With reference to your estimated equation, calculate (using a calculator) a 90% confidence interval for the coefficient of X (i.e.β2 ) in the model. In performing this calculation, use the relevant estimated standard error of the estimator of the coefficient of X given in your summary Excel regression output. Give an interpretation of the confidence interval you calculate. Regression Statistics Multiple R 0.319551492 R Square 0.102113156 Adjusted R Square 0.079665985 Standard Error 30180.01209 Observations 42 ANOVA df SS MS F Significance F Regression 1 4143419459 4.14E+09 4.549043 0.039125 Residual 40 36433325185 9.11E+08 Total 41 40576744644 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 102590.3493 11164.94377 9.188613 2.09E-11 80025.16 125155.5 80025.16 125155.5 X Variable 1 3592.882724 1684.546487 2.132849 0.039125 188.2873 6997.478 188.2873 6997.478 Marketer Hours of Training Sales Revenue 1 3 147643 2 6 120253 3 4 88777 4 3 157183 5 8 166802 6 2 127011 7 2 140582 8 10 104909 9 5 109778 10 5 110944 11 8 117820 12 3 136171 13 9 132366 14 5 64333 15 3 79541 16 7 179653 17 12 127204 18 6 114559 19 4 139737 20 4 71936 21 8 143105 22 6 158422 23 9 124784 24 10 151596 25 4 95001 26 3 103926 27 12 204264 28 9 213471 29 5 82178 30 7 123313 31 6 116686 32 3 126791 33 8 96543 34 6 136833 35 7 115203 36 3 108528 37 11 124951 38 10 98837 39 5 100181 40 5 102495 41 3 118540 42 4 134944

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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With reference to your estimated equation, calculate (using a calculator) a 90% confidence interval for the coefficient of X (i.e.β2 ) in the model. In performing this calculation, use the relevant estimated standard error of the estimator of the coefficient of X given in your summary Excel regression output. Give an interpretation of the confidence interval you calculate.



Regression Statistics
Multiple R	0.319551492
R Square	0.102113156
Adjusted R Square	0.079665985
Standard Error	30180.01209
Observations	42

ANOVA
	df	SS	MS	F	Significance F
Regression	1	4143419459	4.14E+09	4.549043	0.039125
Residual	40	36433325185	9.11E+08
Total	41	40576744644

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	102590.3493	11164.94377	9.188613	2.09E-11	80025.16	125155.5	80025.16	125155.5
X Variable 1	3592.882724	1684.546487	2.132849	0.039125	188.2873	6997.478	188.2873	6997.478

Marketer	Hours of Training	Sales Revenue
1	3	147643
2	6	120253
3	4	88777
4	3	157183
5	8	166802
6	2	127011
7	2	140582
8	10	104909
9	5	109778
10	5	110944
11	8	117820
12	3	136171
13	9	132366
14	5	64333
15	3	79541
16	7	179653
17	12	127204
18	6	114559
19	4	139737
20	4	71936
21	8	143105
22	6	158422
23	9	124784
24	10	151596
25	4	95001
26	3	103926
27	12	204264
28	9	213471
29	5	82178
30	7	123313
31	6	116686
32	3	126791
33	8	96543
34	6	136833
35	7	115203
36	3	108528
37	11	124951
38	10	98837
39	5	100181
40	5	102495
41	3	118540
42	4	134944

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