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Homework answers / question archive / STAT 200 Week 7 Homework Problems 1) 10
STAT 200 Week 7 Homework Problems
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) 10.1.4
The World Bank collected data on the percentage of GDP that a country spends on health expenditures ("Health expenditure," 2013) and also the percentage of women receiving prenatal care ("Pregnant woman receiving," 2013). The data for the countries where this information are available for the year 2011 is in table #10.1.8. Create a scatter plot of the data and find a regression equation between percentage spent on health expenditure and the percentage of women receiving prenatal care. Then use the regression equation to find the percent of women receiving prenatal care for a country that spends 5.0% of GDP on health expenditure and for a country that spends 12.0% of GDP. Which prenatal care percentage that you calculated do you think is closer to the true percentage? Why?
Table #10.1.8: Data of Health Expenditure versus Prenatal Care
Health Expenditure (% of GDP) |
Prenatal Care (%) |
9.6 |
47.9 |
3.7 |
54.6 |
5.2 |
93.7 |
5.2 |
84.7 |
10.0 |
100.0 |
4.7 |
42.5 |
4.8 |
96.4 |
6.0 |
77.1 |
5.4 |
58.3 |
4.8 |
95.4 |
4.1 |
78.0 |
6.0 |
93.3 |
9.5 |
93.3 |
6.8 |
93.7 |
6.1 |
89.8 |
3) 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 |
4) 10.2.4
The World Bank collected data on the percentage of GDP that a country spends on health expenditures ("Health expenditure," 2013) and also the percentage of women receiving prenatal care ("Pregnant woman receiving," 2013). The data for the countries where this information is available for the year 2011 are in table #10.1.8. Find the correlation coefficient and coefficient of determination and then interpret both.
Table #10.1.8: Data of Health Expenditure versus Prenatal Care
Health Expenditure (% of GDP) |
Prenatal Care (%) |
9.6 |
47.9 |
3.7 |
54.6 |
5.2 |
93.7 |
5.2 |
84.7 |
10.0 |
100.0 |
4.7 |
42.5 |
4.8 |
96.4 |
6.0 |
77.1 |
5.4 |
58.3 |
4.8 |
95.4 |
4.1 |
78.0 |
6.0 |
93.3 |
9.5 |
93.3 |
6.8 |
93.7 |
6.1 |
89.8 |
5) 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 |
6) 11.2.4
In Africa in 2011, the number of deaths of a female from cardiovascular disease for different age groups are in table #11.2.6 ("Global health observatory," 2013). In addition, the proportion of deaths of females from all causes for the same age groups are also in table #11.2.6. Do the data show that the death from cardiovascular disease are in the same proportion as all deaths for the different age groups? Test at the 5% level.
Table #11.2.6: Deaths of Females for Different Age Groups
Age |
5-14 |
15-29 |
30-49 |
50-69 |
Total |
Cardiovascular Frequency |
8 |
16 |
56 |
433 |
513 |
All Cause Proportion |
0.10 |
0.12 |
0.26 |
0.52 |
|
7) 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.
Answer 1:
Random Variables
Y= Rental income
X=House value
Income for a rental home that is worth 230,000
X= $230,000
Rental income = 0.0244 *$230,000+5363.9
Rental income = $10,976
Income for a rental home that is worth 400,000
X= $400,000
Rental income= 0.0244 *$400,000+5363.9
Rental income = $15,124
Which rental income that you calculated do you think is closer to the true rental income? Why?
$10,976 is closer to the true rental income from the calculated rental income.
Answer 2:
The random variables for the dataset are
X is Health expenditure = % GDP
Y is prenatal care = %
• The regression equation between percentages spent on health expenditure and the percentage of women receiving prenatal care is
Y= 1.6606x+69.739
• X= 5
Percentage prenatal care = 1.6606*5+69.739
Percentage prenatal care = 78.0%
• X= 12
Percentage prenatal care = 1.6606*12+69.739
Percentage prenatal care = 89.7%
Answer 3:
Correlation coefficient r = SSxy/ Sqrt (SSx SSy)
Where
SSx =∑( (X-x?)^2
SSy= ∑ (y-?)^2
Ssxy= ∑ (X-x?)(Y-?)
Thus, from the data
r= 5527756000/ sqrt(226935750000* 230247403)
r= 0.765
Coefficient of Determination
R squared= r2
R2= 0.7652
R2= 0.585
Answer 4:
Solutions
Random variables
X is Health expenditure (% GDP)
Y is prenatal care (%)
Correlation coefficient r = SSxy/ Sqrt (SSx SSy)
Where
SSx =∑ (X-x?)2
SSy= ∑ (y-?) 2
SSxy= ∑ (X-x?) (Y-?)
Thus, from the data
r= 94.205/ sqrt(5318.417* 56.729)
r= 0.17
Coefficient of Determination
R squared= r2
R2= 0.172
R2= 0.03
Answer 5:
Ho: ρ=0
H1: ρ>0
α=0.05
The t-statistic assuming unequal variance between house value and rental income is
T-statistic = 16.4195
And the P-value at α=0.05 (2 tailed) is
P-value = 0.0000
Therefore, we reject Ho and make a conclusion that the correlation between house value and rental amount is positive at 5 % level of confidence.
b.) Find the standard error of the estimate.
Se= Sqrt( SSy – (b*SSxy)/n-2)
= SQRT((230247403-(0.0244* 5527756000))/46)
Standard error of the estimate = 1439.88
c.) Compute a 95% prediction interval for the rental income on a house worth $230,000. X= $230,000
Rental income= 0.0244 *$230,000+5363.9
Rental income = $10,976
Prediction Interval
y − E < y < ˆy + E
Thus,
10976 - 1439.88 < y < 10976 +1439.88
= $9,536.12< y < $12,415.88
The predicted interval for rental income for a house worth $230,000 is
[$9,536.12, $12,415.88]
Answer 6:
Solution
Ho: Deaths from cardiovascular disease are not in the same proportion as all deaths for different age groups.
H1: Deaths from cardiovascular disease are in the same proportion as all deaths for different age groups.
To test the hypothesis, we use a Chi-square test
Age |
Cardiovascular frequency (O) |
All Cause Proportion |
Expected value |
O-E |
(O-E)2 |
(O-E)2/E |
5- 14 |
8 |
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