Using the following regression table, we can see that the assumed linear regression is significant at 0.05 level of significanceRegression StatisticsMultiple RR SquareAdjusted R SquareStandard ( 2.54008Observati79ANOVARegressioResidualTotalXdfSelect one:O TrueO FalseSSMSFgnificance F1 8241.992 8241.992 1277.430962 1.06E-49Coefficientandard Errt StatP-value Lower 95%Upper 95%ower 95.0%pper 95.0%Intercept 12.05928 0.577032 20.8988 0.008080197 10.91027 13.2083 10.91027 13.20830.44792 0.012532 35.74117 0.063939553 0.422965 0.472876 0.422965 0.47287677 496.8045 6.45200678 8738.797

It is needed to check whether the linear regression is significant.

Answered: Using the following regression table,…

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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rse ations Suppose a study was done to determine whether the average amount of sleep that students get the night before an exam is less than 6 hours. An SRS of 100 students from a university was taken, and a mean of 5.5 hours was computed from the sample. The figure below provides the results of the analysis. Normal calculator Density 04 0.3 02 0.1 0 -3 -2 Mean: 0 Std. Dev. 1 Prob(X<= -2.5 )=0.0062096654 Snapshot Close Compute What is your conclusion based on the results of this study? (Assume a significance level of 0.05.) O On average, students typically get less than 6 hours of sleep the night before an exam. O On average, students typically get 6 hours of sleep the night before an exam. O On average, students typically get more than 6 hours of sleep the night before an exam. O On average, students typically get 5.5 hours of sleep the night before an exam.
Which is not one of the major assumptions typically associated with parametric tests of significance? A. Random Selection B. Homogeneity of Variance C. Absence of restricted ranges
Some entries have been deleted from the following analysis of variance table that is part of the computer output from a simple regression analysis of a data set containing 11 matched-pair observations: Source Regression Residual Total df 9907.49 1100.83 9315.23 33.18 8.4619 1 11 SS 9315.23 19222.72 MS 9315.23 F p value 0.01734 Based on this output, what is the value of the standard error of estimate?
The proper estimate of variance explained in multiple regression is Question 13 options: R2 >50% significant predictors Adjusted R2 All of these
What percentage of the variability in the number of delivery days for a car is NOT explained by knowing the number of factory options? SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations Intercept Factory Options 22.31% 72.16% 88.14% 77.69% O 4.64% 0.8814 0.7769 0.7216 4.6400 10 Coefficients 29.73333 3.284848 Standard Error 2.991435587 0.482113498
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
The key assumptions of the ANOVA include homogeneity of variance, normality of population distributions and: Independence of observations The DV must be measured on a nominal level The IV must be measured on a continuous level Variability of residuals
The following chart Regression Statistics Multiple R 0.896207 R Square 0.803187 Adjusted R Square 0.775071 Standard Error 10.31462 Observations 9 ANOVA df SS MS F Significance F Regression 1 3039.26 3039.26 28.5668 0.001070199 Residual 7 744.7395 106.3914 Total 8 3784 Coefficients Standard Error t Stat P-value Lower 95% Intercept 92.26212 7.55045 12.21942 5.63E-06 74.40814014 X Variable 1 -2.36029 0.441605 -5.34479 0.00107 -3.404514314 shows a: very strong positive correlation very weak correlation very strong negative correlation we don't have the necessary information to evaluate the level of correlation

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