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Rental
6656
7904
12064
8320
8320
13312
11856
12272
8528
10192
11648
12480
4576
8736
7904
9776
9568
8528
10400
10608
10400
12064
12896
12480
7280
6240
7072
6240
9152
7488
9568
11232
10400
11648
10192
12272
6864
7072
7904
7904
7488
8320
12688
8320
Rental
14000
12000
10000
Rental
Value
81000
95000
121000
135000
145000
165000
178000
200000
214000
240000
289000
325000
77000
94000
115000
130000
140000
165000
174000
200000
208000
240000
270000
310000
75000
90000
110000
126000
140000
155000
170000
194000
200000
240000
262000
303000
67500
85000
104000
125000
135000
148000
170000
190000
8000
6000
y = 0.0244x + 5363.9
R² = 0.5848
4000
2000
0
0
50000
100000
150000
200000
250000
300000
Value
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.76471575
R Square
0.58479018
Adjusted R Square
0.57576388
Standard Error
1441.62486
Observations
48
ANOVA
df
Regression
Residual
Total
Intercept
Value
SS
MS
F
1 1.35E+08 1.35E+08 64.78736
46 95600982 2078282
47 2.3E+08
CoefficientsStandard Error t Stat
P-value
5363.86482 567.2408 9.456062 2.34E-12
0.02435824 0.003026 8.04906
2.5E-10
Regression equation is
Rental=5363.865+0.024358*Value
use the regression equation to find the rental income a house worth $230,000 and for a ho
House worth $230,000
Rental income =
$ 10,966.20
House worth $400,000
Rental income =
$ 15,107.06
200000
225000
244500
300000
8320
12480
11232
12480
The rental income for the house worth $230,000 is closer to the true rent income because
y = 0.0244x + 5363.9
R² = 0.5848
300000
350000
Significance F
2.5E-10
Lower 95% Upper 95%Lower 95.0%
Upper 95.0%
4222.068 6505.661 4222.068 6505.661
0.018267 0.03045 0.018267 0.03045
se worth $230,000 and for a house worth $400,000
o the true rent income because it is within the regression line
9.6
3.7
5.2
5.2
10
4.7
4.8
6
5.4
4.8
4.1
6
9.5
6.8
6.1
Prenatal
Care (%)
47.9
54.6
93.7
84.7
100
42.5
96.4
77.1
58.3
95.4
78
93.3
93.3
93.7
89.8
Prenatal Care (%)
120
100
Prenatal Care (%)
Health Expenditure (% of GDP)
80
60
40
y = 1.6606x + 69.739
R² = 0.0294
20
0
0
2
4
6
Health Expenditure (% of GDP)
SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
0.17150523
0.02941404
-0.04524641
19.9267527
15
ANOVA
df
Regression
Residual
Total
Intercept
Health Expenditure (% of GDP)
SS
MS
F
Significance F
1 156.4362 156.4362 0.393971 0.541089
13 5161.981 397.0755
14 5318.417
CoefficientsStandard Error t Stat
P-value Lower 95% Upper 95%
69.7393976 17.00601 4.100869 0.001251 33.00015 106.4786
1.66059887 2.645652 0.627671 0.541089 -4.05498 7.376182
Regression equation is
Prenatal Care = (1.6606*Health Expenditure) + 69.739
use the regression equation to find the percent of women receiving prenatal care for a country that spends 5.0% of GDP on h
for 5% of GDP
% of women receiving Healthcare
78.04
for 12% of GDP
% of women receiving Healthcare
89.67
Hence, the predicted value for those that spend 5% of GDP is closer to the true percentage, because it is closer to the regress
Care (%)
y = 1.6606x + 69.739
R² = 0.0294
8
10
12
enditure (% of GDP)
Lower 95.0%
Upper 95.0%
33.00015 106.4786
-4.05498 7.376182
at spends 5.0% of GDP on health expenditure and for a country that spends 12.0% of GDP
Value (X) Rental (Y)
81000
6656
95000
7904
121000
12064
135000
8320
145000
8320
165000
13312
178000
11856
200000
12272
214000
8528
240000
10192
289000
11648
325000
12480
77000
4576
94000
8736
115000
7904
130000
9776
140000
9568
165000
8528
174000
10400
200000
10608
208000
10400
240000
12064
270000
12896
310000
12480
75000
7280
90000
6240
110000
7072
126000
6240
140000
9152
155000
7488
170000
9568
194000
11232
200000
10400
240000
11648
262000
10192
303000
12272
67500
6864
85000
7072
104000
7904
125000
7904
135000
7488
148000
8320
170000
12688
190000
8320
200000
8320
225000
12480
(X-Xbar)^2
8718890625
6300390625
2848890625
1550390625
862890625
87890625
13140625
656640625
1570140625
4306640625
13138890625
22687890625
9481890625
6460140625
3525390625
1969140625
1181640625
87890625
140625
656640625
1130640625
4306640625
9144140625
18394140625
9875390625
7119140625
4144140625
2340140625
1181640625
375390625
19140625
385140625
656640625
4306640625
7678140625
16544390625
11422265625
7987890625
4952640625
2437890625
1550390625
695640625
19140625
244140625
656640625
2562890625
(Y-Ybar)^2
(x-xbar)(y-ybar)
8733995.111
275954250
2914987.111
135519583.3
6015573.778
-130911083.3
