Question

Find the equation of the 95% confidence line from given data in EXCEL (above regression line): X VALUES:

The equation is supposed to not be linear and be to a power of a constant in the equation.If you can figure out that part its ok. Just need the equation/picture of the data, and equation for graph. Will rate highest for good answer

0.232
0.122
0.241
0.148
0.022
0.165
0.182
0.164
0.077
0.104
0.191
0.032
0.13
0.08
0.132
0.021
0.118

Y VALUES

9.138547
12.94254
9.956063
12.63151
40.78222
12.17864
13.73187
11.01636
17.66244
11.91036
8.969413
40.12883
12.74238
16.63304
14.04124
38.2091
13.13019

Answer 1

x	y	lnx	lny
0.232	9.138547	-1.46102	2.212501
0.122	12.94254	-2.10373	2.56052
0.241	9.956063	-1.42296	2.298182
0.148	12.63151	-1.91054	2.536194
0.022	40.78222	-3.81671	3.708246
0.165	12.17864	-1.80181	2.499684
0.182	13.73187	-1.70375	2.619719
0.164	11.01636	-1.80789	2.399381
0.077	17.66244	-2.56395	2.87144
0.104	11.91036	-2.26336	2.477409
0.191	8.969413	-1.65548	2.19382
0.032	40.12883	-3.44202	3.692095
0.13	12.74238	-2.04022	2.544933
0.08	16.63304	-2.52573	2.811391
0.132	14.04124	-2.02495	2.641999
0.021	38.2091	-3.86323	3.643074
0.118	13.13019	-2.13707	2.574914

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.967966765
R Square	0.936959658
Adjusted R Square	0.932756968
Standard Error	0.127275068
Observations	17
ANOVA
	df	SS	MS	F	Significance F
Regression	1	3.611438816	3.611438816	222.9428697	2.07127E-10
Residual	15	0.242984144	0.016198943
Total	16	3.85442296
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	1.305686642	0.099795019	13.08368542	1.31469E-09	1.092978594	1.51839469	1.092978594	1.51839469
lnx	-0.624962592	0.041855952	-14.93127154	2.07127E-10	-0.714176443	-0.535748742	-0.71417644	-0.535748742

lny = 1.3057 - 0.6251 * lnx

y = e^(1.3057 - 0.6251 * lnx)

y = e^1.3057 * x^-0.6251

y = 3.6902 x^-0.6251

Calculator View:

y = 3.6902 x^-0.6251

function value mean of x 0.1035894564 mean of y 15.22100916 correlation coefficient r -0.967966765 3.69022208 B -0.624962592 9000 0.035 0.065 9600 0.125 0.155 0.185 0.215 0.245 0.275 AXB

X	y	lnx	lny
9.138547	0.232	2.212501401	-1.461017907
12.94254	0.122	2.56051956	-2.103734234
9.956063	0.241	2.298181712	-1.422958345
12.63151	0.148	2.536194486	-1.910543005
40.78222	0.022	3.708246202	-3.816712826
12.17864	0.165	2.499683598	-1.801809805
13.73187	0.182	2.619719409	-1.703748592
11.01636	0.164	2.399381441	-1.807888851
17.66244	0.077	2.871440351	-2.563949857
11.91036	0.104	2.47740861	-2.26336438
8.969413	0.191	2.193820234	-1.655481851
40.12883	0.032	3.692095029	-3.442019376
12.74238	0.13	2.544933446	-2.040220829
16.63304	0.08	2.811391079	-2.525728644
14.04124	0.132	2.641998714	-2.024953356
38.2091	0.021	3.643073707	-3.863232841
13.13019	0.118	2.574914159	-2.137070655

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.967966765
R Square	0.936959658
Adjusted R Square	0.932756968
Standard Error	0.197128656
Observations	17
ANOVA
	df	SS	MS	F	Significance F
Regression	1	8.663494577	8.663494577	222.94287	2.07127E-10
Residual	15	0.582895604	0.038859707
Total	16	9.246390181
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	1.814585685	0.277528889	6.538366831	9.3845E-06	1.223046862	2.406124507	1.223046862	2.406124507
lnx	-1.499225185	0.100408407	-14.93127154	2.0713E-10	-1.713240637	-1.285209733	-1.713240637	-1.285209733

lny = 1.8146 - 1.4992 lnx

y = e^(1.8146 - 1.4992 lnx)

y = e^1.8146 x^(- 1.4992)

y = 6.1385 x^(- 1.4992)