480 |
310000 |
12480 |
303000 |
12272 |
300000 |
12480 |
#2 10.2.2
Table #10.1.6 contains the value of the house and the amount of rental income in a year that the house brings in ("Capital and rental," 2013). Find the correlation coefficient and coefficient of determination and then interpret both.
Table #10.1.6: Data of House Value versus Rental
|
Value |
Rental |
Value |
Rental |
Value |
Rental |
Value |
Rental |
|
81000 |
6656 |
77000 |
4576 |
75000 |
7280 |
67500 |
6864 |
|
95000 |
7904 |
94000 |
8736 |
90000 |
6240 |
85000 |
7072 |
|
121000 |
12064 |
115000 |
7904 |
110000 |
7072 |
104000 |
7904 |
|
135000 |
8320 |
130000 |
9776 |
126000 |
6240 |
125000 |
7904 |
|
145000 |
8320 |
140000 |
9568 |
140000 |
9152 |
135000 |
7488 |
|
165000 |
13312 |
165000 |
8528 |
155000 |
7488 |
148000 |
8320 |
|
178000 |
11856 |
174000 |
10400 |
170000 |
9568 |
170000 |
12688 |
|
200000 |
12272 |
200000 |
10608 |
194000 |
11232 |
190000 |
8320 |
|
214000 |
8528 |
208000 |
10400 |
200000 |
10400 |
200000 |
8320 |
|
240000 |
10192 |
240000 |
12064 |
240000 |
11648 |
225000 |
12480 |
|
289000 |
11648 |
270000 |
12896 |
262000 |
10192 |
244500 |
11232 |
|
325000 |
12480 |
310000 |
12480 |
303000 |
12272 |
300000 |
12480 |
#3 10.3.2
Table #10.1.6 contains the value of the house and the amount of rental income in a year that the house brings in ("Capital and rental," 2013).
Test at the 5% level for a positive correlation between house value and rental amount.
Table #10.1.6: Data of House Value versus Rental
|
Value |
Rental |
Value |
Rental |
Value |
Rental |
Value |
Rental |
|
81000 |
6656 |
77000 |
4576 |
75000 |
7280 |
67500 |
6864 |
|
95000 |
7904 |
94000 |
8736 |
90000 |
6240 |
85000 |
7072 |
|
121000 |
12064 |
115000 |
7904 |
110000 |
7072 |
104000 |
7904 |
|
135000 |
8320 |
130000 |
9776 |
126000 |
6240 |
125000 |
7904 |
|
145000 |
8320 |
140000 |
9568 |
140000 |
9152 |
135000 |
7488 |
|
165000 |
13312 |
165000 |
8528 |
155000 |
7488 |
148000 |
8320 |
|
178000 |
11856 |
174000 |
10400 |
170000 |
9568 |
170000 |
12688 |
|
200000 |
12272 |
200000 |
10608 |
194000 |
11232 |
190000 |
8320 |
|
214000 |
8528 |
208000 |
10400 |
200000 |
10400 |
200000 |
8320 |
|
240000 |
10192 |
240000 |
12064 |
240000 |
11648 |
225000 |
12480 |
|
289000 |
11648 |
270000 |
12896 |
262000 |
10192 |
244500 |
11232 |
|
325000 |
12480 |
310000 |
12480 |
303000 |
12272 |
300000 |
12480 |
a.) State the random variables
1. State the null and alternative hypotheses and the level of significance
1. State and check the assumptions for a hypothesis test
1. Find the test statistic and p-value
1. State the statistical conclusion (in terms of accepting or rejecting the null hypothesis)
1. Provide a real-world interpretation of the results of the hypothesis test
#4 11.1.4
A person’s educational attainment and age group was collected by the U.S. Census Bureau in 1984 to see if age group and educational attainment are related. The counts in thousands are in table #11.1.8 ("Education by age," 2013). Do the data show that educational attainment and age are independent? Test at the 5% level.
Table #11.1.8: Educational Attainment and Age Group
|
Education |
Age Group |
Row Total |
||||
|
|
25-34 |
35-44 |
45-54 |
55-64 |
>64 |
|
|
Did not complete HS |
5416 |
5030 |
5777 |
7606 |
13746 |
37575 |
|
Competed HS |
16431 |
1855 |
9435 |
8795 |
7558 |
44074 |
|
College 1-3 years |
8555 |
5576 |
3124 |
2524 |
2503 |
22282 |
|
College 4 or more years |
9771 |
7596 |
3904 |
3109 |
2483 |
26863 |
|
Column Total |
40173 |
20057 |
22240 |
22034 |
26290 |
130794 |
a.) State the random variables
b.) State the null and alternative hypotheses and the level of significance
c.) State and check the assumptions for a hypothesis test
d.) Find the test statistic and p-value
e.) State the statistical conclusion (in terms of accepting or rejecting the null hypothesis)
f.) Provide a real-world interpretation of the results of the hypothesis test
#5 11.2.6
A project conducted by the Australian Federal Office of Road Safety asked people many questions about their cars. One question was the reason that a person chooses a given car, and that data is in table #11.2.8 ("Car preferences," 2013).
Table #11.2.8: Reason for Choosing a Car
|
Safety |
Reliability |
Cost |
Performance |
Comfort |
Looks |
|
84 |
62 |
46 |
34 |
47 |
27 |
Do the data show that the frequencies observed substantiate the claim that the reasons for choosing a car are equally likely? Test at the 5% level.
a.) State the random variables
b.) State the null and alternative hypotheses and the level of significance
c.) State and check the assumptions for a hypothesis test
d.) Find the test statistic and p-value
e.) State the statistical conclusion (in terms of accepting or rejecting the null hypothesis)
f.) Provide a real-world interpretation of the results of the hypothesis test