1667541.778
50846250
1667541.778
37932916.67
13694933.78
-34693750
5038528.444
8136916.667
7079147.111
68179583.33
1173611.111
-42927083.33
337173.7778
38106250
4148011.111
233452916.7
8229248.444
432092916.7
25354581.78
490315583.3
766208.4444
70354916.67
2914987.111
101372916.7
27115.11111
-7307083.333
1877.777778
1489583.333
1173611.111
10156250
621995.1111
-295750
993344.4444
25539583.33
621995.1111
26518916.67
6015573.778
160956250
10789035.11
314096250
8229248.444
389062916.7
5435115.111
231676250
11365888.44
284456250
6448213.778
163469583.3
11365888.44
163088250
210987.1111
15789583.33
4508544.444
41139583.33
1877.777778
189583.3333
2626560.444
31805583.33
621995.1111
20209583.33
4148011.111
133656250
337173.7778
50880916.67
7079147.111
342228250
7547840.444
293621250
6448213.778
226952916.7
2914987.111
120153583.3
2914987.111
84299583.33
4508544.444
83606250
1667541.778
34058916.67
9465877.778
-13460416.67
1667541.778
-20177083.33
1667541.778
-33090416.67
8229248.444
145226250
244500
300000
11232
12480
4917515625 2626560.444
15781640625 8229248.444
Total
Total
SSX
SSY
8370000 461344 2.26936E+11 230247402.7
Mean
Mean
174375 9611.333
113649250
360376250
SXY
5527756000
Health Expenditure (% of GDP)
9.6
3.7
5.2
5.2
10
4.7
4.8
6
5.4
4.8
4.1
6
9.5
6.8
6.1
Total
mean
Correlation Coefficient =
Prenatal Care (%)
47.9
54.6
93.7
84.7
100
42.5
96.4
77.1
58.3
95.4
78
93.3
93.3
93.7
89.8
X
9.6
3.7
5.2
5.2
10
4.7
4.8
6
5.4
4.8
4.1
6
9.5
6.8
6.1
91.90
6.13
Y
47.9
54.6
93.7
84.7
100
42.5
96.4
77.1
58.3
95.4
78
93.3
93.3
93.7
89.8
1198.70
79.91
Health Expenditure (% of GDP)
Health Expenditure (% of GDP)
Prenatal Care (%)
(x-xbar)^2
12.0640
5.8887
0.8587
0.8587
15.0027
2.0354
1.7600
0.0160
0.5280
1.7600
4.1074
0.0160
11.3794
0.4534
0.0007
56.73
SSX
(y-ybar)^2
1024.8535
640.7648
190.0722
22.9122
403.4742
1399.7575
271.8102
7.9148
467.1362
239.8368
3.6608
179.2028
179.2028
190.0722
97.7462
5318.42
SSY
SXY/(sqrt(SSX*SSY)
0.1715
The correlation coefficient is 0.1715
Based on the computation, the correlation coefficient is positive and very small (close to 0) which suggests that there
is a weak positive relationship between Health Expenditure and Prenatal Care Health Expenditure
Coefficient of Determinantion R^2
0.0294
Based on the calculation above, it appears that there is a 2.94% variation in percentage of women receiving prenatal care can
explained by variation in percentage of GDP that a country spends on health expenditures
Health Expenditure (%Prenatal
of GDP) Care (%)
1
0.171505231
1
(x-xbar)(y-ybar)
-111.1930
61.4270
-12.7756
-4.4356
77.8024
53.3764
-21.8723
0.3564
15.7057
-20.5456
3.8777
-1.6956
45.1577
9.2830
-0.2636
94.20
SXY
o 0) which suggests that there
penditure
of women receiving prenatal care can be
res
Value
Rental
81000
6656
95000
7904
121000
12064
135000
8320
145000
8320
165000
13312
178000
11856
200000
12272
214000
8528
240000
10192
289000
11648
325000
12480
77000
4576
94000
8736
115000
7904
130000
9776
140000
9568
165000
8528
174000
10400
200000
10608
208000
10400
240000
12064
270000
12896
310000
12480
75000
7280
90000
6240
110000
7072
126000
6240
140000
9152
155000
7488
170000
9568
194000
11232
200000
10400
240000
11648
262000
10192
303000
12272
67500
6864
85000
7072
104000
7904
125000
7904
135000
7488
148000
8320
170000
12688
190000
8320
200000
8320
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.764716
R Square
0.58479
Adjusted R Square
0.575764
Standard Error
1441.625
Observations
48
ANOVA
df
Regression
Residual
Total
SS
MS
F
1 1.35E+08 1.35E+08 64.78736
46 95600982 2078282
47 2.3E+08
Coefficients
Standard Error t Stat
P-value
Intercept
5363.865 567.2408 9.456062 2.34E-12
Value
0.024358 0.003026 8.04906
2.5E-10
Test at the 5% level for a positive correlation between house value and rental amount.
Null Hypothesis Ho : p=0
Alternative Hypothesis Ha : p>0
Correlation coefficient value equals 0.765 and the coefficient of determination is 0.5848. A
T statistic value is 8.049 The degrees of freedom = 48-2 = 46. P-value is 0.0000000002504
less than the level of significance, therefore we reject the null hypothesis.
There is evidence to support the claim that there is a positive correlation between house a
225000
244500
300000
12480
11232
12480
Significance F
2.5E-10
Lower 95% Upper 95%Lower 95.0%
Upper 95.0%
4222.068 6505.661 4222.068 6505.661
0.018267 0.03045 0.018267 0.03045
ouse value and rental am...