SUMMARY OUTPUT Regression Statistics 0.8753 Multiple R R Square Adjusted R Square Standard Error 0.7661 0.7271 0.9619 8 Observations ANOVA TABLE FOR REFRESSION SS 18.187 Significance F 0.0044 df MS 18.187 0.925 Regression 1 19.655 Residual 6 5.552 Total 7 23.739 Coefficients Standard Error 65.3843 t Stat 69.6653 P-value 0.9385 Intercept Years of employment 0.2698 0.0609 4.4334 0.0044 a) What is the estimated regression line? b) What is the estimated income for a dentist with 20 years of experience? c) How would you interpret the slope coefficient B1? d) How would you interpret the coefficient of determination? e) Test Ho : B1 = 0 vs. H1 : B1 ± 0 using the t-test.

SUMMARY OUTPUT Regression Statistics 0.8753 Multiple R R Square Adjusted R Square Standard Error 0.7661 0.7271 0.9619 8 Observations ANOVA TABLE FOR REFRESSION SS 18.187 Significance F 0.0044 df MS 18.187 0.925 Regression 1 19.655 Residual 6 5.552 Total 7 23.739 Coefficients Standard Error 65.3843 t Stat 69.6653 P-value 0.9385 Intercept Years of employment 0.2698 0.0609 4.4334 0.0044 a) What is the estimated regression line? b) What is the estimated income for a dentist with 20 years of experience? c) How would you interpret the slope coefficient B1? d) How would you interpret the coefficient of determination? e) Test Ho : B1 = 0 vs. H1 : B1 ± 0 using the t-test.

Name: SUMMARY OUTPUTRegression Statistics0.8753Multiple RR SquareAdjusted R
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Description: SUMMARY OUTPUTRegression Statistics0.8753Multiple RR SquareAdjusted R SquareStandard Error0.76610.72710.9619Observations8ANOVA TABLE FOR REFRESSIONdfMS18.187Significance F0.0044FRegressionResidual118.18719.6555.5520.925Total723.739Coefficients Stand

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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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
Perform a repeated measures ANOVA on the data in the table and construct an ANOVA summary table. Does the ANOVA suggest that systematic differences exist across trials? Calculate the intraclass correlation coefficient for models 2,1 and 3,1. Calculate the standard error of measurement. Assume a person has an observed score of 40. Construct a 95% CI about this subject’s observed score to estimate the interval in which we think that person’s true score lies. Calculate the minimal difference using a 95% CI. Day 1 Day 2 Day 3 35.6 36.8 37.5 40.3 39.5 41.5 37.5 36.5 35.5 45.8 42.5 48.6 41.3 38.6 37.2 34.8 31.9 32.5 41.3 46.5 45.6 32.1 34.7 36.5 29.8 31.2 33.8 48.6 45.6 43.5 42.6 41.8 43.4 37.8 35.1 39.5
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%
Ques = Homework: Ch 8 - Sampling Distributions Critical values of the correlation coefficient ars. Com - X Int. Critical Values for Correlation Coefficient Data table 3 0,997 4 0.950 5 0.878 6 0.811 7 0.754 Year Rate of Return of Company Rate of Return of Company ble. Chod 6 2 2007 0.203 0.402 8 0.707 9 0.666 10 0.632 2008 0.310 0.510 2009 0.267 0.410 11 0.602 12 0.576 13 0.553 14 0.532 15 0.514 16 0.497 2010 0.195 0.426 2011 -0.101 -0.060 2012 - 0.130 -0.151 2013 -0.234 -0.357 2014 0.264 0.328 17 0.482 18 0.468 19 0.456 20 0.444 21 0.433 2015 0.090 0.207 2016 0.030 -0.014 2017 0.128 0.113 2018 - 0.035 0.027 22 0.423 23 0.413 24 0.404 25 0.396 Print Done 26 0.388 27 0.381 Tne correlauon coeicient is- (Round to three decimal places as needed.) Print Done
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Based on the ANOVA table given, is there enough evidence at the 0.05 level of significance to conclude that the linear relationship between the independent variables and the dependent variable is statistically significant? Source df SS Regression 2 984.715358 Residual 7 298.884642 Total 9 1283.600000 ANOVA MS F 492.357679 11.531217 42.697806 Significance F 0.006092 Copy Data Ĵ Based on the ANOVA table given, is there enough evidence at the 0.05 level of significance to conclude that the linear relationship between the independent variables and the dependent variable is statistically significant? Source df SS Regression 2 984.715358 Residual 7 298.884642 Total 9 1283.600000 ANOVA MS F Significance F 492.357679 11.531217 0.006092 42.697806 Copy Data
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