Table Analyzed HVC VolumeTwo-way ANOVA OrdinaryAlpha0.05Source of Variation.AgerGender.Interaction 3.893.0.1020 0.8341 ns69.29 0.0001ANOVA tableSSDFInteraction 0.01103 110.00028900.1962 10.07566 12AgerGenderResidualof total variation P value P value summary Significant?0.2107 nsDifference between column meansMean of MaleMean of FemaleDifference betweenSE of difference.95% CI of difference.Interaction CIDifference between row means.Mean of 25 d0.1725Mean of 55 d0.1810Difference betweenSE of difference95% CI of differenceData summarymeans0.28750.06600means 0.22150.039700.03970Mean diff, A1 B1 0.16900.2740Mean diff, A2 B2(A1-B1) (A2-B2)95% CI of difference.(B1A1) (B2A2)95% CI of difference.MSNumber of columns (Gender)Number of rows (Ager)Number of values. 16F (DFn, DFd)0.01103 F (1, 12) 1.7490.00028900.1962 F (1, 12)- 31.130.0063051NoNoYes0.1350 to 0.3080-0.008500-0.09500 to 0.07800-0.1050-0.2780 to 0.068010.1050-0.06801 to 0.278022P valueP-0.2107F (1, 12) 0.04584P-0.0001P=0.8341

A two way analysis of variance (ANOVA) was conducted to investigate the effect of two categorical…

Answered: Table Analyzed HVC Volume Two-way ANOVA…

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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What is the alternative hypothesis for this research question? Group of answer choices There is no difference in mean job satisfaction across the three height groups. Mean job satisfaction will differ between at least two of the height groups. The mean job satisfaction for tall people is higher than the mean job satisfaction for short people. The mean of one group will differ from the average job satisfaction of the population.
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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The ANOVA table of the above data is as follows: ANOVA Source of Variation SS df MS 2.083 P-value Factor A J 1 0.725 Marketing startegies 0.750 1 0.750 0.832 Interaction 90.750 K 90.750 0.043 Within 125.333 15.667 Total 218.917 10. Calculate J and L 11. State HO and H1 hypotheses of interaction effect based on the above problem. 12. At a = 0.1, is there any significant effect? What is your decision? Support your answer. 13. What is your conclusion?
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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0.6.3 Question 3 Is the cubic effect significant? How about quadratic and linear effects? Analysis of Variance Table Response: EX Df Sum Sq Mean Sq F value Pr(>F) MET 1 332 332 0.1246 0.7257742 I(MET^2) 1 37504 37504 14.0572 0.0005141 *** I(MET^3) 1 7245 7245 2.7154 0.1065086 Residuals 44 117390 2668 > anova(quadModel) Analysis of Variance Table Response: EX Df Sum Sq Mean Sq F value Pr(>F) MET 1 332 332 0.120 0.7306134 I(MET^2) 1 37504 37504 13.541 0.0006216 *** Residuals 45 124635 2770 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
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
Smoke-Free Legislation and AsthmaHospital admissions for asthma in children younger than 15 years was studied1 in Scotland both before and after comprehensive smoke-free legislation was passed in March 2006. Monthly records were kept of the annualized percent change in asthma admissions, both before and after the legislation was passed. For the sample studied, before the legislation, admissions for asthma were increasing at a mean rate of 5.2% per year. The standard error for this estimate is 0.7% per year. After the legislation, admissions were decreasing at a mean rate of 18.2% per year, with a standard error for this mean of 1.79%. In both cases, the sample size is large enough to use a normal distribution.1Mackay, D., et. al., ‘‘Smoke-free Legislation and Hospitalizations for Childhood Asthma,” The New England Journal of Medicine, September 16, 2010; 363(12):1139-45. (a) Find a 95% confidence interval for the mean annual percent rate of change in childhood asthma hospital…
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