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Homework answers / question archive / STAT 200 Week 7 Homework Problems 1) 10

STAT 200 Week 7 Homework Problems 1) 10

Statistics

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.

 

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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