Figure 1 shows the results of the statistical test without interaction term between Region and Design (aov1), and the statistical test with interaction term betweenRegion and Design (aov2) as R output for this problem.Figure 1.R output of model results for two statistical models, one without an interaction term between Design and Region (aov1) and one with interaction term between Designand Region (aov2).aovi <- aov(Response-Design+Region, data=ads)summary(aov1)þesignRegionResidualsDf Sum Sq Mean sq F value2 2363373 9463830118169315461416Pr (>F)83.42 5.56e-13 ***22.27 8. 80e-08 ***42495signif. codes: 0 ****' 0.001 ***' 0.01 **' 0.05 '.' 0.1 ''1aov2 <- aov(Response-Design*Region, data=ads)summary(aov2)of Sum sq Mean sq F value2 236337 118169 82. 812 1.69e-11 ***Pr (>F)þesignRegionþesign:Region 6Residuals3 9463882483424731546 22.107 4. 34e-07 ***0. 963137514270.4724---signif. codes:O ***0.0010. 01 *0.05 .' 0.1'1Using a significant at the significance level alpha = 0.05, based on Figure 1 which statement is correct?%DThe model without interaction (aov1) shows that the factors Design and Region are not significant with regards to their impact on survey responses at the selectedsignificance level.The model with interaction (aov2) shows that interaction term between Design and Region is significant with regards to the impact on survey responses at theselected significance level.Using the model with interaction (aov2) results, we fail to reject the null hypothesis related to interaction between Design and Region at the selected significancelevel, i.e., we can't reject that the levels of Design are the same with regards to the impact of Region on Response, and we can't reject that levels of Region arethe same with regards to the impact of Design on Response.Using the model with interaction (aov2) results, we reject the null hypothesis related to interaction between Design and Region at the selected significance level,i.e., we reject that the levels of Design are the same with regards to the impact of Region on Response, and we reject that levels of Region are the same withregards to the impact of Design on Response.

Answered: Figure 1 shows the results of the…

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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A survey of high school students was done to examine whether students had ever driven a car after consuming a substantial amount of alcohol (1=yes, 0=no). Data was collected on their sex (male/female), race (White/non-White), and grade level (9,10,11,12). Researchers realized that the impact of race on consuming alcohol before driving might vary by grade level and decided to fit the following model. Variable Coding = 1 if Intercept Sex () Female Race () Black Grade level ( 9th grade 10th grade 11th grade [Reference = 12th grade] Attached is the logistic model 1. Compute the OR of drinking before driving for students who self-reported as Black versus non-Black in the 9th grade, adjusting for gender. 2. Compute the OR of drinking before driving for students who self-reported as Black versus non-Black in the 12th grade, adjusting for gender. 3. Compute the OR of drinking before driving for someone in the 9th grade versus 12th grade for a student who…
A study was conducted to assess the impact of nutrient enrichment on zooplankton densities in A & B Islands. An ecologist sampled populations of zooplankton in these two locations and observed the nutrient enrichment level was higher in A island when compared with the level in B island. It is predicted the zooplankton densities in A island will be greater than those found in B island.
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