Analyse the following regression Regression Statistics Multiple R0.79716916 R Square0.63547867 Adjusted R Square0.63254686 Standard Error198.375358 Observations377 ANOVA dfSSMSFSignificance F Regression3255895308529843.34216.7532452.2501E-81 Residual37314678587.939352.7826 Total37640268117.9 CoefficientsStandard Errort StatP-valueLower 95%Upper 95%Lower 95.0%Upper 95.0%Intercept-97.98117479.2847437-1.23581370.21730553-253.8822857.9199296-253.8822857.9199296EquivArea3.55232580.1514906723.44913992.2549E-753.2544433.850208613.2544433.85020861Years2.046294860.317701156.440942533.6636E-101.421585012.671004711.421585012.67100471Condition15.037906710.21444791.472219240.14180492-5.047214735.1230282-5.047214735.1230282

Solution: The regression output is given.

Answered: Analyse the following regression…

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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The following is the ANOVA obtained from a regression analysis: ANOVA Source of Variation REGRESSION ERROR TOTAL Degree of Freedom 1 104 Sum of Squares 14.6 440.9 Mean Squares F-calculated === Your are interested to evaluate the following hypothesis: Ho: B 1 B 2 ---against--- HA: B1 B2 1. How many observation were used in this experiment? (place your answer in the box below) 2. Evaluate the F calculated for the regression, and indicate if you are going to reject(R) or fail to reject(R) the null hypothesis using an a= 0.01 (R/FR; place your answer right beside the calculated number of treatments)
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Hocument/d/1ho6gSyZTqOTIUok211PsszM1fL7Jz4K2HPR4WWL4x1g/edit sert Format Tools Extensions Help 2 100% D 1 ▼ Normal text 2 1 W L #3 E 2 $ Arial A quality control expert at Glotech computers wants to test their new monitors. The production manager claims they have a mean life of 83 months with a variance of 100. If the claim is true, what is the probability that the mean monitor life would be greater than 81.3 months in a sample of 89 monitors? Round your answer to four decimal places. R % 5 T ^ 10.5 + 6 Y & B 7 * CO I U 5 8 I A ( 9 ) 0 6 P
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The results of the regression analysis to estimate setup costs using the number of setup hours are as follows: SUMMARY OUTPUT Regression Statistics Multiple R 0.965127 R Square 0.931469 Adjusted R Square 0.921679 Standard Error 285.6803 Observations 9 ANOVA df SS MS F Regression 1 7765004 7765004 95.14395 Residual 7 571292.5 81613.21 Total 8 8336296 Coefficients Std Error t Stat P-value Intercept 2498.644 680.6304 3.671073 0.007953 X Variable 1 2.506915 0.257009 9.754176 2.52E–05 What percentage change (rounded to one decimal place) in setup costs can be explained by changes in setup hours? a.95.1% b.96.5% c.25.7% d.none of these e.93.1%

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Regression Statistics
Multiple R	0.79716916
R Square	0.63547867
Adjusted R Square	0.63254686
Standard Error	198.375358
Observations	377

ANOVA
	df	SS	MS	F	Significance F
Regression	3	25589530	8529843.34	216.753245	2.2501E-81
Residual	373	14678587.9	39352.7826
Total	376	40268117.9

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	-97.981174	79.2847437	-1.2358137	0.21730553	-253.88228	57.9199296	-253.88228	57.9199296
EquivArea	3.5523258	0.15149067	23.4491399	2.2549E-75	3.254443	3.85020861	3.254443	3.85020861
Years	2.04629486	0.31770115	6.44094253	3.6636E-10	1.42158501	2.67100471	1.42158501	2.67100471
Condition	15.0379067	10.2144479	1.47221924	0.14180492	-5.0472147	35.1230282	-5.0472147	35.1230282