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Researchers Investigated the Effects of Acute Stress on Emotional Picture

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Researchers investigated the effects of acute stress on emotional picture processing and recall by randomly assigning 40 adult males to receive either a stressful cold stimulus or a neutral warm stimulus before viewing pictures. The researchers computed the relative brainwave amplitude (in microvolts, μV) for each subject viewing unpleasant versus neutral pictures. They also recorded how many pictures the subjects were able to recall 24 hours later. The data are displayed in the following scatterplot.
μ  Researchers investigated the effects of acute stress on emotional picture processing and recall by randomly assigning 40 adult males to receive either a stressful cold stimulus or a neutral warm stimulus before viewing pictures. The researchers computed the relative brainwave amplitude (in microvolts, μV)  for each subject viewing unpleasant versus neutral pictures. They also recorded how many pictures the subjects were able to recall 24 hours later. The data are displayed in the following scatterplot. μ   The following model is proposed for predicting the brainwave relative amplitude ( amplitude )  from the number of images recalled ( recall ) , the indicator variable reflecting the nature of the stimulus ( stress ) , and an interaction term ( recall*stress ) : Amplitude<sub>i</sub> = β<sub>0</sub> + β<sub>1</sub> (recall<sub>i</sub>)  + β<sub>2</sub> (stress<sub>i</sub>)  + β<sub>3</sub> (recall*stress<sub>i</sub>)  + ε<sub>i</sub> Where the deviations ε<sub>i</sub> are assumed to be independent and Normally distributed with mean 0 and standard deviation σ. This model was fit to the sample of 40 adult males. The following results summarize the least-squares regression fit of this model: ​  \begin{array}{lrr} \text { Predictor } & \text { Coef } & \text { SE Coef } \\ \text { Constant } & 4.795 & 1.329 \\ \text { recall } & -0.1139 & 0.1069 \\ \text { stress } & -4.262 & 2.205 \\ \text { recall }{ }^{*} \text { stress } & 0.4337 & 0.1677 \\ \mathrm{~S}=2.13946 & \text { R-Sq }=22.8 \% & \end{array}    \begin{array}{l} \text { Analysis of Variance }\\ \begin{array}{lrrr} \text { Source } & \text { DF } & \text { SS } & \text { MS } \\ \text { Regression } & 3 & 48.788 & 16.263 \\ \text { Residual Error } & 36 & 164.783 & 4.577 \\ \text { Total } & 39 & 213.571 & \end{array} \end{array}  Here are the residual plots for this regression model:   Based on the study description, the scatterplot, and the residual plots, what should you conclude? A) The assumption of Normality is not met but the other assumptions are met. B) The assumption of constant variance is not met but the other assumptions are met. C) The assumption of independence is not met but the other assumptions are met. D) All of the assumptions for regression inference are met. The following model is proposed for predicting the brainwave relative amplitude ("amplitude") from the number of images recalled ("recall") , the indicator variable reflecting the nature of the stimulus ("stress") , and an interaction term ("recall*stress") :
Amplitudei = β0 + β1 (recalli) + β2 (stressi) + β3 (recall*stressi) + εi
Where the deviations εi are assumed to be independent and Normally distributed with mean 0 and standard deviation σ. This model was fit to the sample of 40 adult males. The following results summarize the least-squares regression fit of this model:

 Predictor  Coef  SE Coef  Constant 4.7951.329 recall 0.11390.1069 stress 4.2622.205 recall  stress 0.43370.1677 S=2.13946 R-Sq =22.8%\begin{array}{lrr}\text { Predictor } & \text { Coef } & \text { SE Coef } \\\text { Constant } & 4.795 & 1.329 \\\text { recall } & -0.1139 & 0.1069 \\\text { stress } & -4.262 & 2.205 \\\text { recall }{ }^{*} \text { stress } & 0.4337 & 0.1677 \\\mathrm{~S}=2.13946 & \text { R-Sq }=22.8 \% &\end{array}

 Analysis of Variance  Source  DF  SS  MS  Regression 348.78816.263 Residual Error 36164.7834.577 Total 39213.571\begin{array}{l}\text { Analysis of Variance }\\\begin{array}{lrrr}\text { Source } & \text { DF } & \text { SS } & \text { MS } \\\text { Regression } & 3 & 48.788 & 16.263 \\\text { Residual Error } & 36 & 164.783 & 4.577 \\\text { Total } & 39 & 213.571 &\end{array}\end{array} Here are the residual plots for this regression model:
 Researchers investigated the effects of acute stress on emotional picture processing and recall by randomly assigning 40 adult males to receive either a stressful cold stimulus or a neutral warm stimulus before viewing pictures. The researchers computed the relative brainwave amplitude (in microvolts, μV)  for each subject viewing unpleasant versus neutral pictures. They also recorded how many pictures the subjects were able to recall 24 hours later. The data are displayed in the following scatterplot. μ   The following model is proposed for predicting the brainwave relative amplitude ( amplitude )  from the number of images recalled ( recall ) , the indicator variable reflecting the nature of the stimulus ( stress ) , and an interaction term ( recall*stress ) : Amplitude<sub>i</sub> = β<sub>0</sub> + β<sub>1</sub> (recall<sub>i</sub>)  + β<sub>2</sub> (stress<sub>i</sub>)  + β<sub>3</sub> (recall*stress<sub>i</sub>)  + ε<sub>i</sub> Where the deviations ε<sub>i</sub> are assumed to be independent and Normally distributed with mean 0 and standard deviation σ. This model was fit to the sample of 40 adult males. The following results summarize the least-squares regression fit of this model: ​  \begin{array}{lrr} \text { Predictor } & \text { Coef } & \text { SE Coef } \\ \text { Constant } & 4.795 & 1.329 \\ \text { recall } & -0.1139 & 0.1069 \\ \text { stress } & -4.262 & 2.205 \\ \text { recall }{ }^{*} \text { stress } & 0.4337 & 0.1677 \\ \mathrm{~S}=2.13946 & \text { R-Sq }=22.8 \% & \end{array}    \begin{array}{l} \text { Analysis of Variance }\\ \begin{array}{lrrr} \text { Source } & \text { DF } & \text { SS } & \text { MS } \\ \text { Regression } & 3 & 48.788 & 16.263 \\ \text { Residual Error } & 36 & 164.783 & 4.577 \\ \text { Total } & 39 & 213.571 & \end{array} \end{array}  Here are the residual plots for this regression model:   Based on the study description, the scatterplot, and the residual plots, what should you conclude? A) The assumption of Normality is not met but the other assumptions are met. B) The assumption of constant variance is not met but the other assumptions are met. C) The assumption of independence is not met but the other assumptions are met. D) All of the assumptions for regression inference are met. Based on the study description, the scatterplot, and the residual plots, what should you conclude?


A) The assumption of Normality is not met but the other assumptions are met.
B) The assumption of constant variance is not met but the other assumptions are met.
C) The assumption of independence is not met but the other assumptions are met.
D) All of the assumptions for regression inference are met.

